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Federal Reserve Bank of St. Louis
Working Papers
Evaluating Conditional Forecasts from Vector Autoregressions
Todd E. Clark
Michael W. McCracken
Abstract

Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on methods for evaluating conditional forecasts. This paper provides analytical, Monte Carlo, and empirical evidence on tests of predictive ability for conditional forecasts from estimated models. In the empirical analysis, we consider forecasts of growth, unemployment, and inflation from a VAR, based on conditions on the short-term interest rate. Throughout the analysis, we focus on tests of bias, efficiency, and equal accuracy applied to conditional forecasts from VAR models.


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Todd E. Clark & Michael W. McCracken, Evaluating Conditional Forecasts from Vector Autoregressions, Federal Reserve Bank of St. Louis, Working Papers 2014-25, 01 Sep 2014.
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Keywords: Prediction; forecastingf out-of-sample
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