# Euro-Dollar Real Exchange Rate Dynamics in an Estimated Two-Country Model: What is Important and What is Not

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Euro-Dollar Real Exchange Rate Dynamics in an Estimated Two-Country Model: What is Important and What is Not Pau Rabanal1 Vicente Tuesta International Monetary Fund Banco Central de Reserva del Perú First draft: May 2005 This draft: November 2005 Abstract Central puzzles in international macroeconomics are why fluctuations of the real exchange rate are so volatile with respect to other macroeconomic variables, and the contradiction of efficient risk-sharing. Several theoretical contributions have evaluated alternative forms of pricing under nominal rigidities along with different asset markets structures to explain real exchange dynamics. In this paper, we use a Bayesian approach to estimate a standard two-country New Open Economy Macroeconomics (NOEM) using data for the United States and the Euro Area, and perform model comparisons to study the importance of departing from the law of one price and complete markets assumptions. Our results can be summarized as follows. First, we find that the baseline model does a good job in explaining real exchange rate volatility, but at the cost of implying too high volatility in output and consumption. Second, the introduction of incomplete markets allows the model to better match the volatilities of all real variables. Third, introducing sticky prices in local currency pricing (LCP) improves the fit of the baseline model, but not by as much as by introducing incomplete markets. Finally, we show that monetary shocks have played a minor role in explaining the behavior of the real exchange rate, while both demand and technology shocks have been important. JEL Classifications: F41, C11. Keywords: Real Exchange Rates, Bayesian Estimation, Model Comparison. 1 Corresponding author. 700 19th Street NW. Washington, DC 20431, USA. Email:PRabanal@imf.org. We thank Roberto Chang, Jordi Galí, Gian Maria Milesi-Ferreti, Juan Rubio and seminar participants at George Washington University for helpful suggestions. This paper should not be reported as reflecting the views of the IMF or IMF policy. The opinions expressed in this paper are those of the authors and should not be attributed to the IMF or the Banco Central de Reserva del Perú. Any errors and omissions are our own.

-2- I. INTRODUCTION Most puzzles in international macroeconomics are related to real exchange rate dynamics. Fluctuations in real exchange rates are very large and persistent, when compared to other real variables. In addition, there is clear evidence of lack of consumption risk-sharing across countries, which is at odds with the assumption of complete markets. In order to replicate these features of the data, the New Open Economy Macroeconomics (NOEM) literature has incorporated either nominal rigidities, alternative structures of assets markets, or both. The real exchange rate (qt) between two countries is defined as the ratio of price levels expressed in a common currency.2 When all the components of the price level, namely domestically produced and imported goods, are sticky, it can be possible to explain some empirical features, like the high correlation between nominal and real exchange rates, and real exchange rate volatility. In the literature, pricing of imports goods are assumed to be governed either by Producer Currency Pricing (PCP), where the law of one price holds and there is perfect pass-through; or Local Currency Pricing (LCP), where the pass-through is zero in the short run. Moreover, it is well known that under complete markets, the real exchange rate should be equal to the ratio of the marginal utility of consumption across countries, because it reflects the relative price of foreign goods in terms of domestic goods. For example, assuming separable preferences and log utility, for simplicity, the following relationship should hold as an equilibrium condition: qt = ct -ct*, where ct and ct* are the levels of domestic and foreign consumption. This relationship, which implies a correlation of one between the real exchange rate and the ratio of consumption levels in two countries, does not hold for many bilateral relationships in general.3 For the bilateral euro-U.S. dollar exchange rate in particular, the correlation between these variables (HP-filtered) is -0.17. Hence, models that incorporate complete markets are bound to fail, regardless of the presence of other nominal or real rigidities. A common assumption in the literature is one where agents do not have access to complete markets to insure their wealth against idiosyncratic and country-specific shocks. Another possibility is to introduce preference shocks that affect the marginal utility of consumption, as in Stockman and Tesar (1995). Following this line of research, a recent paper by Chari, Kehoe and McGrattan (2002, hereafter CKM) attempts to explain the volatility and persistence of the real exchange rate by constructing a model with sticky prices and local currency pricing. Their main finding is that monetary shocks and complete markets, along with a high degree of risk aversion and price stickiness of one year are enough to account for real exchange rate volatility, and to less extent for its persistence. However, their model found it difficult to account for the observed negative 2 In linear terms, the real exchange rate is defined as qt=st + pt*-pt, where st is the nominal exchange in units of domestic currency per unit of foreign currency, pt* is the foreign price level, and pt is the domestic price level. 3 See Chari, Kehoe and McGrattan (2002).

-3- correlation between real exchange rates and relative consumption across countries, a fact that they labeled the consumption-real exchange rate anomaly. In addition, CKM show that the most widely used forms of asset market incompleteness and habit persistence do not eliminate the anomaly.4 In this paper, we use a Bayesian approach to estimate and compare two-country NOEM models under different assumptions of imports goods pricing and asset markets structures, thereby testing some of the key implications of CKM. Unlike them, we find that monetary policy shocks have a minor role in explaining real exchange rate volatility, and that both demand and technology shocks have had some importance. Using the Bayes factor to compare between competing alternatives, we find that what turns out to be crucial to explain real exchange rate dynamics and the exchange rate-consumption anomaly is the introduction of incomplete markets with stationary net foreign asset positions. Somewhat surprisingly, we find that in a complete markets set up, the introduction of LCP improves the fit of the model, while when incomplete markets are allowed for, LCP actually lowers the overall fit, implying too high real exchange rate volatility and a lower fit for the correlation between the real exchange rate and the ratio of relative consumptions. The main contributions of the present paper are on the estimation side. First, we focus on the relationship between relative consumptions and the real exchange rate by introducing data on consumption for the two economic areas in the estimation. Second, while our model is quite rich in shocks (we need nine shocks because we try to explain nine variables), we have left aside uncovered interest-rate parity (UIP)-type shocks, which tend to explain a large fraction of real exchange rate variability. We do so because under complete markets these shocks, at least conceptually, should not be included and also because we want to study more carefully the role of “traditional” shocks (technology, demand, monetary and so on) in explaining real exchange rate fluctuations.5 Third, we believe this is the first paper to evaluate the merits of the incomplete markets assumption with stationary net foreign assets in a two-country NOEM model. Last, but not least, we perform an in-sample forecast exercise and find that the preferred model does a good job in forecasting compared to the other NOEM models, but is still far away from the performance of a vector autorregression (VAR) model. 4 Alternative ways to explain this anomaly typically include models with traded and nontraded goods. Selaive and Tuesta (2003a) and Benigno and Thoenissen (2005) have shown that this anomaly can be successfully addressed by models with incomplete markets and nontraded goods, with the traditional Balassa-Samuelson effect and sector- specific productivity shocks. Similarly, Ghironi and Melitz (2005) rely on aggregate productivity shocks and also find that the Balassa-Samuelson effect help to explain the consumption real exchange rate anomaly. Finally, Corsetti, Dedola and Leduc (2004) have shown that distribution services can help to account for the real exchange rate-consumption correlation by lowering the import demand elasticity. 5 Our benchmark model, unlike the International Real Business Cycle (IRBC) literature, always includes nominal rigidities, because we want to evaluate the relative importance of monetary shocks in explaining real exchange rate fluctuations.

