Documentation of the Estimated, Dynamic, Optimization-based (EDO) Model of the U.S. Economy: 2010 Version

Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C. Documentation of the Estimated, Dynamic, Optimization-based (EDO) Model of the U.S. Economy: 2010 Version Hess T. Chung, Michael T. Kiley, and Jean-Philippe Laforte 2010-29 NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. References in publications to the Finance and Economics Discussion Series (other than acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.

Documentation of the Estimated, Dynamic, Optimization-based (EDO) Model of the U.S. Economy: 2010 Version Hess Chung, Michael T. Kiley, and Jean-Philippe Laforte ∗ March 25, 2010 Abstract This paper provides documentation for a large-scale estimated DSGE model of theU.S.economy–theFederalReserveBoard’sEstimated,Dynamic,Optimizationbased (FRB/EDO) model project. The model can be used to address a wide range of practical policy questions on a routine basis. The paper discusses the model’s specification, estimated parameters, and key properties. ∗ Hess Chung (hess.t.chung@frb.gov), Michael T. Kiley (michael.t.kiley@frb.gov), and Jean- Philippe Laforte (jean-philippe.laforte@frb.gov) are affiliated with the Division of Research and Statistics of the Federal Reserve Board. The views expressed in the paper are those of the authors alone and should not be attributed to the Board of Governors of the Federal Reserve System or any other members of its staff.

This paper contains documentation for the large-scale estimated DSGE model of the U.S. economy currently used at the Federal Reserve Board for some forecasting and policy projects – the Estimated, Dynamic, Optimization-based (EDO) model. Section 1 provides a brief qualitative description of the model and outlines its main features for production, capital evolution, and preference technologies. Section 2 definestheequilibriumofthemodel. Section3liststhedatathatisusedinestimating the model. Section 4 reports the model’s key estimation results, which include the estimatedparametervalues,variancedecompositions,impulseresponsefunctions,and estimated paths of the exogenous processes driving the model. The equations that characterize equilibrium in this model are contained in the appendix. Appendix A reports the equations of the symmetric and stationary model, and appendix B gives the solution to the model’s steady-state. Finally, since the model contains a large number of parameter and variable names a key is given in appendices C, D, and E. Before moving to our presentation of the model, we note that the EDO model serves as a complement to the analyses that are currently performed using existing large-scale econometric models, such as FRB/US model, as well as smaller, ad hoc models that we have found useful for more specific questions. In our experience, model-based analyses are enhanced by consideration of multiple models (and, indeed, our experience suggests that often we learn as much when models disagree than when they agree). A benefit of having multiple models is the opportunity to examine the robustness of policy strategies across models with quite different foundations, which we view as important given the significant divergences of opinion regarding the plausibility of various types of models. In addition, the EDO model is designed to allow the straightforward consideration of factors not explicitly modeled in the baseline version of the model. For example, the baseline version assumes that investment decisions – of households and firms – are made by capital intermediaries; simple modifications of this intermediation step allow explicit consideration of the financial accelerator or financial intermediation (e.g., a 1

banking sector), topics of ongoing research. These advantages of the model have been demonstrated in previous research on its forecast performance (Edge, Kiley, and Laforte (2010)), its application to policy questions such as the natural rate (Edge, Kiley, and Laforte (2008)), and the analysis of the cyclical state of the economy (Kiley (2010b)). 1 Model Overview and Motivation Figure 1 provides a graphical overview of the model. The model possesses two final good sectors in order to capture key long-run growth facts and to differentiate between the cyclical properties of different categories of durable expenditure (e.g., housing, consumer durables, and nonresidential investment). For example, technological progress has been faster in the production of business capital and consumer durables (such as computers and electronics). Edge, Kiley, and Laforte (2008 and 2010) discuss this motivation in greater detail. The first sector is the slow-growing sector—called “CBI” because most of these goods are used for consumption (C) and because they are produced by the business and institutions (BI) sector—and the second is the fast-growing sector—called “KB” because these goods are used for capital (K) accumulation and are produced by the business (B) sector. The goods are produced in two stages by intermediate- and then final-goods producingfirms(showninthecenterofthefigure). Asinmostnew-Keynesianmodels, the introduction of intermediate and final goods producers facilitates the specification of nominal rigidities. The disaggregation of production (aggregate supply) leads naturally to some disaggregation of expenditures (aggregate demand). We move beyond the typical model with just two categories of (private domestic) demand (consumption and investment) and distinguish between four categories of private demand: consumer non-durable goods and non-housing services, consumer durable goods, residential investment, and 2

non-residential investment. The boxes surrounding the producers in the figure illustrate how we structure the sources of each demand category. Consumer non-durable goods and services are sold directly to households; consumer durable goods, residential capital goods, and non-residential capital goods are intermediated through capital-goods intermediaries (owned by the households), who then rent these capital stocks to households. Consumer non-durable goods and services and residential capital goods are purchased (by households and residential capital goods owners, respectively) from the first of economy’s two final goods producing sectors, while consumer durable goods and non-residential capital goods are purchased (by consumer durable and residential capital goods owners, respectively) from the second sector. In addition to consuming the non-durable goods and services that they purchase, households supply labor to the intermediate goods-producing firms in both sectors of the economy. This remainder of this section provides an overview of the decisions made by each of the agents in our economy. Given some of the broad similarities between our model and others, our presentation is selective. 1.1 The Final Goods Producers’ Problem The economy produces two final goods and services: slow-growing “consumption” goods and services, Xcbi, and fast-growing “capital” goods, Xkb. These final goods t t are produced by aggregating (according to a Dixit-Stiglitz technology) an infinite number of sector-specific differentiated intermediate inputs, Xs(j) for s = cbi,kb, t distributed over the unit interval. The representative firm in each of the consumption and capital goods producing sectors chooses the optimal level of each intermediate input, taking as given the prices for each of the differentiated intermediate inputs, 3

Ps(j), to solve the cost-minimization problem: t Θs t min 1 P t s(j)X t s(j)dj subject to 1 (X t s(j)) Θ Θ s t − s t 1 dj Θs t −1 ≥ X t s, fors = cbi,kb. {X t s(j)}1 j=0Z0 (cid:18)Z0 (cid:19) (1) The term Θs is the stochastic elasticity of substitution between the differentiated t intermediate goods inputs used in the production of the consumption or capital goods sectors. Letting θs ≡ lnΘs−lnΘs denote the log-deviation of Θs from its steady-state t t ∗ t value of Θs, we assume that ∗ θs = ǫ θ,s , for s = cbi,kb, (2) t t θ,s where ǫ is a shock process. A stochastic elasticity of substitution introduces trant sitory markup shocks into the pricing decisions of intermediate-goods producers. 1.2 The Intermediate Goods Producers’ Problem The intermediate goods entering each final goods technology are produced by aggregating (according to a Dixit-Stiglitz technology) an infinite number of differentiated labor inputs, Ls(j) for s = cbi,kb, distributed over the unit interval and combining t this aggregate labor input (via a Cobb-Douglas production function) with utilized u,nr,s non-residential capital, K . Each intermediate-good producing firm effectively t solves three problems: two factor-input cost-minimization problems (over differentiated labor inputs and the aggregate labor and capital) and one price-setting profitmaximization problem. In its first cost-minimization problem, an intermediate goods producing firm chooses the optimal level of each type of differential labor input, taking as given the wages for each of the differentiated types of labor, Ws(i), to solve: t Θl t 1 1 Θl t −1 Θl t −1 min W t s(i)Ls t (i,j)di subject to (Ls t (i,j)) Θl t di ≥ Ls t (j), fors = cbi,kb. {Ls t (i,j)}1 i=0Z0 Z0 ! (3) 4

ThetermΘl isthestochasticelasticityofsubstitutionbetweenthedifferentiatedlabor t inputs. Letting θl ≡ lnΘl−lnΘl denote the log-deviation of Θl from its steady-state t t ∗ t value of Θl, we assume that ∗ θl = ǫ θ,l . (4) t t θ,l where ǫ is a shock process. A stochastic elasticity of substitution introduces trant sitory wage markup shocks into the wage decisions of households. In its second cost-minimization problem, an intermediate-goods producing firm chooses the optimal levels of aggregated labor input and utilized capital, taking as given the wage, Ws, for aggregated labor, Ls (which is generated by the cost function t t nr,s u,nr,s derived the previous problem), and the rental rate, R , on utilized capital, K , t t to solve: min WsLs(j)+R nr,s K u,nr,s (j) t t t t {Ls(j),Ku,nr,s(j)} t t subject to (ZmZsLs(j))1−α (K u,nr,s (j)) α ≥Xs(j), for s = cbi,kb, with Zcbi ≡ 1. (5) t t t t t t The parameter α is the elasticity of output with respect to capital, while the Z varit ables denote the level of productivity. The level of productivity has two components. The first, Zm, is common to both sectors and thus represents the level of economyt wide technology. The second, Zs, is sector specific; we normalize Zcbi to one, while t t Zkb is not restricted. t The exogenous productivity terms contain a unit root, that is, they exhibit permanent movements in their levels. We assume that the stochastic processes Zm and t Zkb evolve according to t lnZn −lnZn = lnΓ z,n = ln(Γz,n ·exp[ǫ z,n ]) = lnΓz,n +ǫ z,n , n = kb,m (6) t t−1 t ∗ t ∗ t where Γz,n and ǫ z,n are the steady-state and stochastic components of Γ z,n . The ∗ t t z,n stochastic component ǫ is an i.i.d shock process. t The unit-root in technology in both sectors yields a non-trivial Beveridge-Nelson permanent/transitory decomposition. The presence of capital-specific technological 5

progress allows the model to generate differential trend growth rates in the economy’s two production sectors. In line with historical experience, we assume a more rapid rate of technological progress in capital goods production by calibrating Γz,kb > 1, ∗ where (as is the case for all model variables) an asterisk on a variable denotes its steady-state value. In its price-setting (or profit-maximization) problem, an intermediate goods producing firm chooses its optimal nominal price and the quantity it will supply consistent with that price. In doing so it takes as given the marginal cost, MCs(j), t of producing a unit of output, Xs(j), the aggregate price level for its sector, Ps, t t and households’ valuation of a unit of nominal profits income in each period, which is given by Λcnn/Pcbi where Λcnn denotes the marginal utility of non-durables and t t t non-housing services consumption. Specifically, firms solve: ∞ Λcnn max E βt t {Ps(j)Xs(j)−MCs(j)Xs(j) {P t s(j),X t s(j)}∞ t=0 0 t=0 P t cbi t t t t X 100·χp Ps(j) 2 − t −ηpΠ p,s −(1−ηp)Πp,s PsXs 2 Ps (j) t−1 ∗ t t (cid:18) t−1 (cid:19) ) subject to Xs(j)=(Ps(j)/Ps)−Θs τXs for τ = 0,1,...,∞ and s = cbi,kb. (7) τ τ τ τ The profit function reflects price-setting adjustment costs (the size which depend on the parameter χp and the lagged and steady-state inflation rate). The constraint against which the firm maximizes its profits is the demand curve it faces for its differentiated good, which derives from the final goods producing firm’s cost-minimization problem. This type of price-setting decision delivers a new-Keynesian Phillips curve. Because adjustment costs potentially depend upon lagged inflation, the Phillips curve can take the “hybrid” form in which inflation is linked to its own lead and lag as well as marginal cost. 6

