DSGE-MODEL FOR RUSSIAN ECONOMY WITH BANKS ANDFIRM-SPECIFIC CAPITAL IN CORONAVIRUS PANDEMIC

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One of the most important aspects of investment is determining the “optimal investment portfolio”. To date, scientific research has been conducted to determine the optimal portfolio with “artificial intelligence” and “Fuzzy logic”. However, we determine the optimal portfolio based on Dynamic Stochastic General Equilibrium (DSGE) model. On the other hand, several factors affect returns, which is one of the most important issues in investment decision-makings, and various models have been developed to analyze the return of “capital” and “other assets”. In this regard, some of the most important models include linear and non-linear models, artificial neural network models, Fama–French model, Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, and optimal stable model. All of these models are indicative of the use of quantitative methods and models in the investment industry. One of the causes of using these models is developing the financial economy. In fact, the development of financial economy has more increased with the concept of “portfolio optimization”. In fact, the portfolio optimization and diversification concept is the basis of developing and expanding classical finance and financial decision-making. Financial markets, especially the capital market, can have a great deal of relevance to other sectors of the economy. In the present study, we design and calibrate a new Keynesian DSGE model in relation to the optimal investment portfolio and effectiveness of shocks (e.g., productivity and foreign exchange earnings fluctuation) on macroeconomics variables.

Abstract: This paper uses a novel method for conducting policy analysis with potentially misspecified DSGE models (Del Negro and Schorfheide 2004) and applies it to a simple New Keynesian DSGE model. We illustrate the sensitivity of the results to assumptions on the policy invariance of model misspecifications. JEL CLASSIFICATION: C32, E5 KEY WORDS: Bayesian Analysis, DSGE Models, Misspecification Policy Analysis. 1 Introduction Despite recent successes in improving the empirical performance of dynamic stochastic general equilibrium (DSGE) models, e.g., Smets and Wouters (2003), even large-scale DSGE models su[R]er to some extent from misspecification (see Del Negro, Schorfheide, Smets, and Wouters 2004). In this paper misspecification means that the DSGE model potentially imposes invalid cross-coe[+ or -]cient restrictions on the moving-average representation of the macroeconomic time series that it aims to explain. As a consequence, one typically observes that the forecasting performance of DSGE models is worse than that of vector autoregressions (VARs) estimated with well-calibrated shrinkage methods. On the other hand, DSGE models have the advantage that one can explicitly assess the e[R]ect of policy regime changes on expectation formation and decision rules of private agents. Thus, policy analysis with DSGE models is robust to the Lucas critique and potentially more reliable than conclusions drawn from VARs. This trade-o[R] poses a challenge to policymakers who want to use DSGE models in practice. Del Negro and Schorfheide (2004a) proposed a framework that combines VARs and DSGE models, extending earlier work by Ingram and Whiteman (1994). In this framework DSGE model restrictions are neither completely ignored as in the unrestricted estimation of VARs, nor are they dogmatically imposed as in the direct estimation of DSGE models. Instead the VAR estimates are tilted toward the restrictions implied by the DSGE model, where the degree of tilting is determined by a Bayesian data-driven procedure that trades o[R] model fit against complexity. Del Negro and Schorfheide (2004a) show that priors arising from the same model used in this paper improve both the in-sample and out-of-sample fit of a VAR. In ongoing research (Del Negro and Schorfheide, 2004b) we build upon our earlier work and further develop procedures that are suitable to study the e[R]ects of rare regime shifts with potentially misspecified DSGE models. These procedures can be viewed as a Bayesian alternative to the robust control and minimax approaches that recently have been proposed to cope with model misspecification, e.g., Hansen and Sargent (2000) and Onatsky and Stock (2002). One advantage of Bayesian procedures is that the policymaker can learn from existing data about the extent of the DSGE model's misspecification, and consequently adjust her policies. The present paper applies these procedures to a simple New Keynesian DSGE model. We illustrate that conclusions about the e[R]ects of changing the response to inflation are sensitive to assumptions about the policy invariance of observed discrepancies between model and reality. Section 2 briefly describes the DSGE model. In Section 3 we outline our framework, Section 4 discusses our findings, and Section 5 concludes. 2 The DSGE Model Starting point is a DSGE model in log-linearized form. The model used here is a standard New Keynesian DSGE model, e.g., Woodford (2003), which we now briefly describe (see Del Negro and Schorfheide (2004a) for details). The log-linearized equilibrium conditions consist of three equations in nominal interest rates [[??].sub.t], output [[??].sub.t], and inflation [[??].sub.t] ([sup.~] denotes percentage deviations from the steady state and [DELTA] is the temporal difference operator): Monetary Policy Rule: [[??].sub.t] = [rho]R [[??].sub.t-1] + (1-[rho]R) [[psi].sub.1] [[??]. …

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INTRODUCTION: DYNAMIC STOCHASTIC GENERAL EQUILIBRIUM MODELLING AND THE STUDY OF ASIA‐PACIFIC ECONOMIES

A Scientific Macroeconomic Model Derived from Fundamental Equation of Economics

Previous articleNext article FreeCommentMarc P. GiannoniMarc P. GiannoniFederal Reserve Bank of Dallas Search for more articles by this author Federal Reserve Bank of DallasPDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreI. IntroductionSince Phillips (1958), economists have sought to estimate a Phillips curve relationship or a positive relation between inflation, πt, and a measure of the output gap, xt. Although historically such a relationship could be easily detected, the Phillips curve appears to have flattened in the United States more recently. Some authors have suggested that inflation does not depend on slack, that it is largely exogenous. This raises the key question: What changed? The answer to that question is critical for much of macroeconomics and in particular for monetary policy. With most central banks around the world seeking to stabilize inflation around a target level (e.g., 2% in the United States), it is crucial to understand the determinants of inflation and to know whether monetary policy can still affect inflation.Several potential explanations have been provided for the flattening of the Phillips curve. Some have suggested that structural changes in the economy in recent decades have played a significant role (e.g., Duca 2019). In many of models of sticky prices, more rigid prices than in the past or increases in market concentration and pricing power (De Loecker and Eeckhout 2017) could also result in a flattening of the Phillips curve. McLeay and Tenreyro argue instead that monetary policy itself is responsible for the flattening of the Phillips curve. The explanation is simple: If the central bank conducts optimal monetary policy, seeking to minimize deviations of inflation from target and output from potential output, then it should set its policy instruments to increase inflation when output is below potential and vice versa. It follows that optimal policy causes a negative correlation between inflation and the output gap. That negative correlation blurs in turn the positive correlation implied by the Phillips curve, so that in equilibrium, the correlation between inflation and the output gap may be positive, negative, or null, depending on the variability of shocks perturbing either the Phillips curve or the optimal policy relationship. The authors make the point very clearly through a sharp and elegant analysis, in the context of a simple New Keynesian model.After exposing the identification problem in estimating the slope of a Phillips curve, McLeay and Tenreyro propose strategies to estimate the Phillips curve and present evidence of a robust Phillips curve in the United States. This is a very nice and transparent paper that should be read by all of those who are interested in understanding and estimating the Phillips curve.In the remainder of this discussion, I will briefly review the authors’ story in the historical context and will quibble in Section III with the authors’ proposed identification of the Phillips curve, focusing in particular on the role of expectations.II. The StoryA key point of the paper is that one should distinguish between (i) a reduced-form Phillips curve, that is, an empirical relationship between inflation and a measure of the output gap, and (ii) a structural Phillips curve, that is, the underlying relationship between inflation, the output gap, inflation expectations, and possibly other factors, resulting from the firms’ optimal setting of their prices. In the debate about the flattening of the Phillips curve, the two concepts are often mixed, as the structural Phillips curve may be difficult to identify. As the authors make