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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 em-pirical evidence on tests of predictive ability for conditional forecasts from estimated models. In the empirical analysis, we consider forecasts of growth, unemployment, and ination from a VAR, based on conditions on the short-term interest rate. Through-out the analysis, we focus on tests of bias, e ¢ ciency, and equal accuracy applied to conditional forecasts from VAR models.

Version révisée/revised: Juin/June 2014 This paper tackles the prediction of the probability and severity of US recessions. We employ parsimonious Probit models to estimate the probability of a recession h periods ahead, for h varying between 1 and 8 quarters. A novel goodness-of-fit measure derived from the Kullback-Leibler Information Criterion is developed and used to select the regressors to include in the Probit models. Next, an autoregression (AR) augmented with inverse Mills ratio (IMR) and diffusion indices (DI) is fitted to selected measures of real economic activity. The resulting “IMR-DI-AR ” model is used to generate forecasts conditional on optimistic and pessimistic scenarios for the horizon of interest. The severity of recessions is defined as the gap between the pessimistic scenario and the recent trend of the series. For a time series of GDP growth, our measure of recession severity has the interpretation of the output loss. Our results support that U.S. recessions are predictable to a great extent, both in terms of occurrence and severity. All recessions are not alike: some are more predictable than others while some are more severe than expected.