-4- The literature on estimating NOEM models in the spirit of CKM and Galí and Monacelli (2005) has grown rapidly, with the adoption of the Bayesian methodology to an open economy setting already used in a closed economy environment.6 For example, Lubik and Schorfheide (2003) estimate small open economy models with data for Australia, New Zealand, Canada and the U.K., focusing on whether the monetary policy rules of those countries have targeted the nominal exchange rate. Justiniano and Preston (2004) also estimate and compare small open economy models with an emphasis on the consequences of introducing imperfect pass-through. Adolfson et al. (2005) estimate a medium-scale (15 variable) small open economy model for the euro area, while Lubik and Schorfheide (2005) and Batini et al. (2005) estimate a small-scale two-country model using U.S. and euro area data. The rest of the paper is organized as follows. In the next section we outline the baseline model, and we describe the LCP and the incomplete markets extensions. In section III we explain the data and in section IV the econometric strategy. The estimation results can be found in section V. First, we present the parameter estimates of the baseline model. Then, we analyze the parameter estimates of all the extensions along with the second moments implied by each model. We select our preferred model based on the comparison of Bayes factors, and analyze its dynamics by studying the impulse response functions. Finally, we evaluate the importance of shocks through variance decompositions. We also compare the forecasting performance of all the DSGE models with respect to VAR models. In section V we conclude. II. THE MODEL In this section we present the stochastic two country New Open Economy Macroeconomics (NOEM) model that we will use to analyze real exchange rate dynamics.7 We first outline a baseline model with complete markets and where the law of one price holds, in the spirit of Clarida, Galí and Gertler (2002), Benigno and Benigno (2003) and Galí and Monacelli (2005). In order to obtain a better fit, we incorporate the following assumptions: home bias, habit formation in consumption, and staggered price setting a la Calvo (1983) with backward looking indexation. Later, we introduce the two main extensions we are interested in, namely incomplete markets and sticky prices of imported goods in local currency. We assume that there are two countries, home and foreign, of equal size. Each country produces a continuum of intermediate goods, indexed by h ∈ [0,1] in the home country and f ∈ [0,1] in the foreign country. Preferences over these goods are of the Dixit-Stiglitz type, implying that producers operate under monopolistic competition, and all goods are internationally tradable. In order to assist the reader with the notation, in Table 1 we present a list with all the variables of 6 Examples of closed economy applications of this methodology are Rabanal and Rubio-Ramírez (2005), and Galí and Rabanal (2004) for the United States, and Smets and Wouters (2003) for the Euro Area. 7 This type of model that has been the workhorse the NOEM literature after Obstfeld and Rogoff (1995). Since the model is now fairly standard, we only outline the main features here, and refer the reader to an appendix available upon request for a full version of the model.

-5- the model. The model contains nine shocks: a world technology shock that has a unit root, and country-specific stationary technology, monetary, demand and preference shocks. All stationary shocks are AR(1), except for the monetary shocks that are iid. Table 1. Variables in the Home and Foreign Countries Home Foreign Quantity Price Quantity Price Consumption goods: Aggregate Ct Pt Ct* Pt* Imports C F ,t PF ,t C H* ,t PH* ,t Domestically Produced C H ,t PH ,t C F* ,t PF*,t Intermediate Goods Imports ct ( f ) pt ( f ) ct* (h) p t* (h) Domestically Produced ct (h) pt (h) ct* ( f ) pt* ( f ) Production: Aggregate (GDP) Y H ,t PH ,t YF*,t PF*,t Intermediate Goods y t (h) pt (h) y t* ( f ) pt* ( f ) Home Foreign Labor Markets: Hours worked Nt N t* Real Wage ωt ω t* Firms’ labor demand N t ( h) N t* ( f ) Terms of Trade Tt Tt* Interest Rates Rt Rt* Bonds Bt Bt* Real Exchange Rate Qt Nominal Exchange Rate St Shocks World Technology At Country Technology Xt X t* Preference Gt Gt* Monetary zt z t* Demand ηt η t*

-6- A. Households In each country there is a continuum of infinitely lived households in the unit interval, who obtain utility from consuming the final good and disutility from supplying hours of labor. It is assumed that consumers have access to complete markets at the country level and at the world level, which implies that consumer’s wealth is insured against country specific and world shocks, and hence all consumers face the same consumption-savings decision.8 In the home country, households’ lifetime utility function is: ∞ N t1+γ E 0 ∑ β t Gt [log(C t − bC t −1 ) − ]. (1) t =0 1+ γ E0 denotes the rational expectations operator using information up to time t=0. β ∈ [0,1] is the discount factor. The utility function displays external habit formation. b ∈ [0,1] denotes the importance of the habit stock, which is last period’s aggregate consumption. γ>0 is inverse elasticity of labor supply with respect to the real wage. Table 2 contains additional variable definitions and functional forms. Ct denotes the consumption of the final good, which is a CES aggregate of consumption bundles of home and foreign goods. The parameter 1 − δ is the fraction of home-produced goods in the consumer basket, and denotes the degree of home bias in consumption. Its analogous in the foreign country is 1 − δ * . The elasticity of subtitution between domestically produced and imported goods in both countries is θ, while the elasticity of substitution between types of intermediate goods is ε>1. In our baseline case, we assume that the law of one price holds for each intermediate good. This implies that PH ,t = St PH* ,t , and PF ,t = St PF*,t . Note, however, that purchasing power parity (a constant real exchange rate) does not necessarily hold because of the presence of home bias in preferences. The home-bias assumption allows to generate real exchange rate dynamics in a model, like this one, with only tradable goods. From previous definitions we can express the real exchange rate as a function of the terms of trade: 1 S P * ⎡ δ * + (1 − δ * )Tt1−θ ⎤ 1−θ Qt = t t = ⎢ 1−θ ⎥ (2) Pt ⎣ (1 − δ ) + δTt ⎦ 8 Baxter and Crucini (1993) have used the same assumption in an IRBC model in order to explain the saving- investment correlation.

