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Evaluating Conditional Forecasts from Vector Autoregressions


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 infl ation 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.

JEL Classification: C53, C52, C12, C32.

Keywords: Prediction, forecasting, out-of-sample.


Suggested citation: Todd E. Clark and Michael W. McCracken, 2014. “Evaluating Conditional Forecasts from Vector Autoregressions,” Federal Reserve Bank of Cleveland, Working Paper no. 14-13.

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