Model selection for forecast combination

A dynamic factor model framework for forecast combination

A panel of ex-ante forecasts of a single time series is modeled as a dynamic factor model, where the conditional expectation is the single unobserved factor. When applied to out-of-sample forecasting, this leads to combination forecasts that are based on methods other than OLS. These methods perform well in a Monte Carlo experiment. These methods are evaluated empirically in a panel of simulate...

Choosing Among Competing Econometric Forecasts: Regression Based Forecast Combination Using Model Selection

Based on Monte Carlo simulations using both stationary and nonstationary data, a model selection approach which uses the SIC to select a "best" group of forecasts in the context of forecast combination regressions dominates a number of other techniques, including the standard t-statistic approach and the simple averaging rule. Our results are robust across various restricted and unrestricted ve...

Density Forecast Combination

In this paper we investigate whether and how far density forecasts sensibly can be combined to produce a “better” pooled density forecast. In so doing we bring together two important but hitherto largely unrelated areas of the forecasting literature in economics, density forecasting and forecast combination. We provide simple Bayesian methods of pooling information across alternative density fo...

Forecast Evaluation and Combination

Model Combination for Correcting Preposition Selection Errors

Many grammatical error correction approaches use classifiers with specially-engineered features to predict corrections. A simpler alternative is to use n-gram language model scores. Rozovskaya and Roth (2011) reported that classifiers outperformed a language modeling approach. Here, we report a more nuanced result: a classifier approach yielded results with higher precision while a language mod...