-7- Table 2: Definitions and Functional Forms θ θ −1 θ −1 θ −1 ⎡ 1 1 ⎤ C t ≡ ⎢(1 − δ ) θ (C H ,t )θ + δ θ (C F , t )θ ⎥ ⎣⎢ ⎦⎥ Consumption θ θ −1 θ −1 θ −1 ⎡ 1 1 ⎤ C t* ≡ ⎢(δ * ) θ (C H* ,t )θ + (1 − δ * θ ) (C * ) θ F ,t ⎥ ⎢⎣ ⎥⎦ ε ε ε −1 ε −1 ⎧ 1 ⎫ ε −1 ⎧ ⎫ ε −1 ∫ [c ] ⎪ ⎪ ⎪ ⎪ ≡ ⎨ [ct (h)] dh ⎬ , C H* ,t ≡ ⎨ 1 ∫ ε * ε C H ,t t ( h) dh ⎬ ⎪⎩ 0 ⎪⎭ ⎪⎩ 0 ⎪⎭ Consumption components ε ε ε −1 ε −1 ⎧ 1 ⎫ ε −1 ⎧ ⎫ ε −1 ∫ [c ] ⎪ ⎪ ⎪ ⎪ ≡ ⎨ [ct ( f )] df ⎬ , C F* ,t ≡ ⎨ 1 ∫ ε * ε C F ,t t ( f) df ⎬ ⎪⎩ 0 ⎪⎭ ⎪⎩ 0 ⎪⎭ 1 [ Pt ≡ (1 − δ )( PH ,t ) 1−θ + δ ( PF ,t ) 1−θ ] 1−θ , Consumer Price Indices 1 ≡ [δ ] 1−θ Pt* * ( PH* ,t )1−θ + (1 − δ * )( PF*,t )1−θ . 1 1 ∫[ ] 1−ε 1−ε ⎧ ⎫ 1−ε ⎧ ⎫ 1−ε ∫ [ p ( h)] 1 1 PH ,t ≡⎨ t dh ⎬ , PH* ,t ≡ ⎨ pt* (h) dh ⎬ ⎩ 0 ⎭ ⎩ 0 ⎭ Price subindices 1 1 ∫ [p ] 1−ε 1−ε ⎧ 1 ⎫ 1−ε ⎧ ⎫ 1−ε ≡ ⎨ [ pt ( f )] df ⎬ , PF*,t ≡ ⎨ 1 ∫ * PF ,t t ( f) df ⎬ ⎩0 ⎭ ⎩ 0 ⎭ Tt = PF ,t / PH ,t , Tt* = PH* ,t / PF*,t Terms of Trade S t Pt* Real Exchange Rate Qt = Pt y t (h) = At X t N t (h) , y t* ( f ) = At X t* N t* ( f ) Production Functions World Technology Shock log( At ) = Γ + log( At −1 ) + ε ta * log( X t ) = ρ x log( X t −1 ) + ε tx , log( X t* ) = ρ x* log( X t*−1 ) + ε tx Country Tech. Shocks * Preference Shocks log(Gt ) = ρ g log(Gt −1 ) + ε tg , log(Gt* ) = ρ g* log(Gt*−1 ) + ε tg

-8- B. Asset Market Structure, the Budget Constraint, and the Consumer’s Optimizing Conditions We model complete markets by assuming that households have access to a complete set of state contingent nominal claims which are traded domestically and internationally. We represent the asset structure by assuming a complete set of contingent one-period nominal bonds denominated in home currency. 9 Hence, households in the home country maximize their utility (1) subject to the following budget constraint: Et {ξ t ,t +1 Bt +1 } − Bt 1 Ct = ωt N t + + ∫ Π t (h)dh, (3) Pt 0 where Bt +1 denotes nominal state-contigent payoffs of the portfolio purchased in domestic currency at t , and ξ t ,t +1 is the stochastic discount factor.10 The real wage is deflated by the country’s CPI. The last term of the right hand side of the previous expression denotes the profits from the monopolistically competitive intermediate goods producers firms, which are ultimately owned by households in each country. The first order conditions for labor supply and consumption/savings decisions are as follows: ωt = (Ct − bCt −1 ) N tγ , (4) and Gt +1 (C t − bC t −1 ) Pt β = ξ t ,t +1 . (5) Gt (C t +1 − bC t ) Pt +1 By taking expectations to the above equation across all possible states, and by using the fact that Et {ξ t ,t +1 } = 1 / Rt we can obtain the traditional Euler equation in consumption. Moreover combining equation (5) with the intertemporal efficiency condition in the foreign country we obtain that under complete markets the ratio of marginal utilities of the two countries is equal to: 9 Given these assumptions, it is not necessary to characterize the current account dynamics in order to determine the equilibrium allocations, and the currency denomination of the bonds is irrelevant. 10 ξ t ,t +1 is a price of one unit of nominal consumption of time t+1, expressed in units of nominal consumption at t, contingent on the state at t+1 being st+1, given any state st in t. The complete market assumptions implies that there exists a unique discount factor with the property that the price in period t of the portfolio with random value Bt +1 is E t {ξ t ,t +1 Bt +1 } .

-9- (Ct − bC t −1 ) Gt* Qt = ν , (6) (C t* − b *C t*−1 ) Gt where ν is a constant that depends on initial conditions (see CKM, and Galí and Monacelli, 2005). The risk-sharing condition (6) differs with respect to the one in CKM because of the presence of both preference shocks and habit persistence. C. Intermediate Goods Producers and Pricesetting In each country, there is a continuum of intermediate goods producers, each producing a type of good that is an imperfect substitute of the others. As shown in Table 2, the production function is linear in the labor input, and has two technology shocks. The first one is a world technology shock, that affects the two countries that same way: it has a unit root, as in Galí and Rabanal (2004) and Ireland (2004), and it implies that real variables in both countries grow at a rate Γ. In addition, there is a country-specific technology shock that evolves as an AR(1) process. Firms face a modified Calvo (1983)-type restriction when setting their prices. When they receive the Calvo-type signal, which arrives with probability 1 − α in the home country, firms reoptimize their price. When they do not receive that signal, a fraction τ of intermediate goods producers index their price to last period’s inflation rate, and a fraction 1 − τ indexes their price to the steady-state inflation rate. This assumption is needed to incorporate trend inflation, as in Yun (1996). The equivalent parameters in the foreign country are 1 − α * and τ * . Cost minimization by firms implies that the real marginal cost of production is ω t /( At X t ) . Since the real marginal cost depends only on aggregate variables, it is the same for all firms in each country. The overall demand for an intermediate good produced in h comes from optimal choices by consumers at home and abroad: −ε −θ ⎛ p ( h) ⎞ ⎛ PH ,t ⎞ Dt (h) = ct (h) + c (h) = ⎜⎜ t * t P ⎟ ⎟ ⎜⎜ ⎟⎟ [(1 − δ )C t ] + δ *C t*Qtθ . ⎝ H ,t ⎠ ⎝ Pt ⎠ Hence, whenever intermediate-goods producers are allowed to reset their price, they maximize the following profit function, which discounts future profits by the probability of not being able to reset prices optimally every period: ∞ ⎧ ⎡ p t ,t + k ( h ) ωt +k ⎤ ⎫ Max Et ∑ α k ξ t ,t + k ⎨⎢ − MCt + k ⎥ Dt ,t + k (h)⎬. (7) ⎩⎣ Pt + k At + k X t + k ⎦ pt ( h ) k =0 ⎭ where pt ,t + k (h) is the price prevailing at t+k assuming that the firm last reoptimized at time t, and whose evolution will depend on whether the firm indexes its price to last period’s inflation

- 10 - rate or to the steady-state rate of inflation, Dt ,t + k (h) the demand associated to that price, and ξ t ,t + k is the k periods ahead stochastic discount factor. The evolution of the aggregate consumption bundle price produced in the home country is: τ 1−ε ⎡ ⎛ P ⎞ 1−τ ⎤ PH1−,tε = (1 − α )( PˆH ,t )1−ε + α ⎢ PH ,t −1 ⎜⎜ ⎟ ΠH ⎥ H , t −1 ⎟ . (8) ⎢ ⎝ PH ,t − 2 ⎠ ⎥ ⎣ ⎦ where PˆH ,t is the optimal price set by firms in a symmetric equilibrium. D. Closing the Model In order to close the model, we impose market clearing conditions for all home and foreign intermediate goods. For each individual good, market clearing requires y t (h) = ct (h) + ct* (h) for all h ∈ [0,1]. Defining aggregate real GDP as YH ,t = ⎡ ∫ pt (h) y t (h)dh⎤ / PH ,t , the following 1 ⎢⎣ 0 ⎥⎦ market clearing condition holds at the home-country level: −θ ⎛ PH ,t ⎞ YH , t [ = (1 − δ )C t + δ C Qt .⎜⎜ * * t θ ] ⎟⎟ + ηt (9) ⎝ Pt ⎠ The analogous expressions for the foreign country are, y t* ( f ) = ct ( f ) + ct* ( f ) , for all f ∈ [0,1] and for aggregate foreign real GDP: −θ * θ ⎛ H ,t ⎞ * [ YF ,t = δC t + (1 − δ )C t Qt .⎜⎜ * P ] ⎟⎟ + η t* (10) ⎝ Pt ⎠ We also introduce an exogenous demand shock for each country (η t ,η t* ) that be interpreted as government purchases, and/or trade with third countries that are not part of in the model. E. Symmetric Equilibrium Since we have assumed a world-wide technology shock that grows at a rate Г, output, consumption, real wages, and the level of exogenous demand in the two economies grow at that same rate. In order to render these variables stationary, we divide them by the level of world technology At . Real marginal costs, hours, inflation, interest rates, the real exchange rate and the terms of trade are stationary.