1.3 The Capital Owners’ Problem Wenowshiftfromproducers’decisionstospendingdecisions. Thereexistsaunitmass of non-residential capital owners (individually denoted by k, with k distributed over the unit interval) who choose investment in non-residential capital, Enr, the stock of t non-residential capital, Knr (which is linked to the investment decision via the capital t accumulation identity), and the amount and utilization of non-residential capital in each production sector, K nr,cbi , Ucbi, K nr,kb , and Ukb. (Recall, that the firm’s choice t t t t variables in equation 5 is utilized capital K u,nr,s = UsK nr,s .) The mathematical t t t representation of this decision is described by the following maximization problem (in which capital owners take as given the rental rate on non-residential capital, Rnr, t the price of non-residential capital goods, Pkb, and households’ valuation of nominal t capital income in each period, Λcnn/Pcbi, and the exogenous risk premium specific to t t non-residential investment, Anr): τ max {Enr(k),Knr (k),Knr,cbi(k),Knr,kb(k)Ucbi(k),Ukb(k)}∞ t t+1 t t t t t=0 ∞ Λcnn E βt t RnrUcbi(k)K nr,cbi (k)+RnrUkb(k)K nr,kb (k)−PkbEnr(k) 0 AnrPcbi t t t t t t t t X t=0 τ t n Ucbi(k)1+ψ −1 Ukb(k)1+ψ −1 −κ t QnrK nr,cbi −κ t QnrK nr,kb 1+ψ t t 1+ψ t t (cid:18) (cid:19) (cid:18) (cid:19) (cid:27) subject to 2 100·χnr Enr(k)−Enr (k)Γ x,kb Knr (k)=(1−δnr)Knr(k)+Enr(k)− τ τ−1 t Knr and τ+1 τ τ 2 Knr τ τ ! Knr,cbi(k)+Knr,kb(k)=Knr(k) for τ = 0,1,...,∞. (8) τ τ τ The parameter δnr in the capital-accumulation constraint denotes the depreciation rate for non-residential capital, while the parameter χnr governs how quickly investment adjustment costs increase when (Enr(k)−Enr (k)Γ x,kb ) rises above zero; note τ τ−1 t that these adjustment costs include a term for the stochastic growth rate of the trend in the level of the output in sector KB, Γ x,kb equal to Γ z,m Γ z,kb . The variable Anr is t t t t a stochastic element reflecting a risk premium on non-residential investment. Letting 7

anr ≡ lnAnr denote the log-deviation of Anr from its steady-state value of unity, we t t t assume that: anr = ρnranr +ǫ a,nr . (9) t t−1 t Higher rates of utilization incur a cost (reflected in the last two terms in the capital owner’s profit function). We assume that utilization is unity in the steady-state, implying κ = Rnr/Qnr. ∗ ∗ The time-variation in utilization, along with the imperfect competition in product and labor markets, implies that direct measurement of total factor productivity may not provide an accurate estimate of technology; as a result, the EDO model can deliver smoother estimates of technology that might be implied by a real-businesscycle model. The problems solved by the consumer durables and residential capital owners are slightly simpler than the non-residential capital owner’s problems. Since utilization ratesarenotvariableforthesetypesofcapital, theirownersmakeonlyinvestmentand capital accumulation decisions. Taking as given the rental rate on consumer durables capital, Rcd, the price of consumer-durable goods, Pkb, and households’ valuation of t t nominalcapitalincome,Λcnn/Pcbi,andtheexogenousriskpremiaspecifictoconsumer t t durables investment, Acd, the capital owner chooses investment in consumer durables, τ Icd, and its implied capital stock, Kcd, to solve: t t ∞ Λcnn max E βt t RcdKcd(k)−PkbEcd(k) {E t cd(k),K t c + d 1 (k)}∞ t=0 } 0 t=0 Ac τ dP t cbi t t t t X (cid:8) (cid:9) subject to 100·χcd Ecd(k)−Ecd (k)Γx,kb 2 Kcd (k)=(1−δcd)Kcd(k)+Ecd(k)− τ τ−1 τ Kcd τ+1 τ τ 2 Kcd τ (cid:18) τ (cid:19) for τ = 0,1,...,∞. (10) 8

The residential capital owner’s decision is analogous: ∞ Λcnn max E βt t RrKr(k)−PcbiEr(k) {E t r(k),K t r +1 (k)}∞ t=0 } 0 t=0 Ar τ P t cbi t t t t X (cid:8) (cid:9) subject to 100·χr Er(k)−Er (k)Γx,cbi 2 Kr (k)=(1−δr)Kr(k)+Er(k)− τ τ−1 τ Kcd τ+1 τ τ 2 Kcd τ (cid:18) τ (cid:19) for τ = 0,1,...,∞. (11) The notation for the consumer durables and residential capital stock problems parallels that of non-residential capital. In particular, the asset-specific risk premia shocks, Acd and Ar, follow an autoregressive process similar to that given in equation (9). t t 1.4 The Households’ Problem The final group of private agents in the model are households who make both expenditure and labor-supply decisions. Households derive utility from four sources: their purchases of the consumer non-durable goods and non-housing services, the flow of services from their rental of consumer-durable capital, the flow of services from their rental of residential capital, and their leisure time, which is equal to what remains of their time endowment after labor is supplied to the market. Preferences are separable over all arguments of the utility function. The utility that households derive from the three components of goods and services consumption is influenced by the habit stock for each of these consumption components, a feature that has been shown to be important for consumption dynamics in similar models. A household’s habit stock for its consumption of non-durable goods and non-housing services is equal to a factor h multiplied by its consumption last period Ecnn. Its habit stock for the other t−1 components of consumption is defined similarly. Each household chooses itspurchasesofconsumernon-durablegoods and services, Ecnn, the quantities of residential and consumer durable capital it wishes to rent, Kr t t and Kcd, its holdings of bonds, B , its wage for each sector, Wcbi and Wkb, and the t t t t supply of labor consistent with each wage, Lcbi and Lkb. This decision is made subject t t 9

to the household’s budget constraint, which reflects the costs of adjusting wages and the mix of labor supplied to each sector, as well as the demand curve the household faces for its differentiated labor. Specifically, the ith household solves: max ∞ {E t cnn(i),K t cd(i),K t r(i),{W t s(i),Ls t (i)}s=cbi,kb,Bt+1(i)} t=0 ∞ E βt ςcnnln(Ecnn(i)−hEcnn(i))+ςcdln(Kcd(i)−hKcd (i)) 0 t t−1 t t−1 t=0 X (cid:8) (Lcbi(i)+Lkb(i))1+ν +ςrln(Kr(i)−hKr (i)) −ςl t t . t t−1 1+ν (cid:27) subject to B (i) τ+1 =B (i)+ Ws(i)Ls(i)+CapitalandProfitsIncome (i)−PcbiEcnn(i) R Ω τ τ τ τ τ τ τ τ s=cbi,kb X 100·χw Ws(j) 2 −RcdKcd(i)−RrKr(i)− τ −ηwΠ w,s −(1−ηw)Πw WsLs τ τ τ τ 2 Ws (j) τ−1 ∗ τ τ s=cbi,kb (cid:18) τ−1 (cid:19) X 100·χl Lcbi ·Wcbi Lkb ·Wkb Lcbi(i) Lcbi 2 Lkb − ∗ τ + ∗ τ τ − τ−1 τ . 2 Lcbi +Lkb Lcbi +Lkb Lkb(i) Lkb Lcbi (cid:18) ∗ ∗ ∗ ∗ (cid:19)(cid:18) τ τ−1(cid:19) τ Lcbi(i)= Wcbi(i)/Wcbi −Θl tLcbi, and Lkb(i)= Wkb(i)/Wkb −Θl tLkb, τ τ τ τ τ τ τ τ for τ = 0(cid:0),1,...,∞. (cid:1) (cid:0) (cid:1) (12) In the utility function the parameter β is the household’s discount factor, ν denotes its inverse labor supply elasticity, while ςcnn, ςcd, ςr, and ςl are scale parameter that tie down the ratios between the household’s consumption components. The stationary, unit-mean, stochastic variable Ω represents an aggregate riskt premium shock that drives a wedge between the policy short-term interest rate and the return to bonds received by a household. Letting ω ≡ lnΩ −lnΩ denote the t t ∗ log-deviation of Ω from its steady-state value of Ω , we assume that t ∗ ω = ρωω +ǫω. (13) t t−1 t The variable ǫω is a shock process, and ρω represents the persistence of Ω . t t The household’s budget constraint reflects wage setting adjustment costs, which depend on the parameter χw and the lagged and steady-state wage inflation rate, and 10

the costs in changing the mix of labor supplied to each sector, which depend on the parameter χl. The costs incurred by households when the mix of labor input across sectors changes may be important for sectoral comovements. 1.5 Gross Domestic Product The demand and production aspects of the model are closed through the exogenous process for demand other than private domestic demand and the GDP identity. XHG t represents exogenous demand (i.e., GDP other than private domestic demand, the e aggregate of Ecnn, Ecd, Er, and Enr). Exogenous demand is assumed to follow the t t t t process: lnXHG −lnXHG = ρHG lnXHG −lnXHG +ǫHG. t ∗ t ∗ t (cid:16) (cid:17) We assume that thee exogenoues demand impineges on eaech sector symmetrically, and specifically that the percent deviation of exogenous demand proportionally affects demand for each sector’s (s = cbi,kb) output via the share of exogenous demand in total demand, ω . (In this formulation, XHG represents the level of expenditure HG t relative to the stochastic long-run trend, i.e., the model assumes balanced growth, so e exogenous demand for each sector fluctuates around its long-run trend; for example, the long-run trend for sector KB is given by ZmZkb). t t The rate of change of Gross Domestic Product (real GDP) equals the Divisia (share-weighted) aggregate of production in the two sectors (and of final spending across each expenditures category), as given by the identity: 1 Xcbi P∗ cbiX∗ cbi Xkb P∗ kbX∗ kb P∗cbiX∗cbi+P∗kbX∗kb H gdp = t t . (14) t Xcbi Xkb (cid:18) t−1(cid:19) (cid:18) t−1(cid:19) ! 1.6 Monetary Authority We now turn to the last important agent in our model, the monetary authority. It sets monetary policy in accordance with an Taylor-type interest-rate feedback rule. 11

¯ Policymakers smoothly adjust the actual interest rate R to its target level R t t R = (R ) φr R ¯ 1−φr exp[ǫr], (15) t t−1 t t (cid:0) (cid:1) where the parameter φr reflects the degree of interest rate smoothing, while ǫr rept ¯ resents a monetary policy shock. The central bank’s target nominal interest rate, R t depends the deviation of output from its stochastic trend (X ˜bn, the output gap as defined by Beveridge and Nelson (1981)) t ∞ X ˜bn = E Hgdp − Hgdp . (16) t t τ τ " # τ=−∞ τ=−∞ X X In equation 16, the deterministic, or steady-state, levels of growth are suppressed. Consumer price inflation and the change in the output gap also enter the target. The target equation is: R ¯ = X ˜ bn φy X ˜ bn /X ˜bn φ∆y Πc t φπ R . (17) t t t t−1 Πc ∗ (cid:18) ∗(cid:19) (cid:16) (cid:17) (cid:16) (cid:17) In equation (17), R denotes the economy’s steady-state nominal interest rate and ∗ φy, φ∆y, and φπ denote the weights in the feedback rule. Consumer price inflation, Πc, is the weighted average of inflation in the nominal prices of the goods produced t p,cbi p,kb in each sector, Π and Π : t t Πc = (Π p,cbi )1−wcd(Π p,kb )wcd. (18) t t t The parameter wcd is the share of the durable goods in nominal consumption expenditures. 1.7 Summary of Model Specification Our brief presentation of the model highlights several important points. First, although our model considers production and expenditure decisions in a bit more detail, it shares many similar features with other DSGE models in the literature, such as imperfect competition, nominal price and wage rigidities, and real frictions like 12

adjustment costs and habit-persistence. The rich specification of structural shocks (to aggregate and investment-specific productivity, aggregate and sector-specific risk premiums, and mark-ups) and adjustment costs allows our model to be brought to the data with some chance of finding empirical validation. Within EDO, fluctuations in all economic variables are driven by eleven structural shocks. It is most convenient to summarize these shocks into four broad categories: • Permanent technology shocks: This category consists of shocks to aggregate and investment-specific (or fast-growing sector) technology. • Financial, or intertemporal, shocks: This category consists of shocks to risk premia. In EDO, variation in risk premia – both the premium households’ receive relative to the federal funds rate on nominal bond holdings and the additional variation in discount rates applied to the investment decisions of capital intermediaries – are purely exogenous. Nonetheless, the specification capturesimportantaspectsofrelatedmodelswithmoreexplicitfinancialsectors (e.g., Bernanke, Gertler, and Gilchrist (1999)), as we discuss in our presentation of the model’s properties below. • Markup shocks: This category includes the price and wage markup shocks. • Other demand shocks: This category includes the shock to autonomous demand and a monetary policy shock. 1.8 Market Clearing There are a number of market clearing conditions that must be satisfied in our model. Market clearing in the slow-growing “consumption” goods and fast-growing “capital” goods sectors, given price- and wage-adjustment costs and variable utilization costs, 13

implies that 1 1 Xcbi= Ecnn(i)di+ Er(k)dk +XHG ∗Zm ∗ Zkb α t t t t t t Z0 Z0 100·χp 2(cid:0) (cid:1) + Π p,kb −ηpΠ p,cbi −(1e−ηp)Πp,cbi PcbiXcbi 2 t t−1 ∗ t t 100·χw (cid:16) (cid:17) 2 Ucbi(k)1+ψ−1 + Π w,cbi −ηwΠ w,cbi −(1−ηw)Πw,cbi WcbiLcbi−κ t PcbiK nr,cbi 2 t t−1 ∗ t t 1+ψ t t (cid:18) (cid:19) (cid:16) (cid:17) (19) and 1 1 Xkb= Ecd(k)dk + Enr(k)dk +XHG ∗Zm ∗Zkb t t t t t t Z0 Z0 100·χp 2 ++ Π p,kb −ηpΠ p,kb −(1e−ηp)Πp,kb PkbXkb 2 t t−1 ∗ t t 100·χw (cid:16) (cid:17) 2 Ukb(k)1+ψ−1 + Π w,kb −ηwΠ w,kb −(1−ηw)Πw,kb WkbLkb−κ t PkbK n . r,kb (20) 2 t t−1 ∗ t t 1+ψ t t (cid:18) (cid:19) (cid:16) (cid:17) The market clearing conditions for the labor and non-residential capital supplied and demanded in sector s are given by 1 1 1 Ls(i) = Ls(i,j)dj and U(k)sK nr,s (k)dk = K u,nr,s (j)dj ∀i ∈ [0,1]and fors = cbi,kb. t t t t t Z0 Z0 Z0 (21) The market clearing conditions for consumer durables and residential capital are 1 1 1 1 Kcd(k)dk = Kcd(i)di and Kr(k)dk = Kr(i)di. (22) t t t t Z0 Z0 Z0 Z0 1.9 Inflation The identities for inflation include: Ws(i) = Π w,s (i)Ws (i) and Ws = Π w,s Ws ∀ i ∈ [0,1] and for s = cbi,kb, and(23) t t t−1 t t t−1 Ps(j) = Π p,s (j)Ps (j) and Ps = Π p,s Ps ∀ j ∈ [0,1] and for s = cbi,kb. (24) t t t−1 t t t−1 2 Equilibrium Before characterizing equilibrium in this model, we define three additional variables: The price of installed non-residential capital Qnr(k); the price of installed consumer t 14

