Todd E. Clark
Michael W. McCracken

tests of predictive ability for vector autoregressions used for conditional forecasting (replication data)

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.

Data and Resources

Suggested Citation

Clark, Todd E.; McCracken, Michael W. (2017): Tests of Predictive Ability for Vector Autoregressions Used for Conditional Forecasting (replication data). Version: 1. Journal of Applied Econometrics. Dataset.