- 11 - F. Dynamics We obtain the model’s dynamics by taking a linear approximation to the steady state values with zero inflation. We impose a symmetric home bias, such that δ = δ * . We denote by lower case variables percent deviations from steady state values. Moreover, variables with a tilde have been normalized by the level of technology to render them stationary. For instance, ~ ~ ~ ~ c~t = (C t − C ) / C , where C t = C t / At . The relationship between the transformed variables in the model (normalized by the level of technology) and the first-differenced variables is as follows: c~t = c~t −1 + ∆ct − ε ta , ~ yt = ~ y t −1 + ∆y t − ε ta , c~t* = c~t*−1 + ∆ct* − ε ta , and ~ y t* = ~ y t*−1 + ∆y t* − ε ta . where ∆ denotes the first difference operator. These relationships are used in the estimation strategy, since we include first-differenced real variables in the set of observable variables. In this subsection, we focus the discussion on the equations that influence the behavior of the real exchange rate, and that will change once we assume imperfect pass-through and incomplete markets. In Table 3, we present the rest of the equations of the model, which are fairly standard given our assumptions. The only exception are the Taylor rules, which modify the original formulation by reacting to output growth instead of the output gap, incorporating interest rate smoothing, and an iid monetary shock. Table 3: Linearized equations Euler equations b∆ct = −(1 + Γ − b)(rt − Et ∆pt +1 ) + (1 + Γ) Et ∆ct +1 + (1 + Γ − b)(1 − ρ g ) g t b * ∆ct* = −(1 + Γ − b * )(rt* − Et ∆pt*+1 ) + (1 + Γ) Et ∆ct*+1 + (1 + Γ − b * )(1 − ρ g* ) g t* Labor supply ⎡ (1 + Γ)c~t − bc~t −1 + bε ta ⎤ ω~t = γnt + ⎢ ⎥ ⎣ 1+ Γ − b ⎦ ⎡ (1 + Γ)c~t* − b * c~t*−1 + b *ε ta ⎤ ω~t* = γnt* + ⎢ ⎥ ⎣ 1 + Γ − b* ⎦ Goods market ~ ⎡ 2δ (1 − δ ) ⎤ clearing y H ,t = θ ⎢ ⎥ qt + (1 − δ )c~t + δc~t* + η t ⎣ 1 − 2δ ⎦ ~ ⎡ 2δ (1 − δ ) ⎤ y F* ,t = −θ ⎢ ⎥ qt + δc~t + (1 − δ )c~t* + η t* . ⎣ 1 − 2δ ⎦ Production ~ y H ,t = xt + nt functions ~ y * = x* + n* F ,t t t Taylor rules rt = ρ r rt −1 + (1 − ρ r )γ p ∆p H ,t + (1 − ρ r )γ y ∆y H ,t + z t ( ) ( ) rt* = ρ r* rt*−1 + 1 − ρ r* γ *p ∆p F* ,t + 1 − ρ r* γ *y ∆y F* ,t + z t* Terms of trade ∆t t = ∆st + ∆p F* ,t − ∆p H ,t

- 12 - The risk sharing condition delivers the following relationship between consumption in the two countries, the preference shocks, and the real exchange rate: ⎡ (1 + Γ)c~t − bc~t −1 ⎤ ⎡ (1 + Γ)c~t − b c~t −1 ⎤ * * * ⎛ 1+ Γ 1+ Γ ⎞ a qt = ⎢ ⎥ − ⎢ ( ⎥ − gt − gt + ⎜ * ) − ε . * ⎟ t (11) ⎣ 1+ Γ − b 1+ Γ − b ⎝1+ Γ − b 1+ Γ − b ⎠ * ⎦ ⎣ ⎦ As in CKM, the real exchange rate depends on the ratio of marginal utilities of consumption, which in our case include the habit stock in each country, and the preference shocks. Note that the innovation to world growth enters as long as the effect on the ratio of marginal utilities is different in the two countries, due to differences in the habit formation parameters. Domestic (GDP deflator) inflation dynamics in each country are given by: ∆p H ,t = γ b ∆p Ht −1 + γ f Et ∆p Ht +1 + κ [ω~t − xt + δt t )], (12) ∆p F* ,t = γ b* ∆p F* ,t −1 + γ *f Et ∆p F* ,t +1 + κ * [ω~t* − xt* − δ *t t ]. (13) where for the home country, the backward and forward looking components are γ b ≡ τ /(1 + βτ ) , γ f ≡ β /(1 + βτ ) , and the slope is given by κ ≡ (1 − αβ )(1 − α ) /[(1 + βτ )α ] . Similar expressions with asterisks deliver the coefficients γ b* , γ *f , and κ * . Domestic inflation is determined by unit labor costs (the real wage), productivity shocks, and the terms of trade. This last variable appears because real wages are deflated by the CPI: an increase in imports goods prices will cause real wages to drop, and households will demand higher wages. As a result, domestic inflation will also increase. When the law of one price holds, the real exchange rate and the terms of trade are linked as follows: qt = (1 − 2δ )t t . The symmetric home bias assumption implies a positive comovement between the real exchange rate and the terms of trade which is consistent with the data. Thus, in this model, the real exchange rate inherits the properties of the terms of trade. With no home bias (δ=1/2), the real exchange rate is constant and purchasing power parity holds. The degree of home bias is crucial to account for the volatility of the real exchange rate: the larger the degree of home bias (smaller δ), the larger the volatility of the real exchange rate.11 Finally, the CPI inflation rates are a combination of domestic inflation and imported goods. Since prices are set in the producer currency, and the law of one price holds, the nominal exchange rate has a direct inflationary impact on CPI inflation: 11 In a model with non-tradable goods this proportionality is broken down so that the real exchange rate will depend upon to the relative price of tradable to non tradable goods across countries.

