The simulated results of this paper show that New Keynesian DSGE models with capital accumulation can generate substantial persistencies in the dynamics of the main economic variables, due to the stock nature of capital. Empirical estimates on U.S. data from 1960:I to 2008:I show the response of monetary policy to inflation was almost twice lower than traditionally considered, as capital accumulation creates an additional channel of influence through real interest rates in the production sector. Versions of the model with indeterminacy empirically outperform determinate versions. This paper allows for the reconsideration of previous findings and has significant monetary policy implications.

This paper studies New Keynesian DSGE models with capital accumulation, different Taylor rules, and the potential for indeterminacy. It is shown that investment activity creates additional channels for monetary policy influence, which allow for the reconsideration of some of the key findings of previous papers. First, while most canonical Keynesian models fail to replicate the high levels of autocorrelation among primary economic variables, the simulated results of this paper show that models with capital accumulation can, in fact, generate substantial persistencies. Secondly, using a Bayesian approach, this paper estimates different modifications of the models with capital accumulation on U.S. data from 1960:I to 2008:I. The results of this paper differ from the findings of earlier papers. In contrast to Lubik and Schorfheide (2004) and Clarida, Gali and Gertler (2000), this paper finds that the response of monetary policy to inflation is substantially lower, the response of the monetary policy rule to output is higher, household's risk aversion increased substantially over time, and the targeted inflation rate was substantially lower for the post-1982 period. Finally, a Bayesian comparison of the models declared that models with indeterminacy dominate determinate models for various periods of U.S. history.

Ideally, scientific theory and scientific measurement should develop in tandem, but in recent years this has not been the case in economics. There used to be a time when leading economists, or their students, established or led statistical offices and took care that the measurements were consistent with the theory (and vice versa). Not anymore. Macroeconomic theorists and macroeconomic statisticians do not even speak the same language any longer. They do use the same words, such as ‘consumption’, ‘investments’ or ‘unemployment’ but the meanings can often be different. This book maps the differences between macroeconomic theory and measurement and explores them in some detail while also tracking their intellectual, historical and, in some cases, ideological origins. It also explores the possible policy implications. In doing so, the book draws on two separate strands of literature which are seldom used in unison: macro-statistical manuals and theoretical macro-papers. By doing so, the book contributes to the effort to bridge the gap between them without compromising on the idea that a meaningful science of economics should, in the end, be based upon individual people and households and their social and cultural embedding instead of a ‘representative consumer’, or Robinson Crusoe figure. This work is essential reading for students, economists, statisticians, and professionals.

The research summaries in the June 2012 issue of the IMF Research Bulletin are "Public Debt in Advanced Economies and Its Spillover Effects on Long-Term Yields" (by C. Emre Alper and Lorenzo Forni) and "Expansionary Fiscal Contractions: The Empirical Evidence" (by Rina Bhattacharya and Sanchita Mukherjee). The Q&A covers "Seven Questions about Income Inequality" (by Laura Feiveson). Also included in this issue are details on visiting scholars at the IMF, a listing of recently published IMF Working Papers and Staff Discussion Notes, as well as information on IMF Economic Review.

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.

Our answer: Not so well. We reached that conclusion after reviewing recent research on the role of technology as a source of economic fluctuations. The bulk of the evidence suggests a limited role for aggregate technology shocks, pointing instead to demand factors as the main force behind the strong positive comovement between output and labor input measures.

Bayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. This handbook is a single source for researchers and policymakers wanting to learn about Bayesian methods in specialized fields, and for graduate students seeking to make the final step from textbook learning to the research frontier. It contains contributions by leading Bayesians on the latest developments in their specific fields of expertise. The volume provides broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing. It reviews the state of the art in Bayesian econometric methodology, with chapters on posterior simulation and Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized tools used by Bayesian time series econometricians such as state space models and particle filtering. It also includes chapters on Bayesian principles and methodology.