durables capital Qcd(k); and the price of installed residential capital Qr(k). These t t variables are the lagrange multiplier on the capital evolution equations that would be implied by the kth capital owner’s profit-maximization problems. Equilibrium in our model is an allocation: H gdp ,Xcbi,{Xcbi(j)}1 ,Xkb,{Xkb(j)}1 ,{Enr(k)}1 ,{Er(k)}1 ,{Ecd(k)}1 , t t t j=0 t t j=0 t k=0 t k=0 t k=0 n {Ecnn(i)}1 ,{Λr(i)}1 ,{Λcd(i)}1 ,{Λcnn(i)}1 ,{Λ l,cbi (i)}1 ,{Λ l,kb (i)}1 , t i=0 t i=0 t i=0 t i=0 t i=0 t i=0 {Lcbi(i)}1 ,{{Lcbi(i,j)}1 }1 ,{Lkb(i)}1 ,{{Lkb(i,j)}1 }1 ,{Ucbi(j)}1 ,{Ukb(j)}1 , t i=0 t i=0 j=0 t i=0 t i=0 j=0 t j=0 t j=0 {K u,nr,cbi (j)}1 ,{K u,nr,kb (j)}1 ,{K nr,cbi (k)}1 ,{K nr,kb (k)}1 ,{Knr (k)}1 , t j=0 t j=0 t k=0 t k=0 t+1 k=0 {Kr (k)}1 ,{Kr (i)}1 ,{Kcd (k)}1 ,{Kcd (i)}1 ∞ t+1 k=0 t+1 i=0 t+1 k=0 t+1 i=0 t=0 (cid:9) and a sequence of values Πc,Π p,cbi ,{Π p,cbi (j)}1 ,Π p,kb ,{Π p,kb (j)}1 ,Π w,cbi ,{Π w,cbi (i)}1 ,Π w,kb ,{Π w,kb (i)}1 , t t t j=0 t t j=0 t t j=0 t t j=0 n Pkb Pkb(j) 1 Wcbi Wcbi(i) 1 Wkb Wkb(i) 1 R nr,cbi R nr,kb Rnr t , t , t , t , t , t , t , t , t , Pcbi Pcbi Pcbi Pcbi Pcbi Pcbi Pcbi Pcbi Pcbi t (cid:26) t (cid:27)j=0 t (cid:26) t (cid:27)i=0 t (cid:26) t (cid:27)i=0 t t t Rr Rcd MCcbi(j) 1 MCkb(j) 1 Qnr(k) 1 Qr(k) 1 Qcd(k) 1 ∞ t , t , t , t , t , t , t ,R Pcbi Pcbi Pcbi Pcbi Pcbi Pcbi Pcbi t t t (cid:26) t (cid:27)j=0 (cid:26) t (cid:27)j=0 (cid:26) t (cid:27)k=0 (cid:26) t (cid:27)k=0 (cid:26) t (cid:27)k=0 ) t=0 that satisfy the following conditions: • The model’s two representative final-good producing firms solve (1) for s = cbi and kb; • All intermediate-good producers j ∈ [0,1] solve (3), (5), and (7) for s = cbi and kb; • All capital owners k ∈ [0,1] solve (8), (10), and (11); • All households i ∈ [0,1] solve (12); • The two final goods markets clear as in (19) and (20); • All intermediate goods markets clear; • The labor and non-residential capital markets clear as in (21); 15

• The consumer durable and residential capital rental markets clear as in (22); • The identities given in (23) hold; • The identities given in (24) hold; • The monetary authority follows (15) and (17). In solving these problems agents take as given the initial values of all (lagged) endogenous state variables (e.g., capital stocks, etc.) and the sequence of exogenous variables ∞ Γ z,kb ,Γ z,m ,Θcbi,Θkb,Θl,Anr,Ar,Acd,Ω,XHG t t t t t t t t t t t=0 n o implied by the sequence of shocks ∞ ǫ z,kb ,ǫ z,m ,ǫ θ,cbi ,ǫ θ,kb ,ǫ θ,l ,ǫ a,nr ,ǫ a,r ,ǫ a,cd ,ǫω,ǫHG,ǫr . t t t t t t t t t t t t=0 n o We estimate the log-linearized, symmetric and stationary version of the model described above. Equilibrium in the symmetric and stationary version of the model is defined in appendix A. The log-linearization of our model equations is performed symbolically by the software that we use to parse the model into its estimable form. The steady-state solution to the symmetric and stationary version of the model is an input into the model’s estimation and is presented in appendix B. 3 Estimation 3.1 Data The empirical implementation of the model takes a log-linear approximation to the first-order conditions and constraints that describe the economy’s equilibrium, casts this resulting system in its state-space representation for the set of (in our case 11) observable variables, uses the Kalman filter to evaluate the likelihood of the observed variables, and forms the posterior distribution of the parameters of interest by combining the likelihood function with a joint density characterizing some prior beliefs. 16

Since we do not have a closed-form solution of the posterior, we rely on Markov-Chain Monte Carlo (MCMC) methods. The model is estimated using 11 data series over the sample period from 1984:Q4 to 2008:Q4. The series are: 1. The growth rate of real gross domestic product; 2. The growth rate of real consumption expenditure on non-durables and services excluding housing services; 3. The growth rate of real consumption expenditure on durables; 4. The growth rate of real residential investment expenditure; 5. The growth rate of real business investment expenditure; 6. Consumer price inflation, as measured by the growth rate of the Personal Consumption Expenditure (PCE) price index; 7. Consumer price inflation, as measured by the growth rate of the PCE price index excluding food and energy prices; 8. Inflation for consumer durable goods, as measured by the growth rate of the PCE price index for durable goods; 9. Hours, which equals hours of all persons in the non-farm business sector from the Bureau of Labor Statistics;1 10. The growth rate of real wages, as given by compensation per hour in the nonfarm business sector from the Bureau of Labor Statistics divided by the GDP price index; 11. The federal funds rate. Our implementation adds measurement error processes to the likelihood implied by the model for all of the observed series used in estimation except the nominal interest rate series. 1We remove a low-frequency trend from hours via the Hodrick-Prescott filter with a smoothing parameterof64000; ourmodelisnotdesignedtocapturelowfrequencytrendsinpopulationgrowth or labor force participation. 17

3.2 Model Parameters The model’ calibrated parameters are presented in Table 2, while the estimated parameters are presented in Tables 3 and 4. We based out decision on several considerations. First, some important determinants of steady-state behavior were calibrated to yields growth rates of GDP and associated price indexes that corresponded to “conventional” wisdom in policy circles, even though slight deviations from such values would have been preferred (in a “statistically significant” way) to our calibrated values. In other cases, parameters were calibrated based on how informative the data were likely to be on the parameter and/or identification and overparameterization issues. Finally, the standard deviations of the measurement error assumed in the observables was chosen to ensure a moderate contribution of such errors to the variability in the data (according to our model) while also preserving desirable forecast properties; we present the observables and the role of measurment error in the results below. The first three columns of Table 3 and 4 outline our assumptions about the prior distributions of the estimated parameters, the remaining columns describe the parameters’ posterior distributions, which we now proceed to discuss. We consider first the parameters related to household and business spending decisions. The habit-persistence parameter is moderate, near 0.6.2 Investment adjustment costs are large for residential investment but small for business investment. This finding highlights once advantage of our disaggregated approach. In addition, this result is importantly driven by the inclusion of inventory investment in business investment; this is a very cyclically important component of GDP and was an important element in early investigations of dynamic general equilbrium models (e.g., Kydland and Prescott (1982)), but is typically ignored in similar DSGE models. The estimated value of the inverse of the labor supply elasticity implies quite 2SeeKiley(2010a)foradiscussionofissuesrelatedtoidentificationofthehabitparameterusing frequentist techniques. 18

elastic labor supply. We also find a role for the sectoral adjustment costs to labor: In our multisector setup, shocks to productivity or preferences in one sector of the economy result in strong shifts of labor towards that sector, which conflicts with the high degree of sectoral co-movement in the data. Finally, adjustment costs to prices and wages are both estimated to be important. Our estimate of the price adjustment cost is equivalent to a Calvo pricing setting where a bit more than half of the firms cannot update their prices each period. The estimated quadratic costs in wages imply a slightly larger frequency of adjustments for the suppliers of labor. We also find only a modest role for lagged inflation in our adjustment cost specification (around 1/4), equivalent to modest indexation to lagged inflation in other sticky-price specifications. This differs from some other estimates, perhaps because of the focus on a more recent post-1983 sample (similar to results in Kiley (2007) and Laforte (2007)). 3.3 Variance Decompositions Tables 5 and 6 present forecast error variance decompositions at various (quarterly) horizons at the posterior mode of the parameter estimates for key variables and shocks. We run through the key results here. Volatility in aggregate GDP growthisaccountedforprimarilybythetechnology shocks in each sector, although the economy-wide risk premium shock contributes non-negligibly to the unconditional variance of GDP growth. Volatility in hours per capita is accounted for primarily by the economy-wide risk premium and business investment risk premium shocks at horizons between one and sixteen quarters. Technology shocks in each sector contribute appreciably to the unconditional variance. The large role for risk premia shocks in the forecast error decomposition at business cycle horizons illustrates the importance of this type of “demand” shock for volatility in the labor market. This result is notable, as hours per capita is the series most like a “gap” variable in the model – that is, house per 19

capita shows persistent cyclical fluctuations about its trend value. Volatility in core inflation is accounted for primarily by the markup shocks in the short run and technology shocks in the long run. Volatility in the federal funds rate is accounted for primarily by the economywide risk premium. Volatility in expenditures on consumer non-durables and non-housing services is, in the near horizon, accounted for predominantly by economy-wide and non-residential investment specific risk-premia shocks. In the far horizon, volatility is accounted for primarily by capital-specific and economy-wide technology shocks. Volatilities in expenditures on consumer durables, residential investment, and non-residential investment are, in the near horizon, accounted for predominantly by their own sector specific risk-premium shocks. At farther horizons, their volatilities are accounted for by capital-specific technology shocks. 3.4 Impulse Responses We now turn to the impulse responses of some of the key observable variables to the exogenous shocks that drive fluctuations in the model. In each case we consider unit shocks; the reader is referred to the reported estimates of the standard deviation of the shocks for information that will scale these responses to units consistent with a standard deviation shock. Expenditure variables are reported as percent deviations from initial values (in natural log points); inflation variables and the federal funds rate are reported at quarterly (not annual) rates. The impulse responses to a monetary policy innovation (shown in figure 2) capturestheconventionalwisdomregardingtheeffectsofsuchshocks. Inparticular, both household and business expenditures on durables (consumer durables, residential investment, and nonresidential investment) respond strongly (and with a hump-shape) to a contractionary policy shock, with more muted responses by nondurables and services consumption; each measure of inflation responds gradually, albeit more quickly 20

than in some analyses based on vector autoregressions (VARs). (This difference between VAR-based and DSGE-model based impulse responses has been highlighted elsewhere – for example, in the survey of Boivin, Kiley, and Mishkin (2010)). Figures 3 to 12 present the impulse responses of key variables to the model’s four risk premia shocks (Ω , Anr, Acd, and Ar), the autonomous spending shock (XHG), t t t t price and wage mark-up shocks (Θcbi, Θcbi, and Θl), and technology shocks (Γ z,m and t t t t z,kb Γ ). t The aggregate risk premium shock (figure 3) depresses spending across the board, lowering hours appreciably; inflation and the federal funds rate fall in response. (As in the model of Smets and Wouters (2007), the aggregate risk premium drives down the flexible-price nominal interest rate one-for-one, and hence the downward move in the nominal funds rate facilitates moving the economy toward its flexible price outcome). Shocks to sectoral risk premia (figures 4, 5 and 6) principally depress spending in the associated category of expenditure, with offsetting positive effects on other spending (which is “crowded in”). The impulse responses to a capital-specific technology shock (shown in figure 11) are a touch more gradual, as the embodied component of this type of technological progressimpliesaneedfornonresidentialcapitalaccumulation. (Inaddition,thelongrun responses of nonresidential investment and consumer durables are much larger than those of other spending, reflecting the biased nature of this technology shock). Following an economy-wide technology shock (figure 12), output rises gradually to its long-run level; hours respond relatively little to the shock (in comparison to, for example, output, reflecting both the influence of stick prices and wages and the offsetting income and substitution effects of such a shock on households willingness to supply labor. 21

