Understanding Dsge Filters In Forecasting And Policy Analysis

This paper introduces methods that allow analysts to (i) decompose the estimates of unobserved quantities into observed data, (ii) to better understand revision properties of the model, and (iii) to impose subjective prior constraints on path estimates of unobserved shocks in structural economic models. For instance, a decomposition of the flexible-price output gap, or a technology shock, into contributions of output, inflation, interest rates, and other observed variables' contribution is feasible. The intuitive nature and analytical clarity of the suggested procedures are appealing for policy-related and forecasting models.

This paper introduces methods that allow analysts to (i) decompose the estimates of unobserved quantities into observed data, (ii) to better understand revision properties of the model, and (iii) to impose subjective prior constraints on path estimates of unobserved shocks in structural economic models. For instance, a decomposition of the flexible-price output gap, or a technology shock, into contributions of output, inflation, interest rates, and other observed variables' contribution is feasible. The intuitive nature and analytical clarity of the suggested procedures are appealing for policy-related and forecasting models.

Estimates of potential output are an important ingredient of structured forecasting and policy analysis. Using information on consensus forecasts, this paper extends the multivariate filter developed by Benes and others (2010). Although the estimates in real time are more robust relative to those of naïve statistical filters, there is still significant uncertainty surrounding the estimates. The paper presents estimates for 16 countries and provides an example of how the filtered estimates at the end of the sample period can be improved with additional information.

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.

Estimates of potential output are an important component of a structured forecasting and policy analysis system. Using information on capacity utilization, this paper extends the multivariate filter developed by Laxton and Tetlow (1992) and modified by Benes and others (2010), Blagrave and others (2015), and Alichi and others (2015). We show that, although still fairly uncertain, the real-time estimates from this approach are more accurate than estimates constructed from naïve univariate statistical filters. The paper presents illustrative estimates for the United States and discusses how the end-of-sample estimates can be improved with additional information.

The Research Summaries in the September 2013 IMF Research Bulletin focus on “External Conditions and Debt Sustainability in Latin America” (Gustavo Adler and Sebastian Sosa) and “Monetary Policy Cyclicality in Emerging Markets” (Donal McGettigan, Kenji Moriyama, and Chad Steinberg). In the Q&A, Itai Aigur and Sunil Sharma discuss “Seven Questions on Macroprudential Policy Frameworks.” The Research Bulletin also includes an updated listing of recent IMF Working Papers, Staff Discussion Notes, and Recommended Readings from the IMF Bookstore, as well as information on a forthcoming conference. The IMF Economic Review’s new Impact Factor is also highlighted.

This paper discusses several popular methods to estimate the ‘output gap’. It provides a unified, natural concept for the analysis, and demonstrates how to decompose the output gap into contributions of observed data on output, inflation, unemployment, and other variables. A simple bar-chart of contributing factors, in the case of multi-variable methods, sharpens the intuition behind the estimates and ultimately shows ‘what is in your output gap.’ The paper demonstrates how to interpret effects of data revisions and new data releases for output gap estimates (news effects) and how to obtain more insight into real-time properties of estimators.

Inflation-forecast targeting is state of the art for monetary policy. This book explores first principles, including managing short-term policy trade-offs. The book also outlines efficient operational procedures and reviews the experiences of Canada, the Czech Republic, and India. The analysis highlights the need for assertive policies and maximum transparency.