- 13 - ∆pt = (1 − δ )∆p H ,t + δ∆p F* ,t + δ∆st (14) and ∆pt* = δ * ∆p H* ,t − δ * ∆st + (1 − δ * )∆p F* ,t . (15) III. EXTENSIONS TO THE BASELINE MODEL A. Incomplete Markets with Stationary Net Foreign Assets In this section we introduce the incomplete markets assumption a simple and tractable way. We assume that home-country households are able to trade in two nominal riskless bonds denominated in domestic and foreign currency, respectively. These bonds are issued by home- country residents in the domestic and foreign currency to finance their consumption. Home- country households face a cost of undertaking positions in the foreign bonds market.12 For simplicity, we further assume that foreign residents can only allocate their wealth in bonds denominated in foreign currency. In each country, firms are still assumed to be completely owned by domestic residents, and profits are distributed equally across households. The real budget constraint of home-country households is now given by: Bt S t Bt* Bt −1 S t Bt*−1 1 Ct + Pt R t + ⎛ S B * ⎞ = ω t N t + P + P + ∫ 0 Π t (h)dh, (3’) Pt Rt*φ ⎜⎜ t t ⎟⎟ t t ⎝ Pt ⎠ where the φ (.) function depends on the real holdings of the foreign assets in the entire economy, and therefore is taken as given by individual households.13 We further assume that the initial level of wealth is the same across households belonging to the same country. This assumption combined with the fact that households within a country equally share the porfits of intermediate goods producers, implies that within a country all households face the same budget constraint. In their consumption decisions, they will choose the same path of consumption. 12 This cost allows to achieve stationarity in the net foreign asset position. See Schmitt-Grohe and Uribe (2001) and Kollman (2002) for applications in small open economies, and Benigno (2001) and Selaive and Tuesta (2003a) for applications in two-country models. Heathcote and Perri (2002) have used the same transaction cost in a two- country IRBC model. 13 In order to achieve stationarity φ (.) has to be differentiable and decreasing in a neighborhood of zero. We further assume that φ (.) equals zero when Bt* =0.

- 14 - Dynamics Under incomplete markets, the net foreign asset (NFA) position for the home country consists of the holding of foreign bonds (since domestic bonds are in net supply in the symmetric equilibrium). By definition, the NFA position of the foreign country equals the stock of bonds outstanding with the home country. The risk sharing condition holds in expected first difference terms and depends on the NFA position and preference shocks: ⎡ (1 + Γ) Et ∆ct +1 − b∆ct ⎤ ⎡ (1 + Γ) Et ∆ct +1 − b ∆ct ⎤ * * * Et q t +1 − q t = ⎢ − ⎥ + (1 − ρ g ) g t − (1 − ρ g ) g t + χbt * * * 1 + Γ − b ⎥ ⎢ 1 + Γ − b * ⎣ ⎦ ⎣ ⎦ (11’) ⎛SB ⎞ * where χ = −φ ' (0 )YH and bt* = ⎜⎜ t t ⎟⎟ , which substitutes equation (11) in section II.F. ⎝ YH ,t Pt ⎠ The net foreign asset position becomes a state variable: its evolution depends on the stock of previous debt and on the trade deficit (or surplus): ⎡ 2θ (1 − δ ) − 1⎤ β bt* = bt*−1 + δ ⎢ 1 − 2 δ ⎥ ( ) qt − δ c~t − c~t* . (16) ⎣ ⎦ Note that the effect of the real exchange rate on the NFA critically depends on the size of the elasticity of substitution: with a low elasticity, a real depreciation will imply that volumes increase less than prices decline, and hence the value of net exports declines after a real devaluation. B. Local Currency Pricing by Intermediate Goods Producers We assume price stickiness in each country’s imports prices in terms of local currency. Each firm chooses a price for the domestic market and a price for the foreign market under the same conditions of the modified Calvo lottery with indexation as above. This assumption allows to generate deviations from the law of one price at the border, and nominal exchange rate movements generate ex-post deviations from the law of one price.14 Importantly, under the assumption of local currency pricing, even without home bias it is possible to generate real exchange rate fluctuations. The overall demand (domestic and exports) for an intermediate good produced in h, is given by: 14 Monacelli (2005) assumes that it is retail importers those are subject to sticky prices, rather than the exporting firms in the country of origin. In his model, the law of one price holds at the border, but the pass-through is slow.

- 15 - −ε −θ −ε −θ ⎛ p ( h) ⎞ ⎛ PH ,t ⎞ ⎛ p * ( h) ⎞ ⎛ PH* ,t ⎞ ct (h) = (1 − δ )⎜⎜ t ⎟ ⎜⎜ ⎟⎟ C t and ct* (h) = δ ⎜ t * ⎟ ⎜ ⎟ C t* . P ⎟ ⎜ P ⎟ ⎜ P* ⎟ ⎝ H , t ⎠ ⎝ Pt ⎠ ⎝ H ,t ⎠ ⎝ t ⎠ Hence, whenever domestic intermediate-goods producers are allowed to reset their prices in the home and the foreign country, they maximize the following profit function: ∞ Max Et ∑ α ξ t ,t + k ⎨ k [ ] ⎧⎪ [ pt ,t + k (h) − ω t /( At X t )]ct ,t + k (h) + pt*,t + k (h) S t + k − ω t /( At X t ) ct*,t + k (h) ⎫⎪ ⎬. pt ( h ), pt* ( h ) k =0 ⎪⎩ Pt + k ⎪⎭ where pt ,t + k (h) and pt*,t + k (h) are prices of the home good set at home and abroad prevailing at t+k assuming that the firm last reoptimized at time t, and whose evolution will depend on whether the firm indexes to last period’s inflation rate (a fraction τ of firms) or to the steady- state rate of inflation (a fraction 1- τ of firms) when it is not allowed to reoptimize. ct ,t + k (h) and ct*,t + k (h) are the associated demands for good h in each country. To obtain the log-linear dynamics, we first need to redefine the terms of trade: t t ≡ p F ,t − p H ,t , and t t* ≡ p H* ,t − p F* ,t . These ratios represent the relative price of imported goods in terms of the domestically produced goods expressed in local currency, for each country.15 Dynamics The following new equations arise with respect to the baseline (PCP) case. The inflation equations for home-produced goods are: ∆p H ,t = γ b ∆p Ht −1 + γ f Et ∆p Ht +1 + κ [ω~t − xt + δt t )], (12’) ∆p H* ,t = γ b ∆p H* ,t −1 + γ f Et ∆p H* ,t +1 + κ [ω~t − xt − (1 − δ )t t* − qt )], (12b’) ∆p F* ,t = γ b* ∆p F* ,t −1 + γ *f Et ∆p F* ,t +1 + κ * [ω~t* − xt* + δt t* ], (13’) ∆p F ,t = γ b* ∆p F ,t −1 + γ *f Et ∆p F ,t +1 + κ * [ω~t* − xt* − (1 − δ )t t + q t ], (13b’) 15 Note that if the law of one price holds, t t = −t t* , but now it is no longer the case.