3.5 Implied Paths Figure 13 presents the observed data (in blue) and the observable data net of the model’s estimated measurement error (in black), along 95 percent confidence intervals. For series other than overall PCE price inflation, measurement error is a moderate portion of movements in the series. The larger role for measurement error in accounting for the path of PCE price inflation reflects the absence of separate sectors for food and energy in the model. Figures 14 and 15 report modal estimates of the model’s structural shocks and the persistent exogenous drivers (i.e., risk premia and autonomous demand). These series have recognizable patterns for those familiar with U.S. economic fluctuations. For example, the risk premia jump at the end of the sample, reflecting the financial crisis and the model’s identification of risk premia, both economy-wide and for housing, as key drivers. In addition, the large negative value for autonomous demand around 2005 reflects the widening of the current account deficit: While this factor is absent from our closed-economy model, the use of economywide data picks up the drag from demand other than demand for U.S. produced goods and services.3 Of course, these stories from a glance at the exogenous drivers yield applications foralternativeversionsoftheEDOmodelandfuturemodelenhancements. Forexample, the exogenous risk premia can easily be made to have an endogenous component following the approach of Bernanke, Gertler, and Gilchrist (1999) (and indeed we have considered models of that type). At this point we view incorporation of such mechanisms in our baseline approach as premature, pending ongoing research on financial frictions, banking, and intermediation in dynamic general equilibrium models. Nonetheless, the EDO model captured the key financial disturbances during the last several years in its current specification, and examining the endogenous factors that 3Modal and median estimates for this driver diverge because modal estimates of the driver’s persistence and variance are substantially greater than the median values. This feature of the distribution, in turn, reflects a high, but imprecise, estimate of persistence for this driver. 22

explain these developments will be a topic of further study. 4 Summing up This paper has presented documentation for the large-scale estimated EDO model of the U.S. economy used for projections and policy analysis at the Federal Reserve Board. Cyclical dynamics are mostly accounted for by shocks to risk premia (e.g., see the discussion of output gaps in Kiley (2010b)). The integration of business cycle andgrowthfactsinatwo-sectormodelwithinvestment-specifictechnologicalprogress also allows consideration of key drivers of productivity and long-run growth. Ongoing research examines a range of issues related to the sources of economic fluctuations, financial frictions, and the design of monetary and fiscal policy (e.g., at the zero lower bound). References Bernanke, B., M. Gertler, and S. Gilchrist. 1999. The financial accelerator in a quantitative business cycle framework, In: John B. Taylor and Michael Woodford, Editor(s), Handbook of Macroeconomics, Elsevier, 1999, Volume 1, Part 3, Pages 1341-1393. Beveridge, S. and C.R. Nelson. 1981. A new approach to the decomposition of economic time series into permanent and transitory components with particular attention to measurement of the business cycle, Journal of Monetary Economics 7, 151-174. Boivin, J., M. Kiley, and F.S. Mishkin. 2010. How Has the Monetary Transmission Mechanism Evolved Over Time? Forthcoming in B. Friedman and M. Woodford, eds., The Handbook of Monetary Economics, Elsevier. 23

Edge, R., Kiley, M., Laforte, J.P., 2008. Natural rate measures in an estimated DSGE model of the U.S. economy. Journal of Economic Dynamics and Control 32:25122535. Edge, R., Kiley, M., Laforte, J.P., 2010. A comparison of forecast performance between Federal Reserve staff forecasts, simple reduced-form models, and a DSGE model. Forthcoming in Journal of Applied Econometrics. Kiley, M., 2007. A Quantitative Comparison of Sticky-Price and Sticky-Information Models of Price Setting. Journal of Money, Credit, and Banking 39, 101-25. Kiley, M., 2010a. Habit Persistence, Non-separability between Consumption and Leisure, or Rule-of-Thumb Consumers: Which Accounts for the Predictability of Consumption Growth? Forthcoming in The Review of Economics and Statistics. Kiley, M., 2010b. Output Gaps. Forthcoming in Federal Reserve Board Finance and Economics Discussion Series (FEDS). Kydland, Finn and Prescott, Edward. 1982. Time-to-build and Aggregate Fluctuations. Econometrica, 50, 6,1345 - 1370. Laforte, J., 2007. Pricing Models: A Bayesian DSGE Approach to the U.S. Economy. Journal of Money, Credit, and Banking 39, 127-54. Smets, F., Wouters, R., 2007. Shocks and Frictions in the US Busines Cycles: A Bayesian DSGE Approach. American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June. 24

A Equilibrium in the Symmetric and Stationary Model The symmetric equilibrium is an allocation: H gdp ,Xcbi,Xkb,Enr,Er,Ecd,Ecnn,Λr,Λcd,Λcnn,Λ l,cbi ,Λ l,kb , t t t t t t t t t t t t n ∞ Lcbi,Lkb,Ucbi,Ukb,K u,nr,cbi ,K u,nr,kb ,K nr,cbi ,K nr,kb ,Knr ,Kr ,Kcd e et et et et e t e e t e t t t+1 t+1 t+1 t=0 o e e e e e e e and a sequence of values Πc,Π p,cbi ,Π p,kb ,Π w,cbi ,Π w,kb ,Pkb,Wcbi,Wkb, t t t t t t t t n cbi kb ∞ R t nr,cbi ,R t nr,kb ,R t nr,Ret r,Rft cd,MfC t ,MC t ,Qn t r,Qr t ,Qc t d,R t t=0 o e e e e e g g e e e thatsatisfythesymmetricandstationaryversionsofthefirst-orderconditionsimplied by the decisions problems of firms and households outlined in the main text, taking as given the initial values of the endogenous states and the sequence of exogenous variables ∞ Γ z,kb ,Γ z,m ,Θcbi,Θkb,Θl,Anr,Ar,Acd,Ω,XHG t t t t t t t t t t t=0 n o implied by the sequence of shocks e ∞ ǫ z,kb ,ǫ z,m ,ǫ θ,cbi ,ǫ θ,kb ,ǫ θ,l ,ǫ a,nr ,ǫ a,r ,ǫ a,cd ,ǫωǫHG,ǫR . t t t t t t t t t t t t=0 n o The stationary versions of the model’s key equations are presented in this section. Note also that definitions for all of the model’s stationary variables can be found in appendix F. The symmetric and stationary first-order conditions implied by the second step of the intermediate-goods producing firms’ cost minimization problems 25

(equation 5) are: s MC Ls=(1−α)·Xs · t for s = cbi,kb (25) t t Ws t s g K u,nr,s MeC t =α·Xs · t f for s = cbi,kb. (26) Γ x,kb t R nr,s t t e g α e K u,nr,s Xs=(Ls)1−α e t for s = cbi,kb (with Zcbi ≡ 1). (27) t t x,kb t Γ ! t e e The stationary price Phillips curves that are implied by the intermediate-goods producing firms’ profit maximization problems (equation 7) are cbi ΘcbiMC Xcbi= Θcbi −1 Xcbi t t t t t +1(cid:0)00·χp Π(cid:1) p,cbi −ηpΠ p,cbi −(1−ηp)Πp,cbi Π p,cbi Xcbi g e te t−1 ∗ t t (cid:16) (cid:17) Λcnn −βE t+1 ·100·χp Π p,cbi −ηpΠ p,cbi −(1−ηp)Π ep,cbi Π p,cbi Xcbi (28) t (Λcnn t+1 t ∗ t+1 t+1 ) t e (cid:16) (cid:17) ΘkbMC kb Xkb= Θkb −1 PkbXkb e t t t t e t t +1(cid:0)00·χp (cid:1)Π p,kb −ηpΠ p,kb −(1−ηp)Πp,kb Π p,kb PkbXkb g e et e t−1 ∗ t t t (cid:16) (cid:17) Λcnn −βE t+1 ·100·χp Π p,kb −ηpΠ p,kb −(1−ηp)Π ep,kbe Π p,kb Pkb Xkb (29) t (Λcnn t+1 t ∗ t+1 t+1 t+1 ) t e (cid:16) (cid:17) e e The symmetric and stationary first-order conditions implied by the non-residential e part of the capital owners’ profit-maximization problem (equation 8) are: Λcnn 1 Qnr=βE t+1 · Rnr +(1−δnr)Qnr (30) t t (Λcnn Γ x,kb t+1 t+1 ) t t+1 e (cid:16) (cid:17) e e e e 26

Rnr R nr,s = t for s = cbi,kb(31) t Us t e 1 e 1 R nr,s ψ Us= · t for s = cbi,kb(32) t κ Qnr ! t e Enr−Enr Pkb=Qnr Aenr −100·χnr t t−1 ·Γ x,kb t t t t " Knr !# t e e e e Λcnn Enr −Enr +βE t+1 ·Qnr ·100·χnr e t+1 t ·Γ x,kb (33) t (Λcnn t+1 Knr t+1 !) t t+1 e e e Knre 100·χnr Enr−Enr 2 Knr Knr =(1−δner) t +AnrEnr− e t t−1 ·Γ x,kb t (34) t+1 Γ x,kb t t 2 Knr t ! Γ x,kb t t t e e e e K ner,cbi +K nr,kb =Knr e (35) t t t e e e e Thesymmetricandstationaryfirst-orderconditionsimpliedbytheconsumer durables part of the capital owners’profit-maximization problem (equation 10) are: Λcnn 1 Qcd=βE t+1 · Rcd +(1−δcd)Qcd (36) t t (Λcnn Γ x,kb t+1 t+1 ) t t+1 e (cid:16) (cid:17) e e Ecd−Ecd e Pkb=Qcd Aecd −100·χcd t t−1 ·Γ x,kb t t t t " Kcd !# t e e e e Λcnn Ecd −Ecd +βE t+1 ·Qcd ·100·χcd e t+1 t ·Γ x,kb (37) t (Λcnn t+1 Kcd t+1 !) t t+1 e e e Kced 100·χcd Ecd−Ecd 2 Kcd Kcd =(1−δecd) t +AcdEcd − e t t−1 ·Γ x,kb t . (38) t+1 Γ x,kb t t 2 Kcd t ! Γ x,kb t t t e e e e e e The symmetric and stationary first-order conditions implied by the residential part e 27

of the capital owners’ profit-maximization problem (equation 11) are: Λcnn 1 Qr=βE t+1 · Rr +(1−δr)Qr (39) t t (Λcnn Γ x,cbi t+1 t+1 ) t t+1 e (cid:16) (cid:17) e eEr−Er e 1=Qr Aer −100·χr t t−1 ·Γ x,cbi t t t " Kr !# t e e e Λcnn Er −Er +βE t+1 ·Qr ·100·χre t+1 t ·Γ x,cbi (40) t (Λcnn t+1 Kr t+1 !) t t+1 e e e Kre 100·χr Er−Er 2 Kr Kr =(1−δer) t +ArEr − e t t−1 ·Γ x,cbi t . (41) t+1 Γ x,cbi t t 2 Kr t ! Γ x,cbi t t t e e e e e e The symmetric and stationary (expenditure-related) firest-order conditions implied by the households’ utility-maximization problem are: (equation 12) are: 1 Λcnn=βR E · Λcnn · (42) t t t ( t Πcbi Γ x,cbi ) t+1 t+1 e 1 e Λcnn=Λcd · (43) t t Rcd t 1 Λ ecnn=Λ er · (44) t t R er t e e 1 (h/Γ x,cbi ) Λc t nn=ςcnn ·e Ecnn −(h/Γ x,cbi ) Ecnn −βςcnnE t (Ecnn −(h/Γ t+ x 1 ,cbi ) Ecnn ) (45) t t t−1 t+1 t+1 t Λecd 1 (h/Γ x,kb ) t =ςcd · e e −βςcdE e t e (46) Γ x,kb Kcd −(h/Γ x,kb ) Kcd t (Kcd −(h/Γ x,kb ) Kcd ) t t t−1 t−1 t+1 t t e Λr 1 (h/Γ x,cbi ) t =ςr · e e −βςrE e t e (47) Γ x,cbi Kr −(h/Γ x,cbi ) Kr t (Kr −(h/Γ x,cbi ) Kr ) t t t−1 t−1 t+1 t t e The key equations from the households’ labor-supply decision are the wage Phillips e e e e 28

