Evaluating Conditional Forecasts from Vector Autoregressions

Todd Clark and Michael McCracken

No 2014-25, Working Papers from Federal Reserve Bank of St. Louis

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.

Keywords: Prediction; forecasting out-of-sample (search for similar items in EconPapers)
JEL-codes: C12 C32 C52 C53 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2014-09-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Citations: View citations in EconPapers (12)

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Working Paper: Evaluating Conditional Forecasts from Vector Autoregressions (2014)
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DOI: 10.20955/wp.2014.025

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