- 16 - Similarly to the baseline case, real wages are deflated by the CPI which causes the terms of trade for each country, as well as the real exchange rate, to matter in the determination of unit labor costs and of domestic inflation. The CPI inflation rates under LCP do not include the nominal exchange rate as a direct determinant of imported goods inflation, because the pass-through is low and imports prices are sticky in domestic currency: ∆pt = (1 − δ )∆p H ,t + δ∆p F ,t (14’) and ∆pt* = δ∆p H* ,t + (1 − δ )∆p F* ,t (15’) which substitute equations (12)-(15). In addition, the market-clearing conditions in Table 3 become: y% H ,t = (1 − δ ) θδ ( tt − tt* ) + (1 − δ )c%t + δ c%t* + ηt , and y% F* ,t = − (1 − δ ) θδ ( tt − tt* ) + δ c%t + (1 − δ ) c%t* + ηt* . C. Incomplete Markets and Sticky Prices in Local Currency Pricing Under incomplete markets and local currency pricing, the equations of the model are given by those in section II.F, Table 3, and modified by those in section III.B. The additional change is that while the behavior of the real exchange rate is the same than under incomplete markets (equation 11’ in section III.A), the NFA dynamics is given by: βbt* = bt*−1 + δ (1 − δ )(θ − 1)(t t − t t* ) + δqt − δ (c~t − c~t* ). (16’) which substitutes (16) in section III.A. IV. ESTIMATION AND MODEL COMPARISON In this section, we describe the data for the United States and the euro area. We also explain the Bayesian methodology used to estimate the parameters of each model, and to compare the different versions of the NOEM model. A. Data Data sources for the United States are as follows (pnemonics are in parenthesis as they appear in the Haver USECON database): we use quarterly real GDP (GDPH), the GDP deflator (DGDP), real consumption (CH), and the 3-month T-bill interest rate (FTB3) as the relevant short-run interest rate. Since we want to express real variables in per capita terms, we divide real GDP and consumption by total population of 16 years and over (LN16).

- 17 - Data for the Euro area as a whole come from the Fagan, Henry and Maestre (2001) dataset, with pnemonics in parenthesis as they appear in this dataset. This dataset is a synthetic dataset constructed by the Econometric Modeling Unit at the European Central Bank, and should not be viewed as an “official” series. We extract from that database real consumption (PCR), real GDP (YER), the GDP deflator (YED), and short-term interest rates (STN). The euro zone population series is taken from Eurostat. Since it consists of annual data, we transform it to quarterly frequency by using linear interpolation. The convention we adopt is that the home country is the euro area, and the foreign country is the United States, such that the real exchange rate consists of the nominal exchange rate in euros per U.S. dollar, converted to the real exchange rate index by multiplying it by the U.S. CPI and dividing it by the Euro area CPI. The “synthetic” euro/U.S. dollar exchange rate prior to the launch of the euro in 1999 also comes from Eurostat, while the U.S. CPI comes from the Haver USECON database (PCU) and the euro area CPI comes from the Fagan, Henry and Maestre data base (HICP). Table 4: Properties of the Data for the United States and the Euro Area Raw Data, Quarterly Growth Rates Consumption Output Consumption Output Real Exch. Euro Euro USA USA Rate Mean 0.47 0.47 0.53 0.48 -0.14 Std. Dev. 0.57 0.58 0.67 0.85 4.59 Raw Data, Quarterly Rates Interest Rate Inflation Interest Rate Inflation Euro Euro USA USA Mean 2.08 1.44 1.59 1.00 Std. Dev. 0.83 0.93 0.73 0.67 First Autocorr. 0.96 0.89 0.94 0.90 HP-Filtered Data Consumption Output Consumption Output Real Exch. Euro Euro USA USA Rate Std. Dev. 0.91 1.01 1.28 1.58 7.83 Corr. with RER -0.26 -0.06 -0.02 -0.08 1.00 First Autocorr. 0.84 0.86 0.87 0.87 0.83 Relative Consumption Output Relative Outputs, Euro, USA Euro, USA Cons., RER RER Other Correlations 0.33 0.47 -0.17 0.04 Note: Relative variables are the ratio between the euro area variable and its US counterpart. Our sample period goes from 1973:1 to 2003:4, at quarterly frequency, which is when the euro area data set ends. To compute per capita output and consumption growth rates, and inflation, we take natural logs and first differences of per capita output and consumption, and the GDP

- 18 - deflator respectively. We divide the short term interest rate by four to obtain its quarterly equivalent. We also take natural logs and first differences of the euro/dollar real exchange rate. Table 4 presents some relevant statistics. Interestingly, the raw data show that per capita output growth rates in the United States and the euro area are not that different (0.48 versus 0.47), while per capita consumption and output in the euro area grow at the same rate (0.47). Consumption growth in the U.S. displays a higher sample mean growth rate (0.53) which is not surprising given current recent trends. Interestingly, growth rates in the euro area are less volatile than in the U.S. The real exchange rate displays a small appreciating trend mean during the sample period, and is much more volatile than any other series. Out of the HP-filtered statistics, it is important to highlight the well-known fact that the real exchange rate is much more volatile than any other series: the bilateral real exchange rate has a standard deviation of 7.83, while output and consumption in the U.S. has a standard deviation of 1.58 and 1.28 respectively. Output and consumption in the euro area are less volatile, with a standard deviation of about 1 percent. Interest rates and inflation rates display high persistence, and so do all real variables when they are HP-filtered. Interestingly, only consumption in the euro area displays some non-zero correlation with the real exchange rate, which is -0.26. The correlation of output in Europe, and output and consumption in the U.S. with the real exchange rate is basically zero. Finally, it is worth noting that the correlation between consumptions is smaller than between outputs (0.33 versus 0.47), although the size of the two correlations are smaller than those obtained using shorter sample periods (ending in the early 1990s), as in Backus, Kehoe and Kydland (1992).16 The correlation of relative output with the real exchange rate is fairly small, while the correlation between the real exchange rate and relative consumptions across countries is negative (-0.17) which is at odds with efficient risk-sharing.17 B. Bayesian Estimation of the Model’s Parameters According to Bayes’ rule, the posterior distribution of the parameters is proportional to the product of the prior distribution of the parameters and the likelihood function of the data. An appealing feature of the Bayesian approach is that additional information about the model’s parameters (i.e. micro-data evidence, features of the first moments of the data) can be introduced via the prior distribution. To implement the Bayesian estimation method, we need to be able to evaluate numerically the prior and the likelihood function. The likelihood function is evaluated using the state-space representation of the law of motion of the model, and the Kalman filter. Then, we use the 16 Heathcote and Perri (2004) document that in recent years the U.S. economy has become less correlated with the rest of the world. 17 All the facts related to the U.S. economy are very similar to the ones presented in CKM.

- 19 - Metropolis-Hastings algorithm to obtain random draws from the posterior distribution, from which we obtain the relevant moments of the posterior distribution of the parameters.18 Let ψ denote the vector of parameters that describe preferences, technology, the monetary policy rules, and the shocks in the two countries of the model. The vector of observable variables consists of xt = {∆y t , ∆ct , rt , ∆p H ,t , ∆y t* , ∆ct* , rt* , ∆p F* ,t , ∆qt }' . The assumption of a world technology shock with a unit root makes the real variables stationary in the model in first differences. Hence, we use consumption and output growth per country, which are stationary in the data and in the model. We first-difference the real exchange rate, while inflation and the nominal interest rate in each country enter in levels.19 We express all variables as deviations from their sample mean. We denote by L({xt }Tt=1 | ψ) the likelihood function of {xt }Tt=1 . Priors Table 5 shows the prior distributions for the model’s parameters, that we denote by Π(ψ). For the estimation, we decide to fix only two parameters. The first one is the steady-state growth rate of the economy. Based on the evidence presented in section III.A, we set Γ=0.5 percent, which implies that the world growth rate of per capita variables is about 2 percent per year. In order to match a real interest rate in the steady state of about 4 percent per year, we set the discount factor to β=0.995. For reasonable parameterizations of these two variables the parameter estimates do not change significantly. For the remainder of parameters, gamma distributions are used as priors when non-negativity constraints are necessary, and uniform priors when we are mainly interested in estimating fractions or probabilities. Normal distributions are used when more informative priors seem to be necessary. Unlike other two-country model papers (i.e. Lubik and Schorfheide, 2005; and CKM), we do not impose that the parameter values be the same in the two countries. However, we do use the same prior distributions for parameters across countries. We use normal distributions for the coefficients of habit formation and inverse elasticity of labor supply with respect to the real wage, centered at conventional values in the literature (0.7 and 1, respectively). We truncate the habit formation parameter to be between 0 and 1, which on the upper bound it would be six standard deviations away from the prior mean. We assume that the average duration of price contracts has a prior mean of 3 in the two countries, following empirical evidence reported in Taylor (1999). In this case, a gamma distribution is used.20 The prior on the fraction of price setters that follow a backward looking indexation rule is less informative and takes the form of a uniform distribution between zero and one. 18 See the Appendix for some details on the estimation. Lubik and Schorfheide (2003, 2005) also provide useful details on the estimation procedure. 19 Hence, we avoid the discussion on which detrending method (linear, quadratic or HP-filter) to use. 20 To keep the probability of the Calvo lottery between 0 and 1, the prior distribution is specified as average duration of price contracts minus one: D=1/(1-α)-1. The shape of the prior is not much different than assuming a beta distribution for α.