clear, optimal policy can lead to a flattening or even a negative relationship between inflation and the output gap in the reduced-form Phillips curve, even though there is a well-defined positively sloped underlying structural Phillips curve. The authors’ result does not rely on assuming that the policy maker conducts optimal policy under discretion and that it has a quadratic objective function. Consider the standard (structural) New Keynesian Phillips curve (eq. [1] in the paper) that characterizes the trade-off between inflation, πt, and the output gap, xt, faced by the central bank:(1)πt=βEtπt+1+κxt+ut,with a slope κ that is positive by assumption. In the face of “cost-push shocks,” ut, it is generally not possible to stabilize both inflation and the output gap. Suppose that the central bank can control the output gap, for example, via a short-term policy rate; that it observes ut and that it seeks to stabilize inflation at its target (πt=0) as in the case of a pure inflation-targeting regime. Optimal policy would then imply that the output gap respond negatively to the cost-push shockxt=−κ−1utso that, in equilibrium, inflation and hence inflation expectations are completely stabilized around the inflation target:πt=0,Etπt+1=0,as illustrated by the x-axis in figure 1 (which is adapted from figure 3 in the paper). The implication of this policy is that inflation would be uncorrelated swith the output gap. In other words, even though the underlying structural Phillips curve implies a positive relationship between inflation and the output gap, inflation targeting gives rise to a flat reduced-form Phillips curve relationship, with inflation apparently unrelated to the output gap.Fig. 1. Structural Phillips curve and optimal policyView Large ImageDownload PowerPointIn the case that the central bank cares both about inflation and output gap deviations from target, as the authors point out, optimal policy under discretion gives rise to a negative relationship between inflation and the output gap. Indeed, when the central bank seeks to minimize the loss function:(2)E0∑t=0∞βt[πt2+λxt2],subject to the behavior of the private sector represented by the structural Phillips curve (eq. [1]), optimal policy under discretion, that is, taking private sector expectations Etπt+j, Etxt+j as given, results in the optimal targeting rule:(3)πt=−λκxt,which states that the central bank seeks to increase inflation when output is below potential and vice versa, as illustrated by the downward-sloping gray line in figure 1. As exogenous shocks ut shift the Phillips curve but not the optimal policy relation (eq. [2]), equilibrium realizations of inflation and the output gap draw not the Phillips curve but rather the optimal target criterion (eq. [2]). In equilibrium, πt, xt depend only on utπt=λκ2+λ(1−βρ)ut,xt=−κκ2+λ(1−βρ)ut,where ρ is the degree of serial correlation in ut so that the covariance between inflation and the output gapcov(πt,xt)=−λκ(κ2+λ(1−βρ))2var(ut)<0is necessarily, and the correlation corr(πt,xt)=−1.A. Targeting Rule versus Taylor RuleSome readers may find a target criterion of the form (eq. [3]) to be unrealistic. We should however note that its characterization of monetary policy is not too different from that under a conventional Taylor rule. Indeed, the optimal target criterion (eq. [3]) implies that the policy rate it is set so as to satisfy πt+(λ/κ)xt=0. The policy rate can thus be related to inflation and the output gap according to a conventional Taylor-type rule:it=ϕ(πt+λκxt)with a large coefficient ϕ(→∞). If, in addition, policy makers care to also stabilize other variables such as the interest rate, then the optimal policy response to inflation and the output gap would likely be of a similar form but with a smaller coefficient 0<ϕ<∞, and the optimal interest rate would in addition respond to these other variables (e.g., the lagged interest rate).B. Historical ContextAs the authors recognize, the flattening or disappearance of an empirical relationship such as the reduced-form Phillips curve as a consequence of a successful monetary policy is an old idea that goes back at least to Kareken and Solow (1963), who emphasized that if monetary policy succeeds at offsetting all shocks that affect income, then we would observe fluctuations in money growth and a perfectly steady path for income. Similar ideas have been reinforced and generalized by many authors since then, most prominently with Goodhart’s “law” (1981)1 and the Lucas (1976) critique,2 and it is still mentioned in recent work (e.g., Hooper, Mishkin, and Sufi 2019). Unfortunately, it appears that much of the profession is quick to forget these powerful lessons when the empirical relationship between two key macroeconomic variables appears to have weakened, and so it is important that McLeay and Tenreyro remind us of this. As we learned from Lucas (1976), these lessons do not apply merely to relationships between two macroeconomic variables; they can be more pervasive. For instance, when Boivin and Giannoni (2006) documented the fact that impulse response functions of inflation and output to an unexpected 25 basis points change in the federal funds rate had become more muted in the post-1980 period, compared with the 1960–80 period, they asked whether this was due to a structural change in the economy (such as a flattening of the structural Phillips curve or a diminished sensitivity of economic activity to interest rate changes) or to a change in policy itself; they found that a more aggressive stance of policy toward inflation stabilization in the post-1980 period could explain most if not all of the change in estimated impulse response functions.III. Identifying the Structural Phillips CurveAside from making it very clear that one should not conclude that the Phillips curve has disappeared based on correlations between inflation and the output gap, or simple regressions, McLeay and Tenreyro describe in simple terms the identification problem, propose ways to address it, and provide evidence that there is a structural Phillips curve with positive slope between inflation and the output gap. As figure 1 illustrates, cost-push shocks ut help trace the policy rule, not the Phillips curve. If the policy rule is itself subject to disturbances et so that it becomes(4)πt=−λκxt−et,then fluctuations in et may help trace the structural Phillips curve. The identification problem arises when we face shocks to both the policy (targeting) rule and the Phillips curve.Focusing on equations (1) and (4) provides a transparent way of characterizing the identification problem, in a near-static environment (for given inflation expectations), in which the Phillips curve implies a positive contemporaneous relation between πt and xt, whereas policy implies a negative contemporaneous relation between these two variables. If only we could control for the cost-push shocks ut, then shocks to the policy rule (represented by the downward-sloping gray line in fig. 1) would trace out the structural Phillips curve. Similarly, the identification problem can be partly addressed in the case of regional Phillips curve subject to region-specific cost-push shocks, but with monetary policy responding to aggregate economic conditions, as McLeay and Tenreyro as well as other recent studies have proposed (Hooper et al. 2019).A. Difficulties with Identification via Disturbances to the Optimal Target CriterionAlthough the authors make a strong case for identifying the Phillips curve using equations (1) and (4), I am concerned that it may not be as easy to identify the Phillips curve in more complicated setups, in particular when the policy rule disturbances et are not exogenous and depend on other variables, including variables affecting the residuals ut themselves, or if the residuals ut capture more than exogenous cost-push shocks, indeed if they depend on variables that also shift the policy rule.To illustrate this point, I consider a few examples:• Take again the simple Phillips curve (eq. [1]) and the objective function (eq. [2]), but assume that optimal policy is conducted under commitment. Then, as pointed out by McLeay and Tenreyro, optimal policy can be represented by an optimal target criterion of the form (eq. [4]) with et=−(λ/κ)xt−1. If the cost-push shock is serially correlated, then et and ut are correlated. A suitable instrument is thus needed.• Assume instead that inflation involves some inertia as modeled, for example, in Christiano, Eichenbaum, and Evans (2005), and as appears realistic in the data. Then, as shown in Giannoni and Woodford (2004, eq. [12]), lagged inflation appears both in the Phillips curve (eq. [1]) and in the optimal target criterion (eq. [4]), so that et and ut would both be functions of lagged inflation.• When the representative household faces habit persistence in expenditures, then again, as shown in Giannoni and Woodford (2004, eq. [47] and eq. [53]), both the Phillips curve and the optimal target criterion involve the lagged output gap, so that ut and et in equations (1) and (4) would be both functions of xt−1 and hence would be correlated.