Abstract Comparative ex-ante prediction experiments over expanding subsamples are a popular tool for the task of selecting the best forecasting model class in finite samples of practical relevance. Flanking such a horse race by predictive-accuracy tests, such as the test by Diebold and Mariano...

Abstract Comparative ex-ante prediction experiments over expanding subsamples are a popular tool for the task of selecting the best forecasting model class in finite samples of practical relevance. Flanking such a horse race by predictive-accuracy tests, such as the test by Diebold and Mariano...

Nonlinear time series models have become fashionable tools to describe and forecast a variety of economic time series. A closer look at reported empirical studies, however, reveals that these models apparently fit well in-sample, but rarely show a substantial improvement in out-of-sample...

Different studies provide surprisingly a large variety of controversial conclusions aboutthe forecasting power of an indicator, even when it is supposed to forecast the sametime series. In this study we aim to provide a thorough overview of linear forecastingtechniques and draw conclusions...

This study investigates usefulness of business tendency surveys in industrial sector for out-of-sample prediction of growth of industrial production in Russia. A special attention is paid to performance of survey-augmented models during the recent Great Recession 2008/2009. Using the real-time...

This paper discusses pooling versus model selection for now- and forecasting in the presence of model uncertainty with large, unbalanced datasets. Empirically, unbalanced data is pervasive in economics and typically due to different sampling frequencies and publication delays. Two model classes...

Business tendency survey indicators are widely recognized as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified...

Different studies provide surprisingly a large variety of controversial conclusions aboutthe forecasting power of an indicator, even when it is supposed to forecast the sametime series. In this study we aim to provide a thorough overview of linear forecastingtechniques and draw conclusions...

A procedure that has been used at the Swiss National Bank for selecting vector-autoregressive (VAR) models in order to forecast Swiss consumer price inflation is presented. In order to examine and improve the quality of the procedure, it is submitted to several modifications and the results are...

This paper discusses pooling versus model selection for now- and forecasting in the presence of model uncertainty with large, unbalanced datasets. Empirically, unbalanced data is pervasive in economics and typically due to di¤erent sampling frequencies and publication delays. Two model classes...