- 20 - Table 5: Prior Distributions of the Model’s Parameters Parameter Distribution Mean Std. Dev. Habit formation b , b* Normal 0.70 0.05 Labor supply γ ,γ * Normal 1.00 0.25 Average Price Duration (1 − α ) −1 , (1 − α * ) −1 Gamma 3.00 1.42 Indexation τ ,τ * Uniform(0,1) 0.50 0.29 Fraction of imported goods δ Normal 0.20 0.03 Elasticity of substitution between θ Normal 1.50 0.25 home and foreign goods Elasticity of the real exchange rate χ Gamma 0.02 0.014 to the NFA position Taylor rule: inflation γ p , γ *p Normal 1.50 0.25 Taylor rule: output growth γy, γ * Normal 1.00 0.20 y Taylor rule: smoothing ρ r , ρ r* Uniform(0,1) 0.50 0.29 AR coefficents of shocks ρx , ρ * ρg , x, Uniform(0,0.96) 0.48 0.28 ρ g* , ρη , ρη* Std. Dev. technology shocks σ x , σ x* , σ a Gamma 0.007 0.003 Std. Dev. preference shocks σ g ,σ * Gamma 0.010 0.005 g Std. Dev. monetary shocks σ z ,σ * Gamma 0.004 0.002 z Std. Dev. demand shocks ση ,ση * Gamma 0.010 0.005 The parameters that incorporate the open economy features of the model take the following distributions. The parameter δ, which captures the implied home bias, has a prior distribution with mean 0.2 and standard deviation 0.03, implies a smaller home-bias than suggested by Heathcote and Perri (2002) and CKM. The elasticity of substitution between home and foreign goods (θ) is source of controversy. We center it at a value of 1.5 as suggested by CKM, but with a large enough standard deviation to accommodate other feasible parameters, even those below one.21 Finally, the parameter χ, that measures the elasticity of the risk premium with respect to the net foreign asset position, is assumed to have a gamma distribution with mean 0.02 and standard deviation 0.014, following the evidence in Selaive and Tuesta (2003a and 2003b). 21 Trade studies typically find values for the elasticity of import demand to respect to price (relative to the overall domestic consumption basket) in the neighborhood of 5 to 6, see Trefler and Lai (1999). Most of the NOEM models consider values of 1 for this elasticity which implies Cobb-Douglas preferences in aggregate consumption.

- 21 - For the coefficients of the interest rate rule, we center the coefficients to the values suggested by Rabanal (2004b) who estimates rules with output growth for the United States. Hence, γ p has a prior mean of 1.5, and γ y has a prior mean of 1. The same values are used for the monetary policy rule in the Euro area, and we use uniform priors for the autoregressive processes between zero and one. We also truncate the prior distributions of the Taylor rule coefficients such that the models deliver a unique, stable solution. Regarding the priors for the shocks of the model, we use also uniform priors on the autoregressive coefficients of the six AR(1) shocks. We truncate the upper bound of the distribution to 0.96, because we want to examine how far can the models go in endogenously replicating persistence. We choose gamma distributions for the priors on the standard deviations of the shocks, to stay in positive numbers. The prior means are chosen to match previous studies. For instance, the prior mean for the standard deviation of all technology shocks is set to 0.007, close to the values suggested by Backus, Kehoe and Kydland (1992), while the prior mean of the standard deviation of the monetary shocks comes from estimating the monetary policy rules using OLS. The standard deviation of the prior are set to reflect the uncertainty over these parameters. Drawing from the Posterior and Model Comparison We implement the Metropolis-Hastings algorithm to draw from the posterior. The results are based on 250,000 draws from the posterior distribution. The definition of the marginal likelihood for each model is as follows: L({xt }Tt=1 ) = ∫ L({xt }Tt=1 | ψ )Π (ψ )dψ (17) ψ ∈Ψ The marginal likelihood averages all possible likelihoods across the parameter space, using the prior as a weight. Multiple integration is required to compute the marginal likelihood, making the exact calculation impossible. We use a technique known as modified harmonic mean to estimate it.22 Then, for two different models (A and B), the posterior odds ratio is P( A | {xt }Tt=1 ) Pr( A) L({xt }Tt=1 | model = A) = . P( B | {xt }Tt=1 ) Pr( B) L({xt }Tt=1 | model = B) If there are m ∈ M competing models, and one does not have strong views on which model is the best one (i.e. Pr(A)=Pr(B)=1/M) the posterior odds ratio equals the Bayes factor (i.e. the ratio of marginal likelihoods). 22 See Fernández-Villaverde and Rubio-Ramírez (2004).

- 22 - V. RESULTS We present our results in the following way. First, we present the posterior estimates obtained for a closed economy vis-à-vis the four specifications considered for open economy models. Second, we perform a model comparison by evaluating the marginal likelihood for each model. Third, we compute the standard deviations and correlations of each model at the mode posterior values. Fourth, we discuss the dynamics of our preferred model by analyzing the importance of the structural shocks for real exchange rate fluctuations. Finally, we look at the one-step ahead in-sample forecast performance of all models, and compare their performance to VARs. A. Posterior Distributions for the Parameters In Table 6, we present the means and standard deviations of the posterior parameters of all the models. In order to have a benchmark for the open economy estimates, we first provide the results from estimating each country as a closed economy. For the closed economy specification we assume that within each country agents only consume home produced goods (δ=θ=0), and are not allowed to trade bonds internationally. In addition, the real exchange rate is dropped from the set of observed variables. In column I of Table 6, we report the mean and standard deviation of the posterior distributions of the parameters for the euro area and U.S., treating each of them as a closed economy. Overall, our estimates are in the line of previous contributions. The average duration of price contracts implied by the point estimate of the price stickiness parameters are above four (5.94) and six quarters (7.09), respectively. The implied proportion of firms that index their prices to the inflation rate are 0.06 and 0.09 percent for the euro area and U.S. respectively, which, as in Galí and Rabanal (2004), suggests that with highly correlated shocks the pure forward-looking model seems to be valid. The habit formation parameters are around 0.5 and 0.6 in the euro area and the U.S. respectively, which are in line with the values found by Smets and Wouters (2003) and slightly above to the ones of Galí and Rabanal (2004) for the U.S. economy. The estimates of the monetary policy rule parameters are similar to what it is usually assumed in the literature. Thus, the estimated coefficients over inflation and output are 1.62 and 1.10 for the euro area and 1.85 and 0.92 for the U.S. respectively. Our results also suggest a high degree of interest rate smoothing (0.87 and 0.83 for the euro area and U.S. respectively). Finally, the estimated processes of the shocks suggest that all of them are highly autocorrelated, except for the productivity shock in the euro area.