curves l,cbi Λ Θl · t ·Lcbi t t Λcnn t = Θl −1 WcbiLcbi t t t e Lcbi Lkb Lcbi Lcbi −Θ (cid:0)l t ·100· (cid:1) χf l Lcbi + ∗ Lcbi ·W t cbi + Lcbi + ∗ Lcbi ·W t kb L t kb − L t k − b 1 (cid:18) ∗ ∗ ∗ ∗ (cid:19)(cid:18) t t−1(cid:19) +100·χw Π w,cbi −ηwΠ w,cbif−(1−ηw)Πw,cbi Π wf,cbi WcbiLcbi t t−1 ∗ t t t (cid:16) (cid:17) Λcnn −βE t+1 ·100·χw Π w,cbi −ηwΠ w,cbi −(1−ηw)Π fw,cbi Π w,cbi WcbiLcbi (48) t (Λcnn t+1 t ∗ t+1 t+1 t+1 ) t e (cid:16) (cid:17) Λ l,kb f Θl · t e·Lkb t t Λcnn t = Θl −1 WkbLkb t t t e Lcbi Lkb Lcbi Lcbi +Θ (cid:0)l t ·100· (cid:1) χf l Lcbi + ∗ Lcbi ·W t cbi + Lcbi + ∗ Lcbi ·W t kb L t kb − L t k − b 1 (cid:18) ∗ ∗ ∗ ∗ (cid:19)(cid:18) t t−1(cid:19) +100·χw Π w,kb −ηwΠ w,kb −f(1−ηw)Πw,kb Π w,kfb WkbLkb t t−1 ∗ t t t (cid:16) (cid:17) Λcnn −βE t+1 ·100·χw Π w,kb −ηwΠ w,kb −(1−ηw)Π fw,kb Π w,kb Wkb Lkb (49) t (Λcnn t+1 t ∗ t+1 t+1 t+1 ) t e (cid:16) (cid:17) f The model’s other conditions for equilibrium, listed in appendix A for the none stationary model, are transformed as follows in the stationary model: • The model’s market clearing conditions become: Xcbi = Ecnn+Er+XHG, Xkb = Ecd+Enr+XHG, K u,nr,cbi = UcbiK nr,cbi , andK u,nr,kb = UkbK nr,kb . t t t t t t t t t t t t t t • Tehe idenetitiesebetweeen reeal wagees, releativeeprices,eand wage andeprice inflatieon e rates become: p,kb x,kb p,ch w,s Π Γ Π Π 1 Pkb= t · t ·Pkb , Pch= t ·Pkb and Ws= t · ·Ws fors = cbi,kb t p,cbi x,cbi t−1 t p,cbi t−1 t p,cbi x,cbi t−1 Π Γ Π Π Γ t t t t t • Eequations (15) ande (17)ethat descriebe the befhavior of monetafry policy are already described in terms of stationary variables; 29

B The Steady-state Solution to the Symmetric and Stationary Model The steady-state growth rates in the fast- and slow-growing sectors of the economy are, respectively, Γx,kb = Γz,mΓz,kb and (50) ∗ ∗ ∗ Γx,cbi = Γz,m(Γz,kb)α. (51) ∗ ∗ ∗ From the steady-state version of the Euler equation (equation 42), we know that the steady-state nominal interest rate is given by: 1 1 R = ·Γx,cbiΠp,cbi = ·Γx,kbΠk (52) ∗ β ∗ ∗ β ∗ ∗ while the real interest rates relevant to consumers, capital owners, and producers respectively are: R 1 ∗ = ·Γx,cbi and (53) Π p,cbi β ∗ ∗ R 1 1 Πp,cbi ∗ = ·Γx,kb= ·Γx,cbi · ∗ . (54) Π p,kb β ∗ β ∗ Π p,kb ∗ ∗ The steady-state values of the relative prices of fast-growing goods (Pkb), installed t non-residential capital goods (Qnr) and installed consumer durables (Qcd) is equal to t et the steady-state relative mark-ups in the two sectors. Since we assume that Θcbi = e e ∗ Θkb, these relative prices are all unity in the steady-state. The relative price of ∗ installed residential capital goods (Qr) is also unity. Specifically, t Θkb Θcbi −1 Pkb = Qcd = Qnr = e ∗ · ∗ = 1 and Qr = 1. ∗ ∗ ∗ Θkb −1 Θcbi ∗ ∗ ∗ e e e e The steady-state values of real marginal cost, the real rental rate, and the real wage can be calculated from the steady-state versions of equations (25), (26), (27), (28), 30

(29), and (30). These are cbi kb Θcbi −1 Θkb −1 MC =MC = ∗ ,= Pkb · ∗ , (55) ∗ ∗ Θcbi ∗ Θkb ∗ ∗ 1 R g Rnr,s=R gnr = Pkb ·Γx,kb − e (1−δnr) =Pkb ∗ −(1−δnr) , and (56) ∗ ∗ ∗ β ∗ ∗ Π p,kb (cid:18) (cid:19) (cid:18) ∗ (cid:19) 1 α e e e Θkb −1 1−α e α 1−α Ws=W = (1−α) ∗ Pkb (57) ∗ ∗ Θkb ∗ Γ x,kb /β −(1−δnr) (cid:18) ∗ (cid:19) (cid:18) ∗ (cid:19) f f e From our calibration of κ, the parameter in the non-residential capital owners utilization function, we know from equation (32) that Ucbi = Ukb = 1. It is useful to note ∗ ∗ from the above equations that: 1−α Ws 1 Θkb −1 ∗ = ·Pk · ∗ (1−α)1−α (α) α R ∗ nr,s ! R ∗ nr,s ∗ Θk ∗ b f α Rnr,s 1 e Θkb −1 e∗ = e ·Pkb · ∗ (1−α)1−α (α) α W ∗ s ! W ∗ s ∗ Θk ∗ b e e From equations (36) and (39) note also that: f f 1 R Rcd=Pkb ·Γx,kb − 1−δcd =Pkb ∗ − 1−δcd , and (58) ∗ ∗ β ∗ ∗ Π p,kb (cid:18) (cid:19) (cid:18) ∗ (cid:19) 1 (cid:0) (cid:1) R (cid:0) (cid:1) e Rr= e ·Γx,cbi −(1−δr) = ∗e −(1−δr) (59) ∗ β ∗ Π p,cbi (cid:18) (cid:19) (cid:18) ∗ (cid:19) e The steady-state inflation rates of capital prices and of nominal wages are given by: Πp,kb=Πp,cbi(Γx,cbi/Γx,kb)=Πp,cbi(1/Γz,kb)1−α, and (60) ∗ ∗ ∗ ∗ ∗ ∗ Πw,s=Πw = Πp,cbiΓx,cbi = Πp,cbiΓz,m(Γz,kb)α, for s = cbi,kb, (61) ∗ ∗ ∗ ∗ ∗ ∗ ∗ where the steady-state inflation rate of consumption prices Πp,cbi is set by the prefer- ∗ ences of the central bank. The steady-state ratios Lcbi/Ycbi, Knr,cbi/Xcbi, Lkb/Ykb, and Knr,kb/Xkb, which ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ e e e e e e 31

are calculated from the factor demand schedules (equations 25 and 26), are 1−α Knr,cbi 1 α 1−α Ws α Θkb −1 ∗ · = ∗ = · ∗ , (62) X ∗ cbi Γ x ∗ ,kb (cid:18) 1−α (cid:19) R ∗ nr,s ! Γ x ∗ ,kb /β −(1−δnr) Θk ∗ b e f Lcbi 1−α α Rnr,s α Γx,kb/β −(1−δnr) Θkb 1− α α e ∗ = ∗e = ∗ · ∗ , (63) X ∗ cbi (cid:18) α (cid:19) W ∗ s ! (cid:18) α Θk ∗ b −1 (cid:19) e 1−α Knr,kb 1 α 1−α Ws α Θkb −1 ∗ · e = f ∗ = · ∗ , and (64) X ∗ kb Γ x ∗ ,kb (cid:18) 1−α (cid:19) R ∗ nr,s ! Γ x ∗ ,kb /β −(1−δnr) Θk ∗ b e f Lkb 1−α α Rnr,s α Γx,kb/β −(1−δnr) Θkb 1− α α e ∗ = ∗e = ∗ · ∗ . (65) X ∗ kb (cid:18) α (cid:19) W ∗ s ! (cid:18) α Θk ∗ b −1 (cid:19) e We can write these as e f Knr,cbi 1 Lcbi Knr,kb 1 Lkb ∗ · = A, ∗ = (A)− 1− α α, ∗ · = A, and ∗ = (A)− 1− α α, Xcbi Γ x,kb Xcbi Xkb Γ x,kb Xkb ∗ ∗ ∗ ∗ ∗ ∗ e e e e (66) e e e e where α Θkb −1 A = · ∗ Γ x,kb /β −(1−δnr) Θkb ∗ ∗ We calibrate aggregate labor input, Lcbi+Lkb, to 0.25. To solve for Lcbi, Kcbi, Lkb, ∗ ∗ ∗ ∗ ∗ and Kkb by themselves we need to solve first for Xcbi and Xkb. This takes a few steps. ∗ ∗ ∗ e The first step is to derive the ratio of Xkb/Xcbi; as we have assumed that autonomous e ∗ ∗ e e demand enters symmetrically, its presence has no effect on this steady-state ratio, so e e we suppress this portion of demand in the following. As part of this exercise we must turn to considering the expenditure side of the model, and in particular the model’s expenditure ratios. The normalizing factors ςcd and ςr are calibrated so that the ratios E e ∗ cd and E e ∗ r are 0.1682 and 0.2094 respec- E e ∗ cnn E e ∗ cnn tively. To calculate the model’s expenditure ratios, we start with what we know about the ratios between the inputs to the optimizing household’s utility function, that is 32

Ecnn, Kcd, and Kr. We know from equations (43) to (47) that ∗ ∗ ∗ Kcd ςcd 1−βh/Γx,kb 1−h/Γx,cbi 1 ∗ = · · · , and Ecnn ςcnn 1−βh/Γx,cbi 1−h/Γx,kb Rcd ∗ ∗ e Kr ςr 1−βh/Γx,cbi 1−h/Γx,cbi 1 e ∗ = · · · e Ecnn ςcnn 1−βh/Γx,cbi 1−h/Γx,cbi Rr ∗ ∗ e We have expressions for Rcd and Rr in equations (58) and (59) and we know from e ∗ ∗ e the steady-state versions of equations (38) and (41) that e e Ecd Γx,kb −(1−δcd) Er Γx,cbi −(1−δr) ∗ = ∗ and ∗ = ∗ . (67) Kcd Γ x,kb Kr Γ x,cbi ∗ (cid:18) ∗ (cid:19) ∗ (cid:18) ∗ (cid:19) e e These equations imply that the ratios of expenditures implied by the optimizing e e agents of the model are Ecd ςcd 1−βh/Γx,kb 1−h/Γx,cbi Γx,kb−(1−δcd) 1 β ∗ = · · · ∗ · · =D Ecnn ςcnn 1−βh/Γx,cbi 1−h/Γx,kb Γ x,kb Pkb Γ x,kb −β(1−δcd) ∗ ∗ ∗ ∗ e (68) e e Er ςr 1−βh/Γx,cbi 1−h/Γx,cbi Γx,cbi−(1−δr) β ∗ = · · · ∗ · =R (69) Ecnn ςcnn 1−βh/Γx,cbi 1−h/Γx,cbi Γ x,cbi Γ x,cbi −β(1−δr) ∗ ∗ ∗ e We can now consider expenditures as shares of their sector’s outputs. Recall from e the equilibrium conditions listed in appendix A that Ecnn +Er=Xcbi and Ecd +Enr=Xkb, ∗ ∗ ∗ ∗ ∗ ∗ Consider first the market clearing condition for the slow growing sector. Since all aggregates in this equation grow at the same rate we can re-write the steady-state expression for Xcbi as shown below (as well as in appendix B) and with some manipt ulations Xcbi=Ecnn +Er implies ∗ ∗ ∗ Ecnn Er Ecnn Er Ecnn Ecnn Er Ecnn e 1= e ∗ +e ∗ = ∗ + ∗ · ∗ = ∗ 1+ ∗ = ∗ (1+R). Xcbi Xcbi Xcbi Ecnn Xcbi Xcbi Ecnn! Xcbi ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ e e e e e e e e This then allows us to write: e e e e e e e e Ecnn Ecnn 1 1 Er Ecnn 1 R ∗ = ∗ = = and ∗ =1− ∗ =1− = , Xcbi Ecnn+Er 1+Er/Ecnn 1+R Xcbi Xcbi 1+R 1+R ∗ ∗ ∗ ∗ ∗ ∗ ∗ e e e e (70) e e e e e e e 33

where R was defined in equation (69). For the fast-growing sector, we can re-write the market clearing condition (as in appendix B) as: Xkb=Ecd +Enr. ∗ ∗ ∗ e e e and make similar tranformations as before. A useful relationship for these transformations is from equations (34) and (35), that is, Γx,kb −(1−δnr) Γx,kb −(1−δnr) Enr = ∗ Knr = ∗ Knr,cbi +Knr,kb . ∗ x,kb ∗ x,kb ∗ ∗ Γ Γ (cid:18) ∗ (cid:19) (cid:18) ∗ (cid:19) (cid:16) (cid:17) e e e e The fast growing sector’s market clearing condition can also be manipulated; specifically, Xkb=Ecd +Enr implies ∗ ∗ ∗ Ecd Enr Ecd Ecnn Xcbi Γx,kb−(1−δnr) Knr,cbi Xcbi Knr,kb e 1= e ∗ +e ∗ = ∗ · ∗ · ∗ + ∗ ∗ · ∗ + ∗ X ∗ kb X ∗ kb E ∗ cnn X ∗ cbi X ∗ kb (cid:18) Γ x ∗ ,kb (cid:19) X ∗ cbi X ∗ kb X ∗ kb ! e e e e e e e e We havee expreessions feor E e ∗ cde, E e ∗ cnn ,eK e ∗ nr,cbi , and K e ∗ nr,kb . Substituteing in tehese experes- E e ∗ cnn X e ∗ cbi X e ∗ cbi X e ∗ kb sions yields 1 Xcbi Γx,kb −(1−δnr) Xcbi 1=D · ∗ + ∗ A·Γx,kb ∗ +1 , (cid:18) 1+R (cid:19) X ∗ kb (cid:18) Γ x ∗ ,kb (cid:19) ∗ X ∗ kb ! e e which can be re-arranged to e e Xkb D+(1+R) Γx,kb −(1−δnr) A ∗ = ∗ = B. (71) X ∗ cbi (1+R)−(1+R(cid:0)) Γ x ∗ ,kb −(1−δ(cid:1)nr) A e (cid:16) (cid:17) This then allowseus to write: Enr Γx,kb−(1−δnr) Knr,cbi Xcbi Knr,kb Γx,kb−(1−δnr) 1+B ∗ = ∗ ∗ · ∗ + ∗ = ∗ A· , X ∗ kb (cid:18) Γ x ∗ ,kb (cid:19) X ∗ cbi X ∗ kb X ∗ kb ! (cid:18) Γ x ∗ ,kb (cid:19) B e e e e (72) e e e e so that Ecd Enr Γx,kb−(1−δnr) 1+B ∗ = 1− ∗ = 1− ∗ A· . Xkb Xkb Γ x,kb B ∗ ∗ (cid:18) ∗ (cid:19) e e e e 34

