Autoregressive models are used routinely in forecasting and often lead to better performance than more complicated models. However, empirical evidence is also suggesting that the autoregressive representations of many macroeconomic and financial time series are likely to be subject to structural...

Autoregressive models are used routinely in forecasting and often lead to better performance than more complicated models. However, empirical evidence is also suggesting that the autoregressive representations of many macroeconomic and financial time series are likely to be subject to structural...

We develop novel forecasting methods for panel data with heterogeneous parameters and examine them together with existing approaches. We conduct a systematic comparison of their predictive accuracy in settings with different cross-sectional (N) and time (T) dimensions and varying degrees of...

The contingency table literature on tests for dependence among discrete multi-category variables is extensive. Existing tests assume, however, that draws are independent, and there are no tests that account for serial dependencies -- a problem that is particularly important in economics and...

Empirical evidence suggests that many macroeconomic and financial time series are subject to occasional structural breaks. In this paper we present analytical results quantifying the effects of such breaks on the correlation between the forecast and the realization and on the ability to forecast...

In this paper, we explore the potential gains from alternative combinations of the surveyed forecasts in the ECB Survey of Professional Forecasters. Our analysis encompasses a variety of methods including statistical combinations based on principal components analysis and trimmed means,...

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted on the assumption of mean squared error loss under which forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast...

We compare different approaches to accounting for parameter instability in the context of macroeconomic forecasting models that assume either small, frequent changes versus models whose parameters exhibit large, rare changes. An empirical out-of-sample forecasting exercise for U.S. GDP growth...

Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination...