Inflation Targeting and Relative Price Variability: What Difference Does Inflation Targeting Make?

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.

Comment

T he consequences of the decisions and policies taken for the reconstruction of Afghanistan during the past decade, showed that there should be paid more attention to the monetary policies. There is also a question that whether monetary policies have the potential to affect the production and inflation in Afghanistan or not. The aim of this paper is to explore this effect by designing a new Keynesian dynamic stochastic general equilibrium (DSGE) model. The DSGE model used in this paper is taken from the new Keynesian theoretical foundations. It has been addressed to the role of households, firms, the monetary policy of Da Afghanistan Bank, as well as aids of international institutions as shocks that have been able to allocate a particular role in the Afghanistan's economy to itself, with regard to nominal rigidities and monopoly competition in this model. The results show the impulse response functions of production is negative to the external aid shock, financial shock, exchange rate shock, technology shock and external shock and is positive to the monetary shock and cost-shock. An impulse response function of inflation is positive for all shocks except for technology shock.

In this paper, we develop a small open economy New Keynesian dynamic stochastic general equilibrium (DSGE) model to understand the relative importance of two key technology shocks, Hicks neutral total factor productivity (TFP) shock and investment specific technology (IST) shock for an emerging market economy like India. In addition to these two shocks, our model includes three demand side shocks such as fiscal spending, home interest rate, and foreign interest rate. Using a Bayesian approach, we estimate our DSGE model with Indian annual data for key macroeconomic variables over the period of 1971–2010, and for subsamples of pre-liberalization (1971–1990) and post-liberalization (1991–2010) periods. Our study reveals three main results. First, output correlates positively with TFP, but negatively with IST. Second, TFP and IST shocks are the first and the second most important contributors to aggregate fluctuations in India. In contrast, the demand side disturbances play a limited role. Third, although TFP plays a major role in determining aggregate fluctuations, its importance vis-à-vis IST has declined during the post liberalization era. We find that structural shifts of nominal friction and relative home bias for consumption to investment in the post-liberalization period can account for the rising importance of the IST shocks in India.

PurposeThis paper seeks to explore the sensitivity of these parameters and their impact on fiscal policy outcomes. We use the existing literature to establish possible ranges for each parameter, and we examine how changes within these ranges can alter the outcomes of fiscal policy. In this way, we aim to highlight the importance of these parameters in the formulation and evaluation of fiscal policy.Design/methodology/approachThe role of fiscal policy, its effects and multipliers continues to be a subject of intense debate in macroeconomics. Despite adopting a New Keynesian approach within a macroeconomic model, the reactions of macroeconomic variables to fiscal shocks can vary across different contexts and theoretical frameworks. This paper aims to investigate these diverse reactions by conducting a sensitivity analysis of parameters. Specifically, the study examines how key variables respond to fiscal shocks under different parameter settings. By analyzing the behavioral dynamics of these variables, this research contributes to the ongoing discussion on fiscal policy. The findings offer valuable insights to enrich the understanding of the complex relationship between fiscal shocks and macroeconomic outcomes, thus facilitating informed policy debates.FindingsThis paper aims to investigate key elements of New Keynesian Dynamic Stochastic General Equilibrium (DSGE) models. The focus is on the calibration of parameters and their impact on macroeconomic variables, such as output and inflation. The study also examines how different parameter settings affect the response of monetary policy to fiscal measures. In conclusion, this study has relied on theoretical exploration and a comprehensive review of existing literature. The parameters and their relationships have been analyzed within a robust theoretical framework, offering valuable insights for further research on how these factors influence model forecasts and inform policy recommendations derived from New Keynesian DSGE models. Moving forward, it is recommended that future work includes empirical analyses to test the reliability and effectiveness of parameter calibrations in real-world conditions. This will contribute to enhancing the accuracy and relevance of DSGE models for economic policy decision-making.Originality/valueThis study is motivated by the aim to provide a deeper understanding of the roles macroeconomic model parameters play concerning responses to expansionary fiscal policies and the subsequent reactions of monetary authorities. Comprehensive reviews that encompass this breadth of relationships within a single text are rare in the literature, making this work a valuable contribution to stimulating discussions on macroeconomic policies.

Does the exchange rate pass-through into prices change when inflation targeting is adopted? The Peruvian case study between 1994 and 2007

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.

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.

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.

Monetary policy and commodity terms of trade shocks in emerging market economies

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.

We test a standard DSGE (Dynamic Stochastic General Equilibrium) model on impulse responses of hours worked and real GDP after technology and non-technology shocks in emerging market economies (EMEs). Most dynamic macroeconomic models assume that hours worked are stationary. However, in the data, we observe apparent changes in hours worked from 1970 to 2013 in these economies. Motivated by this fact, we first estimate a structural vector autoregression (SVAR) model with a specification of hours in difference (DSVAR) and then set up a DSGE model by incorporating permanent labour supply (LS) shocks that can generate a unit root in hours worked, while preserving the property of a balanced growth path. These LS shocks could be associated with very dramatic changes in LS which look permanent in these economies. Hence, the identification restriction in our models comes from the fact that both technology and LS shocks have a permanent effect on GDP yet only the latter shocks have a long-run impact on hours worked. For inference purposes, we compare empirical impulse responses based on the EMEs data to impulse responses from DSVARs run on the simulated data from the model. The results show that a DSGE model with permanent LS shocks that can generate a unit root in hours worked is required to properly evaluate the DSVAR in EMEs as this model is able to replicate indirectly impulse responses obtained from a DSVAR on the actual data. JEL Classification: C32, E32

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.