Since the right-hand sides of equations (63) and (65) are identical, Lcbi/Xcbi = ∗ ∗ Lkb/Xkb. As a result, Xkb/Xcbi = B implies that Lkb/Lcbi = B, which means then ∗ ∗ ∗ ∗ ∗ ∗ e e that: e e e e 1 B Lcbi = ·L and Lkb = ·L. ∗ 1+B ∗ 1+B where L = Lcbi +Lkb is 0.25. Solving for Lcbi and Lkb allows us to find the steady- ∗ ∗ ∗ ∗ state solutions for all of the other production, factor, and expenditure variables of the model. Specifically: • Lcbi and Lcbi/Xcbi (defined in equation 63) imply Xcbi; ∗ ∗ ∗ ∗ • Lkb and Lkb/Xkb (defined in equation 65) imply Xkb; ∗ ∗ e∗ e∗ • Xcbi and Knr,cbi/Xcbi (defined in equation 62) imply Knr,cbi and (since Ucbi = 1) ∗ ∗ e ∗ e ∗ ∗ Ku,nr,cbi; e∗ e e e • Xkb and Knr,kb/Xkb (defined in equation 64) imply Knr,kb and (since Ukb = 1) e∗ ∗ ∗ ∗ ∗ Ku,nr,kb; e∗ e e e • Knr,cbi, Knr,kb, and the non-residential capital market clearing condition imply e∗ ∗ Knr; e∗ e • Xcbi and Ecnn/Xcbi and Er/Xcbi (both defined in equation 70) imply Ecnn and e∗ ∗ ∗ ∗ ∗ ∗ Er; e∗ e e e e e • Xkb and Ecd/Xkb and Enr/Xkb (defined in equations 72 and B) imply Ecd and e∗ ∗ ∗ ∗ ∗ ∗ Enr; e∗ e e e e e • Ecd and Er, and Ecd/Kcd and Er/Kr (both defined in equation 67) imply Kcd e∗ ∗ ∗ ∗ ∗ ∗ ∗ and Kr; and, e ∗ e e e e e e • Λcnn, Λcd, and Λr are then implied by the steady-state versions of equations ∗ e ∗ ∗ (45) to (47). e e e The reader can verify that we have in this section presented a steady-state value for all of the model variables that defined equilibrium in appendix A. 35

C List of Model Parameters h = Habit-persistence parameter for the consumption of non-durable goods and nonhousing services. α = The elasticity of output with respect to capital. β = The household’s discount factor. δcd = The quarterly depreciation rate of consumer durables. δnr = The quarterly depreciation rate of non-residential capital. δr = The quarterly depreciation rate of residential capital. ηp = Parameter reflecting the relative importance of lagged price inflation in the adjustment cost function for prices. ηw = Parameter reflecting the relative importance of lagged wage inflation in the adjustment cost function for wages. κ = Variable capacity utilization scaling parameter. ν = Inverse labor supply elasticity. ρa,nr = Persistence parameter in the AR(1) process describing the evolution of Anr. t ρa,cd = Persistence parameter in the AR(1) process describing the evolution of Acd. t ρa,r = Persistence parameter in the AR(1) process describing the evolution of Ar. t ρω = Persistence parameter in the AR(1) process describing the evolution of Ω . t ςcnn = Co-efficient on the consumer non-durable goods and non-housing serives component of the utility function. ςcd = Co-efficient on the consumer durable goods component of the utility function. ςr = Co-efficient on the consumer housing serives component of the utility function. ςl = Co-efficient on the labor supply components of the utility function. φY = Co-efficient on GDP gap in the monetary policy reaction function. φ∆Y = Co-efficient on change in the GDP gap in the monetary policy reaction function. φπ = Co-efficient on GDP price inflation in the monetary policy reaction function. 36

φr = Co-efficient on lagged nominal interest rates in the monetary policy reaction function. χcd = Investment adjustment costs in the consumer durables evolution equation. χnr = Investment adjustment costs in the non-residential capital evolution equation. χr = Investment adjustment costs in the residential capital evolution equation. χl =Parameterreflectingthesizeofadjustmentcostsinthelaborsectoraladjustment cost function. χp = Parameter reflecting the size of adjustment costs in re-setting prices. χw = Parameter reflecting the size of adjustment costs in re-setting wages. ψ = Elasticity of utilization costs. D List of Endogenous and Exogenous Model Variables Ω = Aggregate risk premium. t Anr = Non-residential sector risk premium. t Ar = Residential sector risk premium. t Acd = Consumer durables sector risk premium. t Enr = Expenditures on goods in the fast-growing “capital” goods sector for use in t non-residential investment. Er = Expenditures on goods in the slow-growing “consumption” goods sector for use t in residential investment. Ecd = Expenditures on goods in the fast-growing “capital” goods sector for use in t consumer durables investment. Ecnn = Expenditures on goods in the slow-growing “consumption” goods sector for t use in consumer non-durable goods and non-housing services. XHG = Exogenous expenditure (by the government and foreign sector). t gdp H = Growth rate of real (chain-weighted) GDP. et 37

K u,nr,cbi = The amount of utilized non-residential capital used in the slow-growing t “consumption”goods sector. K u,nr,kb = The amount of utilized non-residential capital used in the fast-growing t “capital” goods sector. K nr,cbi = The physical amount of non-residential capital used in the slow-growing t “consumption” goods sector. K nr,kb = The physical amount of non-residential capital used in the fast-growing t “capital” goods sector. Knr = The aggregate non-residential capital stock. t Kr = The residential capital stock. t Kcd = The consumer durables capital stock. t Lcbi = Labor used in the slow-growing “consumption” goods sector. t Lkb = Labor used in the fast-growing “capital” goods sector. t MCcbi = Marginal cost in the slow-growing “consumption” goods sector. t MCkb = Marginal cost in the fast-growing “capital” goods sector. t Pcbi = Price level in the slow-growing “consumption” goods sector. t Pkb = Price level in the fast-growing “capital” goods sector. t Qnr = Price of installed non-residential capital. t Qr = Price of installed residential capital. t Qcd = Price of installed consumer durables capital. t R = Nominal interest rate. t nr,cbi R = The nominal rental rate on non-residential capital used in the slow-growing t “consumption” goods sector. nr,kb R = The nominal rental rate on non-residential capital used in the fast-growing t “capital” goods sector. Rnr = The aggregate nominal rental rate on non-residential capital. t Rr = The nominal rental rate on residential capital. t Rcd = The nominal rental rate on consumer durables capital. t 38

Ucbi = The utilization rate of non-residential capital used in the slow-growing “cont sumption” goods sector. Ukb =Theutilizationrateofnon-residentialcapitalusedinthefast-growing“capital” t goods sector. Wcbi = The nominal wage in the slow-growing “consumption” goods sector. t Wkb = The nominal wage in the fast-growing “capital” goods sector. t Xcbi = Production in the slow-growing “consumption” goods sector. t Xkb = Production in the fast-growing “capital” goods sector. t (Zkb)1−α = Level of capital-specific technology. t (Zm)1−α = Level of economy-wide technology. t x,cbi Γ = Growth rate of output in the consumption (slow growth) sector consist tent with the growth rate of technology. (Note Γ x,cbi is not in general equal to t ln(Xcbi/Xcbi ). Rather it is equal to Γ z,m (Γ z,kb )α.) t t−1 t t x,kb Γ =Growthrateofoutputintheconsumption(slowgrowth)sectorconsistentwith t the growth rate of technology. (Note Γ x,kb is not in general equal to ln(Xkb/Xkb ). t t t−1 z,m z,kb Rather it is equal to Γ Γ .) t t z,kb (1−α)Γ = The growth rate of the level of capital-specific technology. t z,m (1−α)Γ = The growth rate of the level of economy-wide technology. t Θl = The elasticity of subsitution between the differentiated labor inputs into prot duction. Θcbi = The elasticity of subsitution between the differentiated intermediate inputs in t the slow-growing “consumption” goods sector. Θkb = The elasticity of subsitution between the differentiated intermediate inputs in t the fast-growing “capital” goods sector. Λr = The marginal utility of residential capital. t Λcd = The marginal utility of durable goods. t Λcnn = The marginal utility of non-durable goods and non-housing services consumpt tion. 39

l,cbi Λ =Themarginaldis-utilityofsupplyinglaborintheslow-growing“consumption” t goods sector. l,kb Λ = The marginal dis-utility of supplying labor in the fast-growing “capital” goods t sector. Πc = The inflation rate of the PCE deflator. t p,cbi Π = The inflation rate for prices in the slow-growing “consumption” goods sector. t p,kb Π = The inflation rate for prices in the fast-growing “capital” goods sector. t w,cbi Π =Theinflationrateofnominalwagesintheslow-growing“consumption”goods t sector. w,kb Π =Theinflationrateofnominalwagesinthefast-growing“capital”goodssector. t E Definitions of Stationary Model Variables In this section we provide definitions for all of the variables of the model that must be transformed in order to render them stationary. Note that in going through our list of model variables we leave out those that are already stationary. The model’s output variables in stationary form are: Xcbi Xcbi = t t Zm(Zkb)α(Zcbi)1−α t t t Xkb X ekb = t t ZmZkb t t e The model’s expenditure variables in stationary form are: Enr Enr = t t ZmZkb t t Er Eer = t t Zm(Zkb)α(Zcbi)1−α t t t Ecd E ecd = t t ZmZkb t t Ecc Eecnn = t t Zm(Zkb)α(Zcbi)1−α t t t e 40

The model’s marginal utility variables in stationary form are: Λr = ΛrZm(Zkb)α(Zcbi)1−α t t t t t Λcd = ΛcdZmZkb et t t t Λcnn = ΛcnnZm(Zkb)α(Zcbi)1−α et t t t t e The model’s capital stock variables in stationary form are: u,nr,cbi K K u,nr,cbi = t t ZmZkb t t u,nr,kb K K eu,nr,kb = t t ZmZkb t t nr,cbi K K enr,cbi = t t ZmZkb t t nr,kb K K enr,kb = t t ZmZkb t t Knr Kenr = t t+1 ZmZkb t t Kr Ker = t t+1 Zm(Zkb)α(Zcbi)1−α t t t Kcd K ecd = t t+1 ZmZkb t t e The model’s relative (KB) output price variable in stationary form is: Pkb Zkb 1−α Pkb = t t t Pcbi Zcbi t (cid:18) t (cid:19) e The model’s real wage variables are: Wcbi 1 Wcbi = t · t Pcbi Zm(Zkb)α(Zcbi)1−α t t t t Wkb 1 W fkb = t · t Pcbi Zm(Zkb)α(Zcbi)1−α t t t t f 41

The model’s real rental rate variables in stationary form are: R nr,cbi Zkb 1−α R nr,cbi = t t t Pcbi Zcbi t (cid:18) t (cid:19) R enr,kb = R t nr,kb Z t kb 1−α t Pcbi Zcbi t (cid:18) t (cid:19) e Rnr Zkb 1−α Rnr = t t t Pcbi Zcbi t (cid:18) t (cid:19) Rr Rer = t t Pcbi t e Rcd Zkb 1−α Rcd = t t t Pcbi Zcbi t (cid:18) t (cid:19) e The model’s real marginal cost variables in stationary form are: cbi MCcbi MC = t t Pcbi t gkb MCkb Zkb 1−α MC = t t t Pcbi Zcbi t (cid:18) t (cid:19) g The model’s relative price of installed capital variables in stationary form are: Qnr Zkb 1−α Qnr = t t t Pcbi Zcbi t (cid:18) t (cid:19) Qr Qer = t t Pcbi t e Qcd Zkb 1−α Qcd = t t t Pcbi Zcbi t (cid:18) t (cid:19) e 42