• Suppose, alternatively, that the policy maker faces a Phillips curve of the form (eq. [1]) but cares about interest rate variability in addition to the two other terms entering the objective function (eq. [2]). Then, the optimal target criterion involves a relationship between current and forecasts of inflation, output gaps, as well as lags of the output gap and interest rates (see Giannoni and Woodford 2004; eq. [22]). Again, that would imply that the terms ut and et in equations (1) and (4) would be correlated.Similar concerns arise when the model involves both price and wage stickiness, so that a Phillips curve arises for price and for wage inflation; when monetary policy actions have delayed effects on macroeconomic variables, so that optimal policy depends on expectations of future inflation and output gaps; and so on.B. Identifying the Phillips Curve: Static versus DynamicAlthough McLeay and Tenreyro make an important conceptual point and provide a very intuitive way of characterizing the difficulty in identifying the Phillips curve in a near-static framework, I am skeptical that one can fully recover the Phillips curve without taking a stronger stance on dynamic relationships linking the key macroeconomic variables. The simple New Keynesian Phillips curve considered is an invaluable tool to develop intuition, but much of the empirical literature suggests that inflation responds to measures of slack in a more inertial fashion. Similarly, whereas the simple model considered assumes that policy makers can instantaneously affect economic activity and the output gap, empirical evidence suggests the effects are more sluggish. (If not, it would be difficult to explain why inflation has been below its target and economic activity has been below estimates of its potential for so many years following the Great Recession.) This implies that the dynamic relationship between inflation and the output gap is more complex than described by the simple New Keynesian model and that it is important to properly model these dynamics to identify a Phillips curve.Estimated dynamic stochastic general equilibrium (DSGE) models are a valuable tool to characterize the joint dynamics of key macroeconomic variables and thus of the complex interactions between the Phillips curve and policy. In such dynamic models, inflation expectations play a key role, and a monetary policy aimed at stabilizing inflation and hence inflation expectations does also imply a flattening of the reduced-form Phillips curve. A potential downside of such fully specified models is that they are necessarily misspecified. A key question, then, is whether such models can explain important recent episodes. In particular, Del Negro, Giannoni, and Schorfheide (2015) study whether a standard DSGE model along the lines of Christiano et al. (2005) and Smets and Wouters (2007) augmented with financial frictions and estimated with data up to 2008Q3 can explain the US macroeconomic behavior during and after the Great Recession. They find that as soon as credit spreads jump in the fall of 2008, the model successfully predicts the sharp contraction in activity and the modest and protracted decline in inflation, as shown in figure 2. They also find that data on credit spreads and inflation expectations, in addition to the standard data series used by, for example, Smets and Wouters (2007), are important in properly characterizing the state of the economy.Fig. 2. Dynamic stochastic general equilibrium (DSGE) model forecast of gross domestic product (GDP) growth, the output gap, and GDP deflator inflation, based on the model in Del Negro et al. (2015). Out-of-sample forecast starting in 2008Q4 (dotted lines); data used in estimation (solid lines); and ex post realization of the data (dashed lines).View Large ImageDownload PowerPointTo understand why inflation does not collapse given the sharp drop in output, it is useful to consider a simplified version of the forward-looking Phillips curve considered in the model. That simplified Phillips curve, which is similar to equation (1)—except that xt is replaced with real marginal costs—implies that inflation does not depend only on the current gap (or marginal cost), but on the entire path of future gaps:πt=∑j=0∞βjEt[κxt+j︸gaps+ut+j︸mark-up shocks].It follows that inflation and inflation expectations in the model remain well anchored, despite the sharp collapse in output, because monetary policy is expected to be aggressive enough to close the gaps in the future.Similarly to McLeay and Tenreyro, although the model includes a structural Phillips curve that involves a positive relationship between inflation and the output gap, inflation was predicted to move relatively little in the face of the output collapse. However, in contrast to McLeay and Tenreyro, according to the DSGE model, it was not the contemporaneous monetary stimulus (at the end of 2008 and in early 2009) that helped stabilize inflation; indeed, short-term nominal rates were constrained by the zero lower bound at that time. Instead, the expectation of future stimulus induced expectations of closing output gaps in the future and hence helped keep inflation near its target.IV. ConclusionMcLeay and Tenreyro have written a very nice paper that clearly and elegantly exposes the identification problem in estimating the slope of a Phillips curve when policy makers seek to stabilize inflation and/or the output gap. They propose interesting strategies to estimate the Phillips curve and present evidence of a robust Phillips curve in the United States. The simplicity of the framework considered allows the authors to provide numerous insights. I have expressed some reservations about the ability to generalize the results beyond the current framework, in particular when one faces more complex dynamic interactions between inflation, inflation expectations, activity, and policy. In more complicated environments, I suspect that DSGE model estimation remains necessary to better characterize the joint dynamics of macro variables, and the role of expectations.Endnotes. Author email address: Giannoni ([email protected]). The views expressed in this discussion are those of the author and do not necessarily represent those of the Federal Reserve Bank of Dallas or the Federal Reserve System. For acknowledgments, sources of research support, and disclosure of the author’s material financial relationships, if any, please see https://www.nber.org/chapters/c14246.ack.1. Goodhart (1981, 116): “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.”2. Lucas (1976, 40–41): “A change in policy [parameters] affects the behavior of the system in two ways: first by altering the time series behavior of [policy variables]; second by leading to modification of the behavioral parameters … governing the rest of the system… . It follows that any change in policy will systematically alter the structure of econometric models.”ReferencesBoivin, J., and M. P. Giannoni. 2006. “Has Monetary Policy Become More Effective?” Review of Economics and Statistics 88 (3): 445–62.First citation in articleCrossrefGoogle ScholarChristiano, L. J., M. Eichenbaum, and C. Evans. 2005. “Nominal Rigidities and the Dynamic Effect of a Shock to Monetary Policy.” Journal of Political Economy 113 (1): 1–45.First citation in articleLinkGoogle ScholarDe Loecker, J., and J. Eeckhout. 2017. “The Rise of Market Power and the Macroeconomic Implications.” Working Paper no. 23687, NBER, Cambridge, MA.First citation in articleGoogle ScholarDel Negro, M., M. P. Giannoni, and F. Schorfheide. 2015. “Inflation in the Great Recession and New Keynesian Models.” American Economic Journal: Macroeconomics 7 (1): 168–96. https://doi.org/10.1257/mac.20140097.First citation in articleCrossrefGoogle ScholarDuca, J. V. 2019. “Inflation and the Gig Economy: Have the Rise of Online Retailing and Self-Employment Disrupted the Phillips Curve?” Dallas Fed Working Paper no. 1814. https://doi.org/10.24149/wp1814.First citation in articleGoogle ScholarGiannoni, M. P., and M. Woodford. 2004. “Optimal Inflation Targeting Rules.” In The Inflation-Targeting Debate, ed. B. Bernanke and M. Woodford, 93–162. Chicago: University of Chicago Press.First citation in articleGoogle ScholarGoodhart, C. 1981. “Problems of Monetary Management: The U.K. Experience.” In Inflation, Depression, and Economic Policy in the West, ed. Anthony S. Courakis, 111–46. Totowa, NJ: Barnes & Noble.First citation in articleGoogle ScholarHooper, P., F. S. Mishkin, and A. Sufi. 2019. “Prospects for Inflation in a High Pressure Economy: Is the Phillips Curve Dead or Is It Just Hibernating?” Working Paper no. 25792, NBER, Cambridge, MA.First citation in articleGoogle ScholarKareken, John H., and Robert M. Solow. 1963. “Lags in Monetary Policy.” In Stabilization Policies, ed. E. Cary Brown, 14–96. New York: Prentice Hall.First citation in articleGoogle ScholarLucas, R. E., Jr. 1976. “Econometric Policy Evaluation: A Critique.” Carnegie-Rochester Conference Series on Public Policy 1:19–46.First citation in articleCrossrefGoogle ScholarPhillips, A. W. 1958. “The Relation between Unemployment and the Rate of Change of Money Wage Rates in the United Kingdom, 1861–1957.” Economica 25 (100): 283–99. https://doi.org/10.2307/2550759.First citation in articleGoogle ScholarSmets, F., and R. Wouters. 2007. “Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach.” American Economic Review 97 (3): 586–606. https://doi.org/10.1257/aer.97.3.586.First citation in articleCrossrefGoogle Scholar Previous articleNext article DetailsFiguresReferencesCited by NBER Macroeconomics Annual Volume 342019 Sponsored by the National Bureau of Economic Research (NBER) Article DOIhttps://doi.org/10.1086/707182 © 2020 by National Bureau of Economic Research. All rights reserved.PDF download Crossref reports no articles citing this article.

