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Estimating and accounting for the output gap with large Bayesian vector autoregressions
Author(s) -
Morley James,
Wong Benjamin
Publication year - 2019
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2733
Subject(s) - econometrics , bayesian vector autoregression , vector autoregression , economics , federal funds , bayesian probability , overfitting , gross domestic product , real gross domestic product , inflation (cosmology) , monetary policy , computer science , macroeconomics , statistics , mathematics , physics , machine learning , theoretical physics , artificial neural network
Summary We consider how to estimate the trend and cycle of a time series, such as real gross domestic product, given a large information set. Our approach makes use of the Beveridge–Nelson decomposition based on a vector autoregression, but with two practical considerations. First, we show how to determine which conditioning variables span the relevant information by directly accounting for the Beveridge–Nelson trend and cycle in terms of contributions from different forecast errors. Second, we employ Bayesian shrinkage to avoid overfitting in finite samples when estimating models that are large enough to include many possible sources of information. An empirical application with up to 138 variables covering various aspects of the US economy reveals that the unemployment rate, inflation, and, to a lesser extent, housing starts, aggregate consumption, stock prices, real money balances, and the federal funds rate contain relevant information beyond that in output growth for estimating the output gap, with estimates largely robust to substituting some of these variables or incorporating additional variables.