Table 1: Calibrated Parameters β α ψ δnr δcd δr Θcbi, Θkb, Θl Γz,m Γz,kb ω Πc ∗ ∗ ∗ ∗ ∗ HG ∗ 0.990 0.260 1 0.030 0.055 0.004 7.000 1.000 1.011 0.20 1.005 Table 2: Measurement Errors on Observable Variables ME ME ME ME ME ∆gdp ∆cns ∆cd ∆res ∆bi 0.3 0.1 1.5 1.5 1.5 ME ME ME ME ME ∆ppce ∆pcorepce ∆pcd h rw 0.5 0.05 0.2 0.3 0.3 43

Table 3: Prior and Posterior Distributions of the Behavioral and Policy Parameters Prior Distribution Posterior Distribution Parameter Type Mean S.D. Mode S.D. 10th perc. 50th perc. 90th perc. h N 0.000 0.3300 0.6024 0.0350 0.5917 0.6392 0.6807 ν G 2.000 1.0000 0.1918 0.2514 0.1409 0.3860 0.7701 χp G 4.000 1.0000 2.5028 1.0797 2.2321 3.2782 4.8710 χl G 4.000 1.0000 3.8424 1.9715 1.9764 3.9778 6.8915 χw G 4.000 1.0000 2.1868 1.0576 2.1997 3.3348 4.8769 χnr G 4.000 1.0000 0.2411 0.0911 0.2239 0.3180 0.4504 χcd G 4.000 1.0000 0.3702 0.5521 0.4485 0.9534 1.8840 χr G 4.000 1.0000 8.6694 2.3585 7.4588 9.9908 13.3231 ηp N 0.000 0.5000 0.3006 0.1343 0.2325 0.4056 0.5779 ηw N 0.000 0.5000 0.2542 0.1318 0.0823 0.2505 0.4207 φ N 1.500 0.0625 1.4562 0.0606 1.3776 1.4548 1.5331 π φ N 0.250 0.1250 0.2096 0.0283 0.1769 0.2101 0.2486 y φ N 0.000 0.1250 0.3310 0.0936 0.2104 0.3273 0.4488 △y φr N 0.500 0.2500 0.6593 0.0453 0.5949 0.6559 0.7116 Table 4: Prior and Posterior Distributions of the Parameters corresponding to the Exogenous Processes Prior Distribution Posterior Distribution Parameter Type Mean S.D. Mode S.D. 10th perc. 50th perc. 90th perc. ρω N 0.000 0.3300 0.7930 0.0364 0.7579 0.8070 0.8502 ρnr N 0.000 0.3300 0.8297 0.0302 0.8076 0.8496 0.8836 ρcd N 0.000 0.3300 -0.2110 0.1422 -0.4099 -0.2412 -0.0469 ρHG B 0.500 0.0150 0.9173 0.1637 0.4577 0.6821 0.8969 ρr N 0.000 0.3300 0.8328 0.0285 0.7914 0.8324 0.8637 σ I 1.000 2.0000 0.3742 0.0597 0.3234 0.3881 0.4737 ω σ I 1.000 2.0000 1.4573 0.3374 0.5267 0.7994 1.3940 HG σ I 1.000 2.0000 1.5877 0.7145 1.6168 2.4055 3.4337 θ,l σ I 0.200 2.0000 0.1572 0.0134 0.1437 0.1595 0.1778 r σ I 0.250 2.0000 0.8771 0.1321 0.7181 0.8748 1.0533 z,k σ I 0.250 2.0000 0.4036 0.0663 0.3751 0.4551 0.5437 z,m σ I 0.200 2.0000 0.3125 0.1576 0.2845 0.4296 0.6678 θ,cbi σ I 0.200 2.0000 0.4621 0.2747 0.3926 0.6584 1.0556 θ,kb σ I 1.000 2.0000 0.4921 0.1562 0.4102 0.5433 0.7742 a,r σ I 1.000 2.0000 7.2703 11.9676 8.8443 18.8741 38.5473 a,cd σ I 1.000 2.0000 0.4788 0.0866 0.3984 0.4922 0.6190 a,nr 44

Table 5: Variance Decompositions for Real GDP, Hours, Inflation and the Federal Funds Rate Shocks Horizon RealGDP Hours Inflation(core) FederalFundsRate ǫω 1 (0.24,0.27,0.30) (0.42,0.46,0.50) (0.04,0.06,0.08) (0.17,0.20,0.24) 4 (0.24,0.27,0.31) (0.41,0.45,0.49) (0.10,0.13,0.17) (0.46,0.50,0.55) 16 (0.26,0.29,0.33) (0.34,0.38,0.42) (0.09,0.12,0.16) (0.53,0.58,0.63) ∞ (0.26,0.29,0.33) (0.31,0.36,0.39) (0.07,0.09,0.13) (0.46,0.51,0.56) ǫHG 1 (0.03,0.04,0.06) (0.03,0.04,0.06) (0.00,0.00,0.00) (0.01,0.02,0.03) 4 (0.03,0.04,0.06) (0.01,0.01,0.02) (0.00,0.00,0.00) (0.00,0.01,0.01) 16 (0.03,0.04,0.06) (0.00,0.01,0.01) (0.00,0.00,0.00) (0.00,0.00,0.01) ∞ (0.03,0.04,0.06) (0.00,0.01,0.01) (0.00,0.00,0.00) (0.00,0.00,0.01) ǫθ,l 1 (0.00,0.00,0.00) (0.01,0.01,0.02) (0.09,0.12,0.16) (0.01,0.01,0.01) 4 (0.00,0.00,0.00) (0.02,0.02,0.03) (0.19,0.23,0.28) (0.03,0.03,0.05) 16 (0.00,0.00,0.00) (0.02,0.03,0.04) (0.14,0.18,0.22) (0.03,0.03,0.05) ∞ (0.00,0.00,0.00) (0.02,0.03,0.03) (0.09,0.12,0.16) (0.02,0.03,0.04) ǫr 1 (0.01,0.02,0.02) (0.03,0.03,0.04) (0.00,0.00,0.00) (0.52,0.58,0.63) 4 (0.01,0.02,0.02) (0.02,0.02,0.03) (0.00,0.00,0.00) (0.17,0.21,0.25) 16 (0.01,0.02,0.02) (0.01,0.02,0.02) (0.00,0.00,0.00) (0.10,0.12,0.14) ∞ (0.01,0.02,0.02) (0.01,0.01,0.02) (0.00,0.00,0.00) (0.08,0.10,0.12) ǫz,k 1 (0.19,0.23,0.28) (0.01,0.02,0.02) (0.00,0.00,0.01) (0.01,0.01,0.02) 4 (0.16,0.20,0.24) (0.01,0.02,0.02) (0.01,0.01,0.02) (0.01,0.01,0.02) 16 (0.15,0.19,0.23) (0.03,0.04,0.05) (0.01,0.02,0.04) (0.01,0.01,0.02) ∞ (0.15,0.19,0.23) (0.04,0.05,0.06) (0.03,0.05,0.06) (0.01,0.02,0.03) ǫz,m 1 (0.20,0.25,0.29) (0.00,0.00,0.00) (0.05,0.08,0.10) (0.03,0.04,0.05) 4 (0.18,0.23,0.27) (0.01,0.02,0.02) (0.14,0.18,0.22) (0.03,0.04,0.05) 16 (0.17,0.21,0.26) (0.04,0.05,0.06) (0.19,0.23,0.29) (0.04,0.05,0.07) ∞ (0.17,0.21,0.25) (0.05,0.06,0.08) (0.20,0.25,0.31) (0.05,0.07,0.09) ǫθ,cbi 1 (0.00,0.00,0.00) (0.00,0.00,0.00) (0.59,0.67,0.73) (0.05,0.06,0.07) 4 (0.00,0.00,0.00) (0.00,0.00,0.00) (0.25,0.31,0.36) (0.02,0.03,0.04) 16 (0.00,0.00,0.00) (0.00,0.00,0.00) (0.15,0.18,0.22) (0.01,0.02,0.02) ∞ (0.00,0.00,0.00) (0.00,0.00,0.00) (0.10,0.13,0.15) (0.01,0.02,0.02) ǫθ,kb 1 (0.00,0.00,0.00) (0.00,0.00,0.00) (0.03,0.04,0.05) (0.00,0.00,0.00) 4 (0.00,0.00,0.00) (0.00,0.00,0.00) (0.02,0.02,0.03) (0.00,0.00,0.00) 16 (0.00,0.00,0.00) (0.00,0.00,0.00) (0.01,0.01,0.02) (0.00,0.00,0.00) ∞ (0.00,0.00,0.00) (0.00,0.00,0.00) (0.01,0.01,0.01) (0.00,0.00,0.00) ǫa,r 1 (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) 4 (0.00,0.00,0.00) (0.00,0.01,0.01) (0.00,0.00,0.00) (0.00,0.00,0.01) 16 (0.00,0.00,0.01) (0.04,0.05,0.06) (0.01,0.02,0.02) (0.03,0.04,0.05) ∞ (0.00,0.00,0.01) (0.05,0.06,0.08) (0.11,0.15,0.19) (0.09,0.11,0.14) ǫa,cd 1 (0.02,0.03,0.03) (0.02,0.03,0.03) (0.00,0.00,0.00) (0.01,0.01,0.02) 4 (0.03,0.03,0.04) (0.01,0.01,0.01) (0.00,0.00,0.00) (0.00,0.01,0.01) 16 (0.02,0.03,0.04) (0.00,0.00,0.01) (0.00,0.00,0.00) (0.00,0.00,0.00) ∞ (0.02,0.03,0.04) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) ǫa,nr 1 (0.11,0.14,0.17) (0.35,0.40,0.44) (0.00,0.01,0.02) (0.03,0.05,0.07) 4 (0.14,0.17,0.20) (0.39,0.43,0.47) (0.03,0.06,0.11) (0.09,0.12,0.15) 16 (0.15,0.18,0.21) (0.37,0.41,0.45) (0.11,0.17,0.25) (0.08,0.11,0.15) ∞ (0.15,0.18,0.21) (0.36,0.40,0.44) (0.09,0.14,0.21) (0.08,0.11,0.14) 45

Table 6: Variance Decompositions for Selected GDP Components Shocks Horizon Consumption Cons. Dur. Res. Inv. Non-Res. Inv. ǫω 1 (0.28,0.32,0.36) (0.01,0.02,0.03) (0.04,0.05,0.06) (0.13,0.15,0.18) 4 (0.23,0.27,0.31) (0.01,0.02,0.03) (0.03,0.04,0.05) (0.11,0.13,0.16) 16 (0.22,0.26,0.30) (0.01,0.02,0.03) (0.03,0.04,0.05) (0.11,0.14,0.16) ∞ (0.22,0.26,0.30) (0.01,0.02,0.03) (0.03,0.04,0.05) (0.11,0.14,0.16) ǫHG 1 (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) 4 (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) 16 (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) ∞ (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) ǫθ,l 1 (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) 4 (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) 16 (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) ∞ (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) ǫr 1 (0.02,0.02,0.03) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.01,0.01,0.01) 4 (0.02,0.02,0.02) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.01,0.01,0.01) 16 (0.02,0.02,0.02) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.01,0.01,0.01) ∞ (0.01,0.02,0.02) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.01,0.01,0.01) ǫz,k 1 (0.04,0.05,0.06) (0.13,0.16,0.20) (0.02,0.03,0.03) (0.04,0.06,0.07) 4 (0.04,0.05,0.06) (0.11,0.13,0.17) (0.01,0.02,0.02) (0.03,0.04,0.05) 16 (0.04,0.05,0.06) (0.10,0.12,0.16) (0.01,0.01,0.02) (0.03,0.04,0.05) ∞ (0.04,0.05,0.06) (0.10,0.12,0.16) (0.01,0.01,0.02) (0.03,0.04,0.05) ǫz,m 1 (0.08,0.10,0.13) (0.05,0.06,0.07) (0.07,0.09,0.11) (0.04,0.05,0.06) 4 (0.11,0.13,0.16) (0.04,0.05,0.06) (0.04,0.05,0.06) (0.03,0.04,0.05) 16 (0.09,0.12,0.14) (0.04,0.05,0.06) (0.04,0.05,0.06) (0.02,0.03,0.04) ∞ (0.09,0.12,0.14) (0.04,0.05,0.06) (0.03,0.04,0.06) (0.02,0.03,0.04) ǫθ,cbi 1 (0.01,0.01,0.01) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) 4 (0.01,0.01,0.01) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) 16 (0.01,0.01,0.01) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) ∞ (0.01,0.01,0.01) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) ǫθ,kb 1 (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.00,0.01) 4 (0.00,0.00,0.01) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.01,0.01) 16 (0.00,0.00,0.01) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.01,0.01) ∞ (0.00,0.00,0.01) (0.00,0.00,0.00) (0.00,0.00,0.00) (0.00,0.01,0.01) ǫa,r 1 (0.09,0.11,0.14) (0.00,0.01,0.01) (0.71,0.74,0.78) (0.01,0.01,0.01) 4 (0.11,0.13,0.16) (0.01,0.01,0.02) (0.76,0.79,0.83) (0.01,0.01,0.01) 16 (0.10,0.13,0.16) (0.01,0.01,0.02) (0.75,0.79,0.83) (0.01,0.01,0.01) ∞ (0.11,0.13,0.17) (0.01,0.02,0.02) (0.76,0.79,0.83) (0.01,0.01,0.01) ǫa,cd 1 (0.00,0.00,0.00) (0.66,0.70,0.75) (0.00,0.00,0.00) (0.00,0.00,0.00) 4 (0.00,0.00,0.00) (0.67,0.72,0.77) (0.00,0.00,0.00) (0.00,0.00,0.00) 16 (0.00,0.00,0.00) (0.65,0.70,0.76) (0.00,0.00,0.00) (0.00,0.00,0.00) ∞ (0.00,0.00,0.00) (0.64,0.70,0.75) (0.00,0.00,0.00) (0.00,0.00,0.00) ǫa,nr 1 (0.31,0.36,0.40) (0.02,0.03,0.06) (0.06,0.08,0.10) (0.69,0.72,0.74) 4 (0.31,0.35,0.40) (0.02,0.04,0.07) (0.06,0.09,0.12) (0.72,0.75,0.78) 16 (0.34,0.39,0.43) (0.04,0.07,0.11) (0.07,0.10,0.13) (0.73,0.76,0.79) ∞ (0.34,0.38,0.43) (0.04,0.07,0.11) (0.07,0.10,0.13) (0.73,0.76,0.79) 46