Using the tools of regional dynamic models for analyzing the economy of the constituent entities of the Russian Federation, in particular, for studying regional business cycles is currently an urgent task. It is determined by the need to develop system conceptions about the factors, conditions and prerequisites for the development of regions, about the features and trends of the dynamics of their sector and territorial structure. The purpose of the article is to develop a dynamic stochastic multi-sector model for analyzing the effects of regional economic policy. The scientific novelty of the research concerns the development and implementation of dynamic models with microeconomic justification to formalize the processes of regional development, the sustainability of regional policy and spatial development. Similar class of models, that forms the theoretical foundation of contemporary macro-economics, is currently used for the analysis of national economy mostly. Models of such class that describe the processes in the regional economy are practically absent. The original tools for the construction of a regional dynamic stochastic general equilibrium model, suggested by the authors, describe the structure of a real sector of the economy of Sverdlovsk region. Parameterization of the model was made on the empirical data basis about the economy of Sverdlovsk region for 2003–2016. The behaviour of the following economic operators has been considered in the model: households; firms operating in the real sector of economy, the regional and federal government, and the Central Bank. Fiscal multipliers for three sectors of the economy – tradable goods sector, non-tradable goods sector and resource sector have been calculated with impulse response functions. The analysis of fiscal multipliers has revealed that the shock of the effective tax rate on individual income and the sock of regional costs have the most significant effect on the output in the above considered sectors of economy among all the rest fiscal shocks. The use of the tools in the form of a historical decomposition of regional variables demonstrates the results of the impact of supply and demand shocks in a time perspective on the output in the three sectors of the regional economy. The results of temporal decomposition of the variations of the endogenous variables mentioned above suggest that the cyclic processes in the regional economy of Sverdlovsk region during the study period are largely due to factors of supply rather than demand. The research results may be used both for the analysis of the regional economic policy priorities and for the development of measures aimed at the decrease of possible crisis phenomena in the regional economy. The trend to the construction of multi-sector models of regional economy in the framework of general equilibrium approach with the macroeconomics justification and rational expectations of economic operators described in the article stresses the importance and prospects of further studies. In particular, to reflect the specifics of the regions, it is necessary to take into account the institutional factors of each region in the model. This issue is an interesting theme for further research in the field of modeling of regional social and economic systems. Keywords region, regional economic policy, dynamic stochastic model, tradable and non-tradable goods sector, resource sector, fiscal multipliers, demand shocks, supply shocks, impulse response functions, historical decomposition of variations of endogenous variables. Acknowledgements The article has been written according to the Plan of Research and Development of the Institute of Economics, the Ural Branch of the Russian Academy of Sciences for 2019–2021.

Previous articleNext article FreeCommentStephanie Schmitt-GrohéStephanie Schmitt-GrohéColumbia University and NBER Search for more articles by this author Columbia University and NBERPDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreBeaudry and Lucke’s paper “Letting Different Views about Business Cycles Compete” is a contribution to the empirical literature on the estimation of the sources of business cycles. It uses various five-variable vector error correction models (VECMs) to estimate the relative importance of anticipated total factor productivity (TFP) shocks, unanticipated TFP shocks, investment-specific technology shocks, preference shocks, and monetary policy shocks. An innovation relative to the large related literature on structural vector autoregression (SVAR)–based estimation of the sources of fluctuations is the focus on anticipated TFP shocks and on imposing cointegration relationships. Further, Beaudry and Lucke use time series on TFP, the relative price of investment, stock prices, federal funds rates, and a measure of aggregate activity in their estimation. This set of observables is slightly different than that used in the related literature. The main finding of their paper is that anticipated TFP shocks explain the majority of fluctuations in aggregate activity and stock prices at business cycle frequencies in the United States.Many authors have studied the question of what the sources of short-run fluctuations are. Yet this fundamental question in macroeconomics remains largely unresolved. Cochrane (1994), in a piece written for the Carnegie-Rochester Conference Series on Public Policy, starts his article on this topic as follows: “What shocks are responsible for economic fluctuations? Despite at least two hundred years in which economists have observed fluctuations in economic activity, we still are not sure” (295). Fifteen years later in business cycle research this statement is still a valid description of the state of the literature.Cochrane interpreted the findings of his (1994) study as suggesting that contemporaneous shocks to technology, money, credit, and oil cannot account for the majority of observed aggregate fluctuations.1 More recent SVAR-based papers using long-run restrictions such as Galí and Rabanal (2004) find, like Cochrane, a small role for permanent technology shocks in accounting for business cycle variations in hours and output. In table 2 of their paper Galí and Rabanal report that the share of variance due to technology shocks lies between 7% and 37% for output and between 5% and 36% for hours. Most important, under their favored interpretation, the technology shock accounts for less than 10% of the variance of output and hours. They therefore conclude that “nevertheless, it is safe to state that the bulk of the evidence reviewed in the present paper provides little support for the initial claims of the RBC literature on the central role of technological change as a source of business cycles” (Galí and Rabanal 2004, 228).On the other hand, there are papers presenting evidence that suggests that technology shocks are the major source of fluctuations, and the Beaudry and Lucke paper fits into this group. For example, the empirical paper of Fisher (2006), using SVAR methods, comes to the conclusion that neutral and investment-specific “technology shocks account for 73 percent of hours’ and 44 percent of output’s business cycle variation before 1982, and 38 percent and 80 percent afterward. The shocks also account for more than 40 percent of hours’ and 58 percent of output’s forecast errors over a three- to eight-year horizon in both samples. The majority of these effects are driven by the investment shocks” (413). Using Bayesian methods to estimate a dynamic stochastic general equilibrium model, Smets and Wouters (2007) find that at least 30% of the forecasting error variance of output is attributable to a combination of neutral and investment-specific technology shocks, with the majority of this share explained by neutral technology shocks. Justiniano, Primiceri, and Tambalotti (2008), like Smets and Wouters, using Bayesian estimation of a dynamic stochastic general equilibrium model, find an even larger share of fluctuations driven by technology shocks. Contrary to Smets and Wouters, however, Justiniano et al. find that most of the output variance is accounted for by the investment-specific technology shock rather than the neutral technology shock. Justiniano et al. attribute their finding of a larger role for the investment-specific shock to data differences, such as differences in the treatment of inventories and consumer durables. These differences in the definition of the data can increase the estimated share of the variance of output due to investment-specific technology shocks at business cycle frequencies from 18% to 53% for output and from 21% to 61% for hours.2The paper of Beaudry and Lucke is most closely related to Beaudry