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Monetary Policy in a Data Rich Environment
 Journal of Monetary Economics
, 2002
"... Most empirical analyses of monetary policy have been confined to frameworks in which the Federal Reserve is implicitly assumed to exploit only a limited amount of information, despite the fact that the Fed actively monitors literally thousands of economic time series. This article explores the feasi ..."
Abstract

Cited by 66 (2 self)
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Most empirical analyses of monetary policy have been confined to frameworks in which the Federal Reserve is implicitly assumed to exploit only a limited amount of information, despite the fact that the Fed actively monitors literally thousands of economic time series. This article explores the feasibility of incorporating richer information sets into the analysis, both positive and normative, of Fed policymaking. We employ a factormodel approach, developed by Stock and Watson (1999a,b), that permits the systematic information in large data sets to be summarized by relatively few estimated factors. With this framework, we reconfirm Stock and Watson’s result that the use of large data sets can improve forecast accuracy, and we show that this result does not seem to depend on the use of finally revised (as opposed to “realtime”) data. We estimate policy reaction functions for the Fed that take into account its datarich environment and provide a test of the hypothesis that Fed actions are explained solely by its forecasts of inflation and real activity. Finally, we explore the possibility of developing an “expert system ” that could aggregate diverse information and provide benchmark policy settings. *Prepared for a conference on “Monetary Policy Under Incomplete Information”,
Forecasting in Dynamic Factor Models . . .
, 2003
"... This paper considers the problem of forecasting in dynamic factor models using Bayesian model averaging. Theoretical justifications for averaging across models, as opposed to selecting a single model, are given. Practical methods for implementing Bayesian model averaging with factor models are descr ..."
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This paper considers the problem of forecasting in dynamic factor models using Bayesian model averaging. Theoretical justifications for averaging across models, as opposed to selecting a single model, are given. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms which simulate from the space defined by all possible models. We discuss how these simulation algorithms can also be used to select the model with the highest marginal likelihood (or highest value of an information criterion) in an efficient manner. We apply these methods to the problem of forecasting GDP and inflation using quarterly U.S. data on 162 time series. For both GDP and inflation, we find that the models which contain factors do outforecast an AR(p), but only by a relatively small amount and only at short horizons. We attribute these findings to the presence of structural instability and the fact that lags of dependent variable seem to contain most of the information relevant for forecasting. Relative to the small forecasting gains provided by including factors, the gains provided by using Bayesian model averaging over forecasting methods based on a single model are appreciable.
Forecasting in Large . . .
, 2003
"... This paper considers the problem of forecasting in large macroeconomic panels using Bayesian model averaging. Theoretical justi…cations for averaging across models, as opposed to selecting a single model, are given. Practical methods for implementing Bayesian model averaging with factor models are d ..."
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This paper considers the problem of forecasting in large macroeconomic panels using Bayesian model averaging. Theoretical justi…cations for averaging across models, as opposed to selecting a single model, are given. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms which simulate from the space de…ned by all possible models. We discuss how these simulation algorithms can also be used to select the model with the highest marginal likelihood (or highest value of an information criterion) in an e¢cient manner. We apply these methods to the problem of forecasting GDP and in‡ation using quarterly U.S. data on 162 time series. For both GDP and in‡ation, we …nd that the models which contain factors do outforecast an AR(p), but only by a relatively small amount and only at short horizons. We attribute these …ndings to the presence of structural instability and the fact that lags of dependent variable seem to contain most of the information relevant for forecasting. Relative to the small forecasting gains provided by including factors, the gains provided by using Bayesian model averaging over forecasting methods based on a single model are appreciable.