Figure 1: Model Overview 47

Figure 2: Impulse Responses: Funds Rate 0 −0.2 −0.4 −0.6 −0.8 −1 −1.2 5 10 15 20 PDG laeR −0.1 −0.2 −0.3 −0.4 −0.5 −0.6 5 10 15 20 noitpmusnoC laeR 0 −0.5 −1 −1.5 5 10 15 20 selbaruD laeR −0.5 −1 −1.5 5 10 15 20 gnisuoH laeR 0 −1 −2 −3 −4 5 10 15 20 tnemtsevnI laeR 0 −0.5 −1 −1.5 5 10 15 20 sruoH 0.8 0.6 0.4 0.2 0 5 10 15 20 sdnuF deF 0 −0.02 −0.04 −0.06 5 10 15 20 48 noitalfnI

Figure 3: Impulse Responses: Risk-premium 0 −0.5 −1 −1.5 −2 5 10 15 20 PDG laeR −0.2 −0.4 −0.6 −0.8 −1 5 10 15 20 noitpmusnoC laeR 0 −0.5 −1 −1.5 −2 −2.5 5 10 15 20 selbaruD laeR −1 −2 −3 −4 5 10 15 20 gnisuoH laeR 0 −2 −4 −6 5 10 15 20 tnemtsevnI laeR 0 −0.5 −1 −1.5 −2 −2.5 5 10 15 20 sruoH −0.1 −0.2 −0.3 −0.4 −0.5 −0.6 5 10 15 20 sdnuF deF −0.05 −0.1 −0.15 −0.2 5 10 15 20 49 noitalfnI

Figure 4: Impulse Responses: Housing Risk-Premium 0 −0.1 −0.2 −0.3 −0.4 5 10 15 20 PDG laeR 1 0.8 0.6 0.4 0.2 0 5 10 15 20 noitpmusnoC laeR 2 1.5 1 0.5 0 5 10 15 20 selbaruD laeR 0 −5 −10 −15 5 10 15 20 gnisuoH laeR 2.5 2 1.5 1 0.5 0 5 10 15 20 tnemtsevnI laeR 0 −0.1 −0.2 −0.3 −0.4 −0.5 −0.6 5 10 15 20 sruoH 0 −0.02 −0.04 −0.06 −0.08 −0.1 5 10 15 20 sdnuF deF 0.04 0.02 0 −0.02 −0.04 −0.06 5 10 15 20 50 noitalfnI

Figure 5: Impulse Responses: Durables Risk-Premium 0 −0.005 −0.01 −0.015 −0.02 5 10 15 20 PDG laeR −3 x 10 3 2 1 0 5 10 15 20 noitpmusnoC laeR 0 −0.05 −0.1 −0.15 −0.2 −0.25 −0.3 5 10 15 20 selbaruD laeR −3 x 10 6 4 2 0 −2 5 10 15 20 gnisuoH laeR 0.02 0.015 0.01 0.005 0 −0.005 −0.01 5 10 15 20 tnemtsevnI laeR 0 −0.005 −0.01 −0.015 −0.02 −0.025 5 10 15 20 sruoH −3 x 10 0 −1 −2 −3 −4 5 10 15 20 sdnuF deF −4 x 10 0 −1 −2 −3 5 10 15 20 51 noitalfnI

Figure 6: Impulse Responses: Capital Risk-Premium 0 −0.5 −1 −1.5 5 10 15 20 PDG laeR 1 0.5 0 5 10 15 20 noitpmusnoC laeR 4 3 2 1 0 −1 5 10 15 20 selbaruD laeR 6 4 2 0 5 10 15 20 gnisuoH laeR 0 −5 −10 −15 5 10 15 20 tnemtsevnI laeR 0 −0.5 −1 −1.5 −2 5 10 15 20 sruoH 0.05 0 −0.05 −0.1 −0.15 −0.2 5 10 15 20 sdnuF deF 0.15 0.1 0.05 0 5 10 15 20 52 noitalfnI

Figure 7: Impulse Responses: Exog. Demand 0.15 0.1 0.05 0 5 10 15 20 PDG laeR 0 −0.02 −0.04 −0.06 5 10 15 20 noitpmusnoC laeR 0 −0.05 −0.1 −0.15 5 10 15 20 selbaruD laeR 0 −0.1 −0.2 −0.3 −0.4 5 10 15 20 gnisuoH laeR 0 −0.05 −0.1 −0.15 −0.2 −0.25 5 10 15 20 tnemtsevnI laeR 0.2 0.15 0.1 0.05 0 5 10 15 20 sruoH 0.04 0.03 0.02 0.01 0 5 10 15 20 sdnuF deF −3 x 10 0 −5 −10 5 10 15 20 53 noitalfnI

Figure 8: Impulse Responses: Non-Invest. Price Markup 0.1 0.05 0 −0.05 −0.1 −0.15 5 10 15 20 PDG laeR 0 −0.05 −0.1 −0.15 −0.2 −0.25 5 10 15 20 noitpmusnoC laeR 0.4 0.3 0.2 0.1 0 5 10 15 20 selbaruD laeR 0 −0.2 −0.4 −0.6 5 10 15 20 gnisuoH laeR 0.8 0.6 0.4 0.2 0 −0.2 5 10 15 20 tnemtsevnI laeR 0.1 0.05 0 −0.05 −0.1 −0.15 −0.2 5 10 15 20 sruoH 0.2 0.15 0.1 0.05 0 5 10 15 20 sdnuF deF 0.4 0.3 0.2 0.1 0 5 10 15 20 54 noitalfnI

Figure 9: Impulse Responses: Invest. Price Markup 0 −0.05 −0.1 −0.15 −0.2 5 10 15 20 PDG laeR 0.1 0.08 0.06 0.04 0.02 0 −0.02 5 10 15 20 noitpmusnoC laeR 0 −0.1 −0.2 −0.3 −0.4 −0.5 5 10 15 20 selbaruD laeR 0.4 0.3 0.2 0.1 0 −0.1 5 10 15 20 gnisuoH laeR 0 −0.5 −1 5 10 15 20 tnemtsevnI laeR 0.05 0 −0.05 −0.1 −0.15 5 10 15 20 sruoH 0.02 0.01 0 −0.01 −0.02 5 10 15 20 sdnuF deF 0.06 0.04 0.02 0 5 10 15 20 55 noitalfnI

Figure 10: Impulse Responses: Wage Markup 0 −0.02 −0.04 −0.06 5 10 15 20 PDG laeR 0 −0.01 −0.02 −0.03 5 10 15 20 noitpmusnoC laeR 0 −0.02 −0.04 −0.06 −0.08 5 10 15 20 selbaruD laeR −0.05 −0.1 −0.15 5 10 15 20 gnisuoH laeR 0 −0.05 −0.1 −0.15 −0.2 5 10 15 20 tnemtsevnI laeR 0 −0.02 −0.04 −0.06 −0.08 −0.1 −0.12 5 10 15 20 sruoH 0.03 0.025 0.02 0.015 0.01 0.005 0 5 10 15 20 sdnuF deF 0.04 0.03 0.02 0.01 0 5 10 15 20 56 noitalfnI

Figure 11: Impulse Responses: Capital Goods Technology 0.55 0.5 0.45 0.4 5 10 15 20 PDG laeR 0.2 0.15 0.1 5 10 15 20 noitpmusnoC laeR 1.15 1.1 1.05 1 0.95 0.9 0.85 5 10 15 20 selbaruD laeR 0.5 0.4 0.3 0.2 5 10 15 20 gnisuoH laeR 1.6 1.4 1.2 1 0.8 5 10 15 20 tnemtsevnI laeR 0.18 0.16 0.14 0.12 0.1 0.08 0.06 5 10 15 20 sruoH 0 −0.01 −0.02 −0.03 5 10 15 20 sdnuF deF 0 −0.005 −0.01 −0.015 −0.02 −0.025 5 10 15 20 57 noitalfnI

Figure 12: Impulse Responses: Overall TFP 1.3 1.2 1.1 1 0.9 0.8 5 10 15 20 PDG laeR 0.8 0.7 0.6 0.5 0.4 0.3 5 10 15 20 noitpmusnoC laeR 1.5 1.4 1.3 1.2 1.1 1 0.9 5 10 15 20 selbaruD laeR 3 2.5 2 1.5 5 10 15 20 gnisuoH laeR 2.5 2 1.5 1 5 10 15 20 tnemtsevnI laeR 0.5 0.4 0.3 0.2 0.1 0 5 10 15 20 sruoH −0.02 −0.04 −0.06 −0.08 −0.1 −0.12 5 10 15 20 sdnuF deF −0.04 −0.06 −0.08 −0.1 −0.12 5 10 15 20 58 noitalfnI

Figure 13: Smoothed Observables and Data 1 0 −1 −2 1985 1990 1995 2000 2005 PDG laeR 1 0.5 0 −0.5 −1 1985 1990 1995 2000 2005 selbaruD−noN laeR 5 0 −5 1985 1990 1995 2000 2005 tnemtsevnI laeR 5 0 −5 1985 1990 1995 2000 2005 selbaruD laeR 5 0 −5 −10 1985 1990 1995 2000 2005 serutidnepxE gnisuoH laeR 2 1 0 −1 1985 1990 1995 2000 2005 egaW laeR 5 0 −5 1985 1990 1995 2000 2005 sruoH etagerggA 1 0 −1 1985 1990 1995 2000 2005 noitalfnI ECP 0.8 0.6 0.4 0.2 0 −0.2 1985 1990 1995 2000 2005 noitalfnI eroC 1 0.5 0 −0.5 1985 1990 1995 2000 2005 59 noitalfnI tnemtsevnI

Figure 14: Innovations to Exogenous Processes 1 0.5 0 −0.5 −1 1985 1990 1995 2000 2005 muimerp−ksiR 4 2 0 −2 1985 1990 1995 2000 2005 dnameD .goxE 10 0 −10 1985 1990 1995 2000 2005 pukraM egaW 0.2 0 −0.2 1985 1990 1995 2000 2005 kcohS etaR sdnuF 2 1 0 −1 −2 1985 1990 1995 2000 2005 ygolonhceT sdooG latipaC 1 0 −1 1985 1990 1995 2000 2005 PFT llarevO 2 1 0 −1 1985 1990 1995 2000 2005 pukraM ecirP .tsevnI−noN 2 1 0 −1 −2 1985 1990 1995 2000 2005 pukraM ecirP .tsevnI 2 1 0 −1 1985 1990 1995 2000 2005 muimerP−ksiR gnisuoH 100 50 0 −50 −100 1985 1990 1995 2000 2005 muimerP−ksiR selbaruD 2 1 0 −1 1985 1990 1995 2000 2005 60 muimerP−ksiR latipaC

Figure 15: Exogenous Drivers 2 1 0 −1 −2 1985 1990 1995 2000 2005 muimerp−ksiR 10 5 0 −5 −10 1985 1990 1995 2000 2005 dnameD .goxE 4 2 0 −2 −4 1985 1990 1995 2000 2005 muimerP−ksiR gnisuoH 50 0 −50 −100 1985 1990 1995 2000 2005 muimerP−ksiR selbaruD 2 1 0 −1 −2 1985 1990 1995 2000 2005 61 muimerP−ksiR latipaC