and Portier’s paper (2006). In that paper, Beaudry and Portier introduce a novel identification scheme to estimate (in the context of a VECM framework) anticipated TFP shocks. Most of the analysis is carried out for bivariate systems of TFP and stock prices. Under one identification scheme, the news TFP shock is that shock that does not affect TFP contemporaneously and under the other scheme the news TFP shocks is the one that has a long-run effect on TFP. Beaudry and Portier show that the correlation between the news TFP shock series identified by these two alternative schemes is very high and that impulse responses to them of measures of economic activity are quite similar. Therefore, Beaudry and Portier conclude that the common component of these two shocks represents an anticipated TFP shock. Most important for the relation to the paper of Beaudry and Lucke is the fact that Beaudry and Portier show that the so identified news TFP shock explains more than 50% of the forecast error variance of consumption, hours, investment, and output (measured as the sum of investment and consumption).1At the same time, Cochrane showed that VARs estimated using artificial data from a real business cycle (RBC) model driven by contemporaneous and news shocks to technology produce responses to consumption shocks that resemble the corresponding responses implied by VARs estimated on actual U.S. data. And thus his paper is often cited as one of the first to revive the idea of Pigou or news-driven business cycles.2One caveat to the results of Justiniano et al. is that their estimates imply a volatility for the relative price of investment, which they exclude from the set of observables, that is significantly larger than the observed standard deviation of this variable.I. Interpretation of Structural DisturbancesThe current paper by Beaudry and Lucke extends the work of Beaudry and Portier by moving away from bivariate SVAR systems to larger ones. Within a larger SVAR/VECM system the identification assumption of Beaudry and Portier must be modified. Specifically, Beaudry and Lucke estimate a VECM model of the form where the vector yt contains period t observations for TFP, the relative price of investment, stock prices, hours, and the federal funds rate, β denotes the cointegration vector, $$\Gamma ( L) $$ denotes a lag-polynomial, and $$\varepsilon _{t}=[ \varepsilon ^{1}_{t};\varepsilon ^{2}_{t};\varepsilon ^{3}_{t};\varepsilon ^{4}_{t};\varepsilon ^{5}_{t}] $$ denotes the vector of structural shocks that are the focus of interest. To identify the VECM, in particular the matrix B and the vector $$\varepsilon _{t}$$, Beaudry and Lucke impose the following identification restrictions. Identification restriction A1 says that only $$\varepsilon ^{1}_{t}$$ may have a contemporaneous effect on TFP. Therefore, $$\varepsilon ^{1}_{t}$$ is labeled the TFP shock. Implicitly it is therefore assumed that TFP is measured without error and that TFP is exogenous. Identification restriction A3 says that $$\varepsilon ^{5}_{t}$$ does not affect economic activity contemporaneously and is therefore interpreted as a monetary policy shock. Identification restriction A2 imposes that $$\varepsilon ^{4}_{t}$$ and $$\varepsilon ^{5}_{t}$$ have no long-run effect on TFP. Under identification scheme 1, denoted ID1, $$\varepsilon ^{3}_{t}$$, $$\varepsilon ^{4}_{t}$$, and $$\varepsilon ^{5}_{t}$$ are assumed to have no contemporaneous effect on the price of investment. Identification assumptions A1 and B1 then imply that $$\varepsilon ^{2}_{t}$$ must be the contemporaneous innovation to the relative price of investment. While in principle this identification scheme allows for $$\varepsilon ^{4}_{t}$$ to represent an anticipated temporary TFP shock or an anticipated temporary or permanent shock to the relative price of investment, the estimation results show that $$\varepsilon ^{4}_{t}$$ has very little effect on either TFP or the relative price of investment and thus it is unlikely that it represents a technology shock. Beaudry and Lucke therefore interpret it as a preference shock.Under identification scheme 2, ID2, assumption B1 is replaced by imposing that $$\varepsilon ^{2}_{t}$$ has no long-run effect on TFP. This would still allow for the possibility that $$\varepsilon ^{2}_{t}$$ is an anticipated temporary TFP shock. But the estimation assigns almost no role to $$\varepsilon ^{2}_{t}$$ in accounting for the variance of TFP. Identification scheme 2 leaves open the possibility that $$\varepsilon ^{2}_{t}$$, $$\varepsilon ^{3}_{t}$$, or $$\varepsilon ^{4}_{t}$$ affect the price of investment contemporaneously and thus could be called investment-specific shocks. Beaudry and Lucke, however, interpret $$\varepsilon ^{3}_{t}$$ as an anticipated TFP shock. The reason is that the identification assumption imposes that $$\varepsilon ^{3}_{t}$$ does not affect TFP on impact—thus it could not be an unanticipated TFP shock—and that the estimation yields that (at horizons not shown in figs. 3 or 5, namely 60 quarters) $$\varepsilon ^{3}_{t}$$ explains about three-fourths of the forecasting error variance of TFP under ID2. However, for horizons of 32 quarters or less (the time horizon shown in fig. 5) $$\varepsilon ^{3}_{t}$$ explains less than 20% of TFP and thus the interpretation as a TFP shock is less immediate. I want to entertain whether one could with equal plausibility interpret $$\varepsilon ^{3}_{t}$$ as an investment-specific shock. As shown in my figure 1, under identification scheme 2, $$\varepsilon ^{3}_{t}$$, explains between 40% and 60% of the forecasting error variance of the price of investment for forecasting horizons between 8 and 32 quarters. And $$\varepsilon ^{3}_{t}$$ explains less of the forecast error variance of TFP than that of the price of investment at any of these forecasting horizons. This might lead one to interpret $$\varepsilon ^{3}_{t}$$ as an investment-specific technology shock rather than, as maintained by Beaudry and Lucke, a TFP shock. The figure further shows that $$\varepsilon ^{3}_{t}$$ explains 60% of the forecasting error variance of hours and stock prices for forecasting horizons greater than 4 quarters. And thus one might be led to conclude that an investment-specific technology shock is the most important source of fluctuations in stock prices and real activity. This interpretation of $$\varepsilon ^{3}_{t}$$ would therefore be less at odds with the findings of Fisher (2006) on the importance of investment-specific technology shocks.Fig. 1. Share of forecast error variance due to ε3,t, VECM with ID 2View Large ImageDownload PowerPointII. Are Anticipated Shocks Identified in the Vector Error Correction Model?To be convinced by the interpretation given to the paper’s findings regarding the importance of anticipated shocks one needs to be convinced that the empirical strategy employed indeed is able to identify such shocks. Because it is not immediately obvious that this is the case, in what follows I will discuss some concerns one may have regarding the ability of SVAR/VECM methods to identify anticipated shocks.Beaudry and Lucke address the question of identification by presenting a theoretical model of the business cycle and checking whether the empirical identification strategy they employ, that is, a VECM analysis, would uncover the true structural shocks from data generated by this theoretical model. In particular, figure 4 (of the Web appendix of Beaudry and Lucke) shows the population forecast error variance decomposition (FEVD) of hours in the theoretical model with respect to the four structural shocks of the theoretical model: the unanticipated innovation to the growth rate of TFP, $$\varepsilon _{A,t}$$; the 8-quarter anticipated innovation to TFP, $$\varepsilon _{NA,t}$$; the unanticipated innovation to the growth rate of the price of investment, $$\varepsilon _{Z,t}$$; and the unanticipated innovation to the preference shock, $$\varepsilon _{\psi ,t}$$. (This is a real model and hence the fifth structural shock, which had the interpretation of a monetary policy shock, is dropped.) Then figures 4–6 of the Beaudry and Lucke Web appendix show the FEVD one would obtain were one to feed data generated by the calibrated real business cycle (RBC) model through the VECM machinery and impose the various identification schemes labeled ID1–ID3. In figure 2, I repeat this exercise for the case of identification scheme ID1. One difference between my figure 2 and Beaudry and Lucke’s figures is that I show the population FEVD implied by the calibrated theoretical model and the FEVD stemming from applying identification scheme ID1 to artificial model generated data in the same graph and