- 23 - Table 6: Posterior Distributions I. Two Closed II. Complete III. Complete IV. Incomplete V. Incomplete Economies Markets, PCP Markets, LCP Markets, PCP Markets, LCP Std. Std. Std. Std. Std. Mean Dev. Mean Dev. Mean Dev. Mean Dev. Mean Dev. b 0.57 0.04 0.78 0.02 0.72 0.03 0.62 0.03 0.64 0.03 * b 0.61 0.04 0.69 0.04 0.68 0.04 0.55 0.04 0.54 0.03 γ 1.09 0.23 1.25 0.21 1.47 0.20 1.18 0.20 0.79 0.23 γ* 1.00 0.21 0.86 0.25 1.02 0.23 1.16 0.20 1.03 0.23 −1 (1 − α ) 5.94 0.89 4.77 0.46 6.29 0.62 4.28 0.39 4.12 0.47 * −1 (1 − α ) 7.09 0.92 14.74 1.68 12.66 1.36 5.74 0.63 4.95 0.63 τ 0.06 0.07 0.93 0.06 0.41 0.17 0.94 0.06 0.84 0.13 * τ 0.09 0.08 0.04 0.04 0.06 0.05 0.09 0.07 0.09 0.08 δ - - 0.13 0.02 0.23 0.02 0.12 0.01 0.06 0.01 θ - - 0.04 0.03 0.01 0.01 0.44 0.00 0.91 0.01 χ - - - - - - 0.007 0.004 0.013 0.008 ρr 0.87 0.02 0.88 0.01 0.87 0.01 0.89 0.01 0.88 0.01 γy 1.08 0.16 0.07 0.06 0.16 0.10 1.04 0.16 0.98 0.17 γp 1.59 0.13 2.24 0.15 2.03 0.13 1.90 0.15 1.71 0.16 ρ r * 0.82 0.02 0.85 0.02 0.85 0.01 0.81 0.02 0.82 0.02 * γ y 0.91 0.15 1.24 0.13 1.34 0.14 1.16 0.14 1.01 0.15 * γ p 1.81 0.17 1.67 0.13 1.71 0.13 1.85 0.13 1.86 0.15 ρx 0.80 0.27 0.63 0.04 0.69 0.04 0.17 0.08 0.39 0.07 ρ * x 0.93 0.02 0.96 0.002 0.96 0.002 0.92 0.02 0.96 0.002 ρg 0.91 0.02 0.96 0.001 0.96 0.001 0.93 0.02 0.93 0.02 ρ g* 0.82 0.05 0.84 0.03 0.89 0.03 0.87 0.03 0.88 0.03 ρη 0.93 0.03 0.89 0.04 0.86 0.04 0.96 0.00 0.93 0.02 ρη* 0.93 0.03 0.94 0.02 0.93 0.04 0.95 0.01 0.94 0.01 σ x (in %) 1.93 0.45 4.45 0.52 4.27 0.48 4.00 0.55 4.23 0.54 σ *x (in %) 1.91 0.29 4.68 0.43 3.32 0.33 0.92 0.20 0.62 0.09 σ g (in %) 2.32 0.29 5.72 0.40 4.78 0.31 3.64 0.45 3.41 0.49 σ (in %) * g 2.16 0.24 2.73 0.27 3.07 0.30 2.13 0.26 2.17 0.27 σ z (in %) 0.20 0.02 0.23 0.02 0.22 0.02 0.20 0.01 0.19 0.01 σ *z (in %) 0.23 0.02 0.22 0.02 0.22 0.02 0.25 0.02 0.23 0.02 σ a (in %) 0.70 0.02 2.58 0.21 2.03 0.17 1.39 0.12 1.44 0.14 σ η (in %) 0.46 0.03 0.43 0.03 0.44 0.03 0.67 0.05 0.50 0.03 σ η* (in %) 0.69 0.05 0.67 0.04 0.66 0.04 0.89 0.06 0.70 0.05 Log-Marginal 3981.6 4033.2 4106.8 4070.9

- 24 - Our benchmark open economy model is the one that assumes complete markets and PCP. The results are displayed in column II of Table 6. Interestingly, the results change in important ways with respect to the closed economy case. First, the proportion of firms that index their prices to the lagged inflation rate increases to almost one in the euro area, while inflation remains almost purely forward looking in the United States. The average duration of price contracts decreases for the euro area to 4.77 quarters and increases significantly for the United States to 14.74 quarters, which is a fairly large number.23 The habit persistence parameters increase both in the euro area (to 0.78) and the U.S. (to 0.69). Estimates of the Taylor rule for the U.S. obtained from the two-country model are more or less the same to the one obtained from the closed economy counterpart. However, we observe significant changes in the euro area, thus the estimated coefficient on inflation rises from 1.59 to 2.24 and the one on output decreases from 1.08 to 0.07. The degree of interest rate smoothing for each block presents minor changes with respect to the closed economy estimations. The persistence and volatility of all shocks increases greatly when the model tries to match the behavior of the real exchange rate. Except for the monetary and the demand shocks, the standard deviation of the shocks doubles or triples with respect to the closed economy estimates. Also, the autocorrelation of technology shocks in the United States and of preference shocks in the euro area increases to 0.96, which is the upper bound allowed for in the estimation. Thus, there is a tension in the model between matching a highly volatile real exchange rate and the less volatile output and consumption series. Below, we examine how well the models match the second moments of the data.24 We now turn to analyze the parameters that are critical in NOEM models and which are key in shaping real exchange rate dynamics: the implied degree of home bias captured by 1-δ, the intratemporal elasticity of substitution between goods across countries, θ, and the real exchange- rate elasticity with respect to the stock of foreign debt, χ, that arises from the incomplete markets assumption. In our benchmark NOEM model we find that the implied degree of home bias towards home goods is 0.87 which is below 0.984, the value used by CKM (2002) and Heathcote and Perri (2002). The baseline two-country model delivers a very small estimate for the elasticity θ, close to zero. This result can be understood from the market clearing conditions, because output and consumption are much less volatile than the real exchange rate. Another 23 This result comes from the assumption of a production function that is linear in labor input. If we assumed, as Galí, Gertler and López-Salido (2001) that the production function is concave in labor, we would obtain smaller average price durations. The same would happen if we introduced firm-specific capital or real demand rigidities, as in Altig et al. (2005) or Eichenbaum and Fischer (2004). 24 In our estimation we assume that shocks are orthogonal. However, the world aggregate shock indirectly adds some form of spillovers. Baxter and Crucini (1995) highlight the importance of the structure of shocks for international asset market structures in IRBC models. In particular, they find that if shocks are stationary and with substantial spillovers both complete and incomplete markets perform similarly. But if shocks are very persistent without spillovers, adding incomplete markets changes significantly the prediction of IRBC models.

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