Recent work has emphasized the importance of evaluating forecasts of a statistical functional (such as a mean, quantile, or distribution) using a loss function that is consistent for the functional of interest, of which there are an in…nite number. If forecasters all use correctly speci…ed models free from estimation error, and if the information sets of competing forecasters are nested, then t...

We discuss some challenges presented by trending data in time series econometrics. To the empirical economist there is little guidance from theory about the source of trend behavior and even less guidance about practical formulations. Moreover, recent proximity theorems reveal that trends are more elusive to model empirically than stationary processes, with the upshot that optimal forecasts are...

The article is devoted to the author’s approach and tools for regional industries’ modeling, analysis forecasting, following general idea of splitting time series into four components: trend, cycles, seasonal component, residuals. However, authors introduce new approaches, models, metrics, identification algorithms, components’ interaction structures, having included 12 industries in 82 regions...

Much research has investigated the differences between option implied volatilities and econometric model-based forecasts in terms of forecast accuracy and relative informational content. Implied volatility is a market determined forecast, in contrast to model-based forecasts that employ some degree of smoothing to generate forecasts. Therefore, implied volatility has the potential to reflect in...

Subjective Probability Forecasts for Recessions Kajal Lahiri is distinguished professor of economics and director of the Econometric Research Institute at the University at Albany, SUNY. He has been a visiting scholar at the Social Security Administration, U.S. Department of Transportation, and the International Monetary Fund. He has received grants and contracts from the National Science Found...

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...