for all four variables, that is, TFP, the price of investment, stock prices, and hours, whereas Beaudry and Lucke show this only for hours and in two different graphs.3 The purpose of this exercise is to check whether the VECM identified innovation $$\varepsilon ^{3}_{t}$$ does indeed explain the same share of variance in all four observables as the anticipated TFP shock, $$\varepsilon _{NA,t}$$, that it is meant to identify. A convincing case that the ID1 scheme, or any other of the identification schemes considered, is able to recover the true structural shocks is incomplete unless it does so for all four variables considered. After all, the fact that $$\varepsilon ^{3}_{t}$$ is interpreted as an anticipated TFP shock by Beaudry and Lucke is based on the finding that at very long forecasting horizons (60 quarters), longer than those shown in the graphs, it explains a large fraction (60%) of the forecasting error variance of TFP. (In the artificial economy, given the calibration of Beaudry and Lucke, the anticipated TFP shock explains 99% of the FEV of TFP for horizons greater than 8 quarters.) It follows that one needs to show that the ID1 scheme also picks up a similar share of the variance of these other variables as is true in population. The horizontal axis of each panel of figure 2 shows the forecasting horizon, which takes values between 1 and 32 quarters; the vertical axis measures the share of variance explained by the particular shock considered. The solid line corresponds to the FEVD implied by the structural vector error correction model (SVECM), and the dotted line corresponds to the population FEVD implied by the log-linearized approximation to the calibrated model. If the identification strategy were perfect, the solid line and the dotted line should be identical to each other. The figure shows that the SVECM delivers FEVDs that are very close to the population ones and hence suggests that the SVECM, with identification scheme ID1, is able to identify the contribution of all four structural shocks quite closely—as argued by Beaudry and Lucke. In particular, in the theoretical model most of the variance of hours of work, the measure of economic activity used by Beaudry and Lucke, at short horizons is due to the preference shock, $$\varepsilon _{\psi ,t}$$, and $$\varepsilon ^{4}_{t}$$ of the VECM reproduces this fact. Further, at longer forecasting horizons the most important source of fluctuations in hours are 8-quarter anticipated TFP shocks and the SVECM-identified innovation, $$\varepsilon ^{3}_{t}$$, is consistent with this feature of the theoretical model.Fig. 2. Forecast error variance decompositions in the baseline model: theoretical versus VECM estimates. Solid lines show the share of the forecasting error variance for horizons 1–32 quarters due to $$\varepsilon ^{i}_{t}$$, for i = 1, 2, 3, 4, which are the error terms identified with scheme ID1 by estimating a VECM on artificial time series simulated from the calibrated theoretical model. Dotted lines show the population forecasting error variance shares due to the true structural shocks εA,t, εN A,t, εZ,t, and εψ,t, respectively, and were computed from the log-linear approximation to the baseline model.View Large ImageDownload PowerPointI next consider a small variation in the model to see how well the SVECM methodology identifies the structural shocks in a slightly more complicated but empirically equally realistic environment. The only change I introduce is that the relative price of investment now is also subject to anticipated disturbances. And to keep it similar to the structure assumed by Beaudry and Lucke for anticipated TFP shocks, I will assume that the innovations to the investment price growth rate are also anticipated 8 quarters. Formally, this yields a process for the relative price of investment of the form where $$\varepsilon _{NZ,t-8}$$ denotes the 8-quarter anticipated innovation to the growth rate of investment. The innovation $$\varepsilon _{NZ,t-8}$$ enters the information set of private agents in period $$t-8$$ and thus will lead to changes in the endogenous variables included as observables, namely, the of hours and the growth rate of the stock in period but will only in an observed change in the price of investment 8 agents about I the structural of the model as only the standard of the shocks as follows: and _{\psi Under this calibration of the relative TFP is in equal due to and anticipated shocks, and the same is true for the relative price of investment. As we have in the stock prices to TFP shocks the assumed and hence stock prices will almost in equal be explained by and anticipated TFP I this calibration so that hours are in the long almost in equal driven by all shocks. It out that under this calibration in the short preference shocks are the most important source of As I artificial time series of the first and subject each of the data to the SVECM with the ID1 identification scheme and the that there are structural shocks and the VECM methodology only can identify it is less what the identification restrictions will Identification assumption A1 of Beaudry and Lucke imposes that $$\varepsilon ^{1}_{t}$$ is the only shock TFP suggesting that it identifies $$\varepsilon identification assumption 2, $$\varepsilon ^{4}_{t}$$ cannot have a long-run effect on TFP, thus only the possibility that it is $$\varepsilon $$\varepsilon or $$\varepsilon _{\psi ,t}$$, or a combination identification assumption B1 that $$\varepsilon ^{3}_{t}$$ $$\varepsilon ^{4}_{t}$$ have a contemporaneous effect on the price of investment. It follows that $$\varepsilon ^{2}_{t}$$ is to identify $$\varepsilon and $$\varepsilon ^{4}_{t}$$ cannot have a long-run effect on TFP, only $$\varepsilon ^{3}_{t}$$ has a of $$\varepsilon this leaves $$\varepsilon ^{4}_{t}$$ to identify either $$\varepsilon or $$\varepsilon _{\psi or some combination 3 the FEVD As in figure 2, each panel with a solid line the of the FEVD from applying the VECM methodology to the artificial data and with a dotted line the population FEVD implied by the theoretical model. The figure shows that in this economy, it is no longer the case that the structural identified by of the VECM methodology identify the structural shocks of the RBC model The VECM methodology delivers large in the FEVD of TFP, the price of investment, and in particular to news shocks. in this example, it that the share of variations in TFP explained by anticipated TFP shocks is estimated by the VECM methodology to be than the population most important, the figure shows that the of the contribution of news TFP shocks and news investment price shocks to economic activity, identified using the VECM is very different from the true or population The VECM methodology to that the share of anticipated investment-specific shocks in the FEV of the relative price of investment is The VECM assigns equal importance to the anticipated TFP shock and the anticipated investment-specific shock in the FEV of the relative price of investment. This case provides an of a in which the VECM methodology to identify the importance of sources of business Forecast error variance decompositions in model with anticipated investment-specific theoretical versus VECM estimates. Solid lines show the share of the forecasting error variance for horizons 1–32 quarters due to $$\varepsilon ^{i}_{t}$$, for i = 1, 2, 3, 4, which are the error terms identified with scheme ID1 by estimating a VECM on artificial time series simulated from the calibrated theoretical model. Dotted lines show the population forecasting error variance shares due to the true structural shocks εA,t, εN A,t, εZ,t, and εψ,t, respectively, and were computed from the log-linear approximation to the theoretical model with anticipated investment-specific Large ImageDownload FEVD from the VECM shown in my fig. 2 is the of FEVDs on simulated data with observations The simulated time series are and I the first I the calibration of Beaudry and Lucke by = β = = = = = = = = = and _{\psi I measure the stock price as the of the the of the be denoted by output by by and the of by stock prices can be as Identification and than VECM and true FEVD one could check for identification by whether the theoretical model with anticipated shocks to a in the the baseline model without anticipated investment-specific shocks shown in figure 2. that even figure 2 contains differences between the true population variance decompositions and those implied by the SVECM This could be due to or due to the fact that the particular theoretical model to have a of the assumed in the VECM analysis and given in and of the of the Beaudry and Lucke In particular, yt the vector of