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Tests of Predictive Ability for Vector Autoregressions Used for Conditional Forecasting
Author(s) -
Clark Todd E.,
McCracken Michael W.
Publication year - 2016
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.2529
Subject(s) - econometrics , conditional variance , conditional expectation , conditional probability distribution , dynamic stochastic general equilibrium , vector autoregression , economics , computer science , monetary policy , autoregressive conditional heteroskedasticity , macroeconomics , volatility (finance)
Summary 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 examine conditional forecasts obtained with a VAR in the variables included in the DSGE model of Smets and Wouters ( American Economic Review 2007; 97 : 586–606). Throughout the analysis, we focus on tests of bias, efficiency and equal accuracy applied to conditional forecasts from VAR models. Copyright © 2016 John Wiley & Sons, Ltd.