Methods to estimate dynamic stochastic general equilibrium models

The article presents a dynamic stochastic general equilibrium model (DSGE-model) for the Russian economy. The model describes the behavior of the following macroeconomic agents: households, real sector, banking sector, Central Bank, as well as the interactions between them and the world. Household modeling uses the external habit formation approach to account for the inertia of preferences. To model the real sector, we abandoned the most common approach which assumes that the decision on investments is made by the households as the owners of production factors. Instead, we took the firm-specific capital approach which assumes that the decision on investment is made by the firms themselves. The study also considers that in Russia, fixed assets are mostly invested from the firms' own funds. To account for the investment inertia in the fixed asset in a real sector model, the expenditures are transferred to the commissioning of new facilities, the Calvo model is applied to describe the price setting under the monopolistic competition. A banking sector which defines the loan and debt interest rates to the key Central Bank interest rate is chosen to be a link between the households and firms in the model. The Taylor equation is used to describe the monetary policy of the Bank of Russia under the inflation targeting, while an inertia factor is included into the equation with the uncovered interest parity for the budget rule which regulates the purchases (or sales) of the currency by the National Welfare Fund. The final linearized model is a system of 23 difference equations with rational expectations. Based on the proposed model, calculations were made and key macroeconomic indicators were forecasted for 2020–2021 on a quarterly basis for the Russian economy. The calculations account for the relevant recessionary factors: oil price fall, oil production cut in OPEC+ deals, quarantine measures aimed to prevent the spread of the corona virus infection, anti-recessionary measures of the RF Government. The findings show that the economic downturn in 2020 can be from 5 to 7% under COVID-19 pandemic. Growth in 2021 is estimated to be within 3–5%. The developed model can be used for scenario projecting for the Russian economy, upgrading the monetary policy of the Bank of Russia, and for developing applied quarterly projection models (QPM). The model could be further modified by including more elements: decomposing the household sector into the Ricardian and non-Ricardian ones, identifying the resources industries and industries in the real sector which manufacture the invested goods, including the key taxes and budget expenses into the model. One more promising area is to analyze the equilibrium of the interest rates when large firms could accumulate their own financial resources. This prerequisite decreases the demand for the bank loans from the real sector and, thus, leads to lower, including the negative, interest rates. The proposed approach enhances the quality of a DSGE model as a predictive tool for making the political and managerial decisions.

The global financial crisis has sparked renewed debate over the state of macroeconomic modeling, particularly in the lead up to the 2008/2009 Great Recession. The standard workhorse of macroeconomic modeling, the Dynamic Stochastic General Equilibrium (DSGE) model, has been subject to intensive scrutiny. Over the past decade, there has been significant increase in the use of DSGE models by central banks for policy analysis, forecasting and prescriptions. The majority of central banks from developed countries have established DSGE models, including the Federal Reserve Bank, the European Central Bank, the IMF and the Bank of England. Given their prevalence among central banks coupled with their use by policy makers for analysis and forecasting, the objective of this research paper is to assess the behavior and forecasts made by these DSGE models in the run up to a financial crisis. A DSGE model is estimated for the United States for the pre-crisis period Q1.1947 to Q4.2007. An empirical verification of the data is undertaken, whereby forecasts made by the DSGE models are compared with the observed post-crisis data. We find that the DSGE model does a poor job of forecasting the Great Recession, and gives no indication that a downturn is imminent in the economy. Within the current paradigm, there is no role for financial frictions. As such, we suggest that the building blocks of DSGE models are too simplistic to effectively model key dynamics within the economy. We use the role of debt accumulation by US households as a means of illustrating this. The global financial crisis has sparked renewed debate over the state of macroeconomic modeling, particularly in the lead up to the 2008/2009 Great Recession. The standard workhorse of macroeconomic modeling, the Dynamic Stochastic General Equilibrium (DSGE) model, has been subject to intensive scrutiny. Over the past decade, there has been significant increase in the use of DSGE models by central banks for policy analysis, forecasting and prescriptions. The majority of central banks from developed countries have established DSGE models, including the Federal Reserve Bank, the European Central Bank, the IMF and the Bank of England. Given their prevalence among central banks coupled with their use by policy makers for analysis and forecasting, the objective of this research paper is to assess the behavior and forecasts made by these DSGE models in the run up to a financial crisis. A DSGE model is estimated for the United States for the pre-crisis period Q1.1947 to Q4.2007. An empirical verification of the data is undertaken, whereby forecasts made by the DSGE models are compared with the observed post-crisis data. We find that the DSGE model does a poor job of forecasting the Great Recession, and gives no indication that a downturn is imminent in the economy. Within the current paradigm, there is no role for financial frictions. As such, we suggest that the building blocks of DSGE models are too simplistic to effectively model key dynamics within the economy. We use the role of debt accumulation by US households as a means of illustrating this.

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's salient ingredients to understand the results.

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 factor models and dynamic stochastic general equilibrium (DSGE) models are widely used for empirical research in macroeconomics. The empirical factor literature argues that the co-movement of large panels of macroeconomic and financial data can be captured by relatively few common unobserved factors. Similarly, the dynamics in DSGE models are often governed by a handful of state variables and exogenous processes such as preference and/or technology shocks. Boivin and Giannoni(2006) combine a DSGE and a factor model into a data-rich DSGE model, in which DSGE states are factors and factor dynamics are subject to DSGE model implied restrictions. We compare a data-richDSGE model with a standard New Keynesian core to an empirical dynamic factor model by estimating both on a rich panel of U.S. macroeconomic and financial data compiled by Stock and Watson (2008).We find that the spaces spanned by the empirical factors and by the data-rich DSGE model states are very close. This proximity allows us to propagate monetary policy and technology innovations in an otherwise non-structural dynamic factor model to obtain predictions for many more series than just a handful of traditional macro variables, including measures of real activity, price indices, labor market indicators, interest rate spreads, money and credit stocks, and exchange rates.

Estimating Nonlinear DSGE Models by the Simulated Method of Moments

<p>This thesis consists of an introduction and three substantive chapters. Chapter 2 explores the identification of a small open economy model. Chapter 3 focuses on the business cycle consequences of migration. And chapter 4 investigates the contribution of investment-specific technology shocks to business cycle fluctuations in the presence of financial frictions. Chapter 2 takes a conventional new open economy macro model for a small open economy and addresses three questions: what data series should be used to identify the parameters of such a model? Are foreign data important for the identification of domestic parameters? And lastly, which structural parameters are interdependent? The chapter illustrates an applied methodology that enables an investigator to understand which data series are informative about parameters. The methodology can also be used to learn about the properties of the model. In particular, the methodology highlights which parameters are connected to which data series. Identification of business cycle models matters because our ability to recover structural parameters is influenced by the data series that are used to inform the estimation. Structural parameters determine both the specification of household preferences and the constraints that affect business cycle volatility, which together determine welfare. Consequently, identification analysis can provide insights into household welfare, which in turn has ramifications for the specification of monetary policy rules. If parameters are identified then the likelihood will eventually outweigh any prior beliefs as the sample size becomes large (Gelman et al., 2004, p. 107). The approach discussed here thus shows whether data will eventually dominate prior beliefs about parameters, determining whether analysis can – in the limit – resolve conflicting prior beliefs, and therefore usefully inform the design of policy rules. Chapter 3 of this thesis examines the business cycle effects that arise from an expansion of the population due to migration. In recent years, migration flows have become a highly politicised topic, both in New Zealand and abroad. While the debate on migration has become heated, comparatively little is known about the business cycle consequences of migration flows. This chapter contributes to the macroeconomic literature by illustrating the contribution that migration shocks make to cyclical fluctuations in New Zealand, and illustrates their dynamic impact. Using an estimated dynamic stochastic general equilibrium (DSGE) model of a small open economy and a structural vector autoregression, the chapter shows that migration shocks account for a considerable portion of the variability of per capita gross domestic product (GDP). While migration shocks matter for the capital investment and consumption components of per capita GDP, other shocks are more important drivers of cyclical fluctuations in these aggregates. Migration shocks also make some contribution to residential investment and real house prices, but other shocks play a more substantial role in driving housing market volatility. In the DSGE model, the level of human capital possessed by migrants relative to that of locals materially affects the business cycle impact of migration. The impact of migration shocks is larger when migrants have substantially different – larger or smaller – levels of human capital relative to locals. When the average migrant has higher levels of human capital than locals, as seems to be common for migrants into most OECD¹ economies, a migration shock has an expansionary effect on per capita GDP and its components, which also accords with the evidence from a structural vector autoregression. Chapter 4 of this thesis investigates the contribution of investment-specific technology (IST) shocks in driving cyclical fluctuations in a closed economy model when a borrowing constraint is introduced à la Kiyotaki and Moore (1997). IST shocks have been identified as a major driver of the business cycle, eg see Greenwood et al. (2000), and Justiniano et al. (2010, 2011). These shocks affect the rate at which investment goods are transformed into capital stock, and have been linked to frictions in financial markets, because financial intermediation is instrumental in facilitating investment. The third chapter shows that the importance of these investment shocks is in fact substantially diminished when collateral constraints on firms are introduced into an estimated dynamic stochastic general equilibrium model. In the presence of binding collateral constraints, risk premium shocks, which perturb interest rates and affect intertemporal substitution, supplant IST shocks as important drivers of the business cycle. ¹ Organisation for Economic Cooperation and Development.</p>

Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility

The Zero Lower Bound (ZLB) on short nominal interest rates has imposed serious constraint on stimulating and stabilizing economy of major central banks. Analysis of monetary policy by Dynamic Stochastic General Equilibrium (DSGE) models under the existence of ZLB has also been an important issue from both practical and academic views for central banks and macroeconomists. However, the nonlinearity of ZLB constraint makes linear solution and estimation techniques of DSGE models unreliable and impractical. In many recent empirical works, it has been proved that the shadow rate can be used as an accurate proxy to represent the stance of unconventional monetary policy in the ZLB environment. We use shadow rate to estimate a medium-scale DSGE model based on the theoretical foundation proposed by Wu and Zhang (2016). A shadow rate New Keynesian model (No. w22856). National Bureau of Economic Research, and conduct counterfactual simulation exercises to quantify the macroeconomic effects of unconventional monetary policy implemented by Bank of Japan (BoJ). Compared with the estimation results of pre-ZLB sub-sample (1980Q1–1998Q4), the structural parameters estimated from full-sample (1980Q1–2016Q3) with the shadow rate still have very reasonable values that are consistent with most related medium-scale DSGE literature. The statistical properties of model dynamics implied by two groups of estimation are also very close. Counterfactual simulation shows that without the unconventional monetary policy, macroeconomic variables would have worse performance than their actual realizations.

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. The book is essential reading for graduate students, academic researchers, and practitioners at policy institutions.

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions.

System reduction of dynamic stochastic general equilibrium models solved by gensys

This volume of Advances in Econometrics contains articles that examine key topics in the modeling and estimation of dynamic stochastic general equilibrium (DSGE) models. Because DSGE models combine micro- and macroeconomic theory with formal econometric modeling and inference, over the past decade they have become an established framework for analyzing a variety of issues in empirical macroeconomics. The research articles make contributions in several key areas in DSGE modeling and estimation. In particular, papers cover the modeling and role of expectations, the study of optimal monetary policy in two-country models, and the problem of non-invertibility. Other interesting areas of inquiry include the analysis of parameter identification in new open economy macroeconomic models and the modeling of trend inflation shocks. The second part of the volume is devoted to articles that offer innovations in econometric methodology. These papers advance new techniques for addressing major inferential problems and include discussion and applications of Laplace-type, frequency domain, empirical likelihood and method of moments estimators.

This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and comparisons to vector autoregressions, as well as the non-linear estimation based on a second-order accurate model solution. These methods are applied to data generated from correctly specified and misspecified linearized DSGE models and a DSGE model that was solved with a second-order perturbation method.

The Bayesian estimation of dynamic stochastic general equilibrium (DSGE) models implies usage of Monte Carlo Markov Chain (MCMC) algorithms. The analysis of MCMC algorithms is made for densities with unfavorable properties common in DSGE models (restricted density support, heavy tails, sharp peaks, non-convex log-density, and non-convex density). Three groups of algorithms are investigated: random walk (RW), MALA and suggested LTG (local truncated Gauss). Three versions of MALA and LTG are investigated: the version using local hessian and gradient of log-density, the version using only local gradient and the version that uses only information about the mode. Performance of MALA and LTG are close to each other. There is some advantage of LTG in average and in test with DSGE model. RW performance is worse than MALA or LTG (especially for small-scale cases). The computational costs are almost the similar for RW and approximation based versions of MALA or LTG. Existence of heavy tails leads to decrease of advantage of MALA and LTG algorithms. Acceptance rate (corresponding to the lowest inefficiency of sample) can be quite different from conventional values.