observables, that is, the of TFP, the of the relative price of investment, the of hours, and the of the stock the VECM analysis is the assumption that the vector yt has a A log-linear approximation to the of the theoretical model takes the form where is a 4 matrix a of the variables, denoted to the vector of state variables, denoted which in of and variables and has The state vector over time to \varepsilon where is an matrix and an 4 The 4 1 vector contains the four structural shocks. In the $$\varepsilon _{t}=[ \varepsilon _{\psi A over a denotes from the would be to the vector of observables of the growth rate of TFP, the growth rate of the price of investment, the growth rate of the stock and the of the of hours, that is, first a strategy to whether there a for in which the errors are indeed One can this question by applying the methods for example, et al. But the to this question should be for in the theoretical model there is a between the of the stock the price of investment, and TFP. Therefore, the differences of these variables, that is, should not have a This is the reason all Beaudry and Lucke a VECM rather than a model in consider the following vector of of four observables, where denotes the in which is given by Then is and is equal to where is the of In this case, we were able to show that has a then we would conclude that the of the observables also have a and thus we would have shown that indeed estimates from a VECM model should be able to recover the true structural shocks, et al. a model with this structure is that is, has a of the form L) only all the of the matrix ( are less than one in I a check of this for the model under and find that the is In particular, I find that more than of this matrix are greater than thus that the model to have a But in the of it is to interpret the of the VECM as the true shocks the model that the fact that we have four observables and four structural shocks and further that the matrix is reason for the of could be the large of state variables that an 8-quarter anticipated innovation is considered. If this were the case, this would support the that VECM methods are not well to identify news shocks. I this by the anticipated innovation to TFP by Then the model is driven by shocks only and we have $$\varepsilon _{t}=[ \varepsilon _{\psi To have any of the model a in we to thus consider only I hours from the vector of observables and set For this I the and I first check whether $$ is and find that it I then as before following et al. ( and I find that all are less than one in It follows that the model without news shock has a in and therefore the VECM methodology should be able to identify the true structural shocks. I stock prices from the vector of observables and set I can show that the theoretical model is that is, it has a in These results that at least in the it is the of news shocks that led to the of the I these findings as further evidence that VECM methods may not be well to the identification of news shocks even in where they a valid identification of unanticipated could also the preference shock, $$\varepsilon _{\psi ,t}$$, and $$\varepsilon _{t}=[ \varepsilon one can show that the theoretical model to have a for the case that the observables are $$ as well as for the case that for the of Anticipated the to the identification of news shocks by of SVAR/VECM methods that I have some recent authors have alternative empirical to estimate the importance of anticipated shocks as a source of business cycles. and (2008), for example, that methods a to the estimation of the importance of anticipated shocks. methods the that the empirical literature on the importance of news shocks has for it does not the dynamic stochastic general equilibrium model to have a in the is, it can be even methods allow to estimate what of anticipated shock is important we the VECM could not TFP and anticipated investment-specific and they allow to estimate how quarters in the main of business are In the VECM all we have is the between an innovation that the fundamental contemporaneously unanticipated and innovations that are and that will affect the fundamental in the anticipated But the VECM methodology is by about the In and we a structural Bayesian estimation of the contribution of anticipated shocks to business in the United in the context of an RBC model, which is slightly more than that by Beaudry and Lucke. assume four real investment in consumption, and in and allow business to be driven by permanent and neutral productivity shocks, permanent investment-specific shocks, and shocks. of these is by four of structural unanticipated innovations and innovations anticipated 1, 2, and 3 quarters in find that anticipated shocks account for more than of aggregate 1 estimation uses U.S. data on hours, investment, consumption, and the relative price of investment for the period which is very similar to the period in Beaudry and Lucke. 1 shows that to of the population variance of hours is due to anticipated shocks. further show that the forecasting error variance of hours explained by anticipated shocks with the forecasting horizon from 20% at a forecasting horizon of 2 quarters to 60% at a forecasting horizon of 32 which is similar to the in Beaudry and Lucke. 2 the decomposition of forecasting error at horizon 32 quarters by Beaudry and Lucke and those by and are differences between the two the most important one that Beaudry and Lucke an VECM whereas and using Bayesian methods, a dynamic stochastic general equilibrium model. use U.S. data on the price of investment, consumption, investment, and hours. Beaudry and Lucke use in data on TFP, stock prices, and Further, Beaudry and Lucke allow one measure of aggregate activity to the estimated system at the In and information on investment, consumption, and hours is used at the same In and use data on 2 shows that these differences the estimated shares of forecast error explained by anticipated shocks are rather similar the two that anticipated technology shocks explain the majority of short-run fluctuations in U.S. time 1 Share of by Anticipated Shocks and (2008), table 2 Share of of Error to Anticipated and and decompositions for the labeled Beaudry and Lucke are based on VECM estimation and should the information in fig. of Beaudry and Lucke. decompositions for the labeled and are from table of and These authors report FEVD for growth rates, with the of hours, which is in and FEVD at the of the of the estimated structural and and in Carnegie-Rochester Conference Series on Public in and of in of and of in and Shocks and the Business U.S. NBER in Primiceri, and Shocks and Business in and in Business Columbia in and and in U.S. Business A Bayesian in Previous articleNext article by NBER by the of on this are from by the of the following articles this of of and for

Large body of empirical literature points to the tight integration of financial and credit markets with real economic activity as well as the need for inclusion of financial frictions into macroeconomic modelling. After the recent mortgage loan crisis which spilled over into the global financial crisis, the assessment of relationship of monetary policy and house prices gained in importance. The aim of this article is to test the compliance of monetary policy shock in calibrated dynamic stochastic general equilibrium (DSGE) model which includes financial frictions with the empirical impact of monetary policy shock in Croatia estimated using vector autoregression (VAR) model. After the DSGE model is calibrated, the VAR model is estimated for Croatia. The comparative analysis of impulse response functions of DSGE and VAR model is conducted. In both models, monetary policy shock has positive initial impact on interest rate and negative initial impact on house prices and output gap. Results indicate that empirical impact of the monetary policy shock adequately reflects the impact of monetary shock in DSGE model with financial frictions.

In this paper, we document the forecasting performance of estimated basic dynamic stochastic general equilibrium (DSGE) models and compare this to extended versions which consider alternative expectation formation assumptions and financial frictions. We also show how standard model features, such as price and wage rigidities, contribute to forecasting performance. It turns out that neither alternative expectation formation behaviour nor financial frictions can systematically increase the forecasting performance of basic DSGE models. Financial frictions improve forecasts only during periods of financial crises. However, traditional price and wage rigidities systematically help to increase the forecasting performance.

