This paper proposes a framework to implement regression-based tests of predictive ability in unstable environments, including, in particular, forecast unbiasedness and efficiency tests, commonly referred to as tests of forecast rationality. Our framework is general: it can be applied to...

This paper proposes forecast optimality tests that can be used in unstable environments. They include tests for forecast unbiasedness, efficiency, encompassing, serial uncorrelation, and, in general, regression-based tests of forecasting ability. The proposed tests are applied to evaluate the...

We analyze forecasts of car sales in the U.S. and forecasts of car registrations in Japan. We document a substantial heterogeneity of forecasts, and we show that, based on traditional criteria, forecasts are neither rational nor unbiased. We also report that forecasters anti-herd, that is,...

The economic forecasts for Germany in the period 2001 to 2003 grossly missed reality. Forecasters estimated an average annual growth rate of 1.6 per cent, but real GDP actually grew by only 0.3 per cent per annum. In 2003 the real GDP in Germany even shrank by 0.1 per cent. Forecasters tend to...

In this paper we suggest an approach to comparison of models' forecasting performance in unstable environments. Our approach is based on combination of the Cumulated Sum of Squared Forecast Error Differential (CSSFED) suggested earlier in Welch and Goyal (2008) and the Bayesian change point...

The economic forecasts for Germany in the period 2001 to 2003 grossly missed reality. Forecasters estimated an average annual growth rate of 1.6 per cent, but real GDP actually grew by only 0.3 per cent per annum. In 2003 the real GDP in Germany even shrank by 0.1 per cent. Forecasters tend to...

We propose a new methodology to identify the sources of models’ forecasting performance. The methodology decomposes the models’ forecasting performance into asymptotically uncorrelated components that measure instabilities in the forecasting performance, predictive content, and over-fitting....

Imports represent a relevant component of total economic resources. For the Italian case, they mainly consist of raw materials and intermediate goods. In this paper, we evaluate several econometric models performing shorthorizon forecasts of Italian imports of goods. Year-to-year growth rate of...

In this paper we suggest an approach to comparison of models' forecasting performance in unstable environments. Our approach is based on combination of the Cumulated Sum of Squared Forecast Error Differential (CSSFED) suggested earlier in Welch and Goyal (2008) and the Bayesian change point...

We present a first assessment of the predictive ability of machine learning methods for inflation forecasting in Costa Rica. We compute forecasts using two variants of k-nearest neighbors, random forests, extreme gradient boosting and a long short-term memory (LSTM) network. We evaluate their...