2019년 근로장려세제 확대로 수혜가구가 전체 가구의 20%를 차지하면서 향후 이루어지는 제도 개편은 경제 전체에 미치는 효과가 커질 것으로 예상된다. 따라서 앞으로 근로장려세제의 경제적 효과를 분석하는데는 이질적인 경제주체들의 생애주기를 고려한 일반균형(HA-LC-DSGE)모형의 역할이 중요해질 것으로 판단된다. 본 논문에서는 근로장려세제 개편과정을 정리한 뒤, 근로장려세제 관련 선행연구들을 노동공급과 소득재분배에 대한 효과로 나누어 살펴보고, HA-LC-DSGE 모형을 이용한 정책효과 분석의 필요성을 제시한다. 마지막으로 근로장려세제 확대에 관한 정책실험을 통해 HA-LC-DSGE 모형을 이용한 분석방법을 예시적으로 보여주고, 모형 구축과 결과분석 시 고려해야 될 요소들을 설명한다.\n\nAs the Earned Income Tax Credit(EITC) massively expanded in 2019, 20% of total households benefit from the credit. Due to this large reform, we expect that any future reform will also largely affect the aggregate economy; thus, the heterogeneous agent life cycle dynamic stochastic general equilibrium (HA-LC-DSGE) model will be widely used in future research. This paper reviews the EITC reforms in Korea since the first implementation and surveys the previous studies, examining the labor supply effects. We address why the HA-LC-DSGE model is necessary to examine the current EITC system in Korea. Then, we provide an example of the HA-LC-DSGE model with a policy simulation of the EITC expansion and explain the model&amp;apos;s salient ingredients to understand the results.

ABSTRACTFirst-generation dynamic stochastic general equilibrium (DSGE) models have been criticized for their lack of financial markets but, more perceptively, for their barter properties. This note explains why the second of these criticisms is fundamental. All DSGE models are built on frictionless, perfect barter, Walrasian microeconomic foundations. Introducing money and banks into such models converts them into a ‘friction’ contra the fundamental principle that monetary exchange is more efficient than barter. This insoluble difficulty with the microeconomic foundations of DSGE models arises because theorists ignore the Hahn problem that applies to all monetary models based on Walrasian general equilibrium (GE) microeconomic foundations. The Hahn problem reveals three things. First, a perfect barter GE solution always exists in any ‘monetary’ model erected on Walrasian GE microeconomic foundations. Second, inessential monetary features are easily attached to perfect barter microeconomic foundations but as easily removed, leaving the perfect barter solution intact. Third, attaching such inessential additions leads to logical error; the misuse of language that produces invalid conclusions. A second-generation DSGE model that is intended to increase understanding of financial crises is then examined to show that it suffers from the Hahn problem; it converts banking and financial markets into ‘frictions’, and words and economic concepts take on different meanings. That renders the new DSGE model impossible to interpret or use as a basis for advice on monetary policy.

This article discusses the evolution of dynamic macroeconomic models from calibrated Real Business Cycle models to estimated dynamic stochastic general equilibrium models. The purpose is to suggest the usefulness of these models as a tool for policy analysis, with a particular emphasis on aspects of monetary policy. (JEL classification: D58, E50) This article gives an overview of the literature that has led to the emergence of dynamic stochastic general equilibrium (DSGE) models. This approach to macroeconomic modelling has gained widespread support among researchers and has recently started to be taken seriously by policy-making institutions as a modelling framework which is useful for policy analysis and the conceptual support of decision making. Modern macroeconomics is the result of an intense, and at times passionate, scientific debate that has taken place over the last decades. In the early 1980s, a new approach to the business cycle analysis was introduced by Kydland and Prescott (1982). The main tenet of their approach was that a small model of a frictionless and perfectly competitive market economy, inhabited by utility-maximising rational agents which operate subject to budget constraints and technological restrictions, could replicate a number of stylised business cycle facts when hit by random productivity shocks. This so-called real business cycle (RBC) approach to macroeconomic modelling was early on criticised on various aspects. Nevertheless, as it is now widely acknowledged, the RBC agenda has made a lasting methodological contribution. Most of today’s DSGE models

Dynamic Stochastic General Equilibrium (DSGE) models have become the&#x0D; standard framework for quantitative macroeconomic analysis in the world. Naturally,&#x0D; there is now a growing literature on DSGE models designed to study the&#x0D; Brazilian economy. A conference organized by the Centro Macro Brasil of the&#x0D; Sao Paulo School of Economics – FGV, sponsored by the Instituto de Pesquisa&#x0D; Econˆomica Aplicada (IPEA), on August 22, 2014 featured papers using DSGE&#x0D; models applied to Brazil. This special issue of the Brazilian Review of Econometrics&#x0D; contains five papers presented in the conference.&#x0D; The Brazilian Central Bank (Banco Central do Brasil) has its own DSGE&#x0D; model, the so-called SAMBA. The paper that presents and analyses the model,&#x0D; by Marcos de Castro, Solange Gouvea, Andr´e Minella, Rafael Santos and Nelson&#x0D; Souza-Sobrinho, leads this special issue. SAMBA is a large scale DSGE model&#x0D; with a few features designed to bring it closer to the Brazilian economy, namely,&#x0D; the presence of administered prices, an explicit target for the primary surplus,&#x0D; a fraction of households with no access to financial markets, external finance of&#x0D; imports, and imports used as inputs in the production function. SAMBA can be&#x0D; used as a tool for forecasting and for assessing the impact of different shocks.&#x0D; The second paper in this volume, by Fabio Kanczuk, shares the same objectives&#x0D; but employs a medium scale DSGE model of a small open economy. The model is&#x0D; then estimated to understand which shocks can explain the observed fluctuations&#x0D; in output in the last 15 years. The model is also used to assess the economic&#x0D; impacts of a hypothetical currency depreciation and to check the hypothesis that&#x0D; monetary policy has become more powerful over time in Brazil.&#x0D; The next paper in this volume, by Marco Cavalcanti and Luciano Vereda,&#x0D; builds a DSGE framework with a rich modeling of the public sector that explicitly&#x0D; considers public employment as well as other types of public expenditures, public&#x0D; investments and transfers. The model also incorporates a fairly detailed fiscal apparatus&#x0D; comprising several policy instruments both on the taxation and spending&#x0D; sides, and considers different fiscal rules. The model is thus able to quantify the&#x0D; macroeconomic effects of shocks to different types of fiscal policy in the short and&#x0D; medium run.&#x0D; The fourth paper in this volume, by Vladimir Teles, Celso Costa J´unior and&#x0D; Rafael Rosa, presents a DSGE model with two sectors that incorporates technical progress in the investment goods sector. This is motivated by evidence of the&#x0D; importance of this channel that they also document in the paper. They show&#x0D; that incorporating productivity shocks specific to the investment goods sector in&#x0D; the model affects the results in important ways. In particular, optimal monetary&#x0D; policy is more rigorous than in standard models.&#x0D; Most DSGE models applied to the Brazilian economy do not use data from the&#x0D; periods preceding the adoption of the inflation targeting regime in 1999. In the&#x0D; last paper of this volume, Carlos Carvalho and Andr´e Vilela build a DSGE model&#x0D; to investigate the transition between the different exchange rate (and monetary&#x0D; policy) regimes that took place in 1999. Their results support the transition to&#x0D; the inflation targeting regime in 1999, but suggest that an earlier transition in the&#x0D; first half of 1998 might have been even better.&#x0D; The papers in this special issue highlight the main advantages of the use of&#x0D; DSGE models for quantitative macroeconomic analysis. As put by Kanczuk, a&#x0D; DSGE model “forces one to think in terms of exogenous shocks and endogenous&#x0D; responses, and thus to ask sensible questions”. Castro et al. add that DSGE&#x0D; models “can be successfully used as a story-telling device in the policymaking&#x0D; process.” We hope that by bringing together a number of papers applying the&#x0D; DSGE methodology to study the Brazilian macro economy, this volume serves&#x0D; both as a useful guide to and as an inspiration for researchers interested in working&#x0D; in this area.