Pooling Versus Model Selection for Nowcasting GDP With Many Predictors: Empirical Evidence for Six Industrialized Countries

Abstract

SUMMARY This paper discusses pooling versus model selection for nowcasting with large datasets in the presence of model uncertainty. In practice, nowcasting a low‐frequency variable with a large number of high‐frequency indicators should account for at least two data irregularities: (i) unbalanced data with missing observations at the end of the sample due to publication delays; and (ii) different sampling frequencies of the data. Two model classes suited in this context are factor models based on large datasets and mixed‐data sampling (MIDAS) regressions with few predictors. The specification of these models requires several choices related to, amongst other things, the factor estimation method and the number of factors, lag length and indicator selection. Thus there are many sources of misspecification when selecting a particular model, and an alternative would be pooling over a large set of different model specifications. We evaluate the relative performance of pooling and model selection for nowcasting quarterly GDP for six large industrialized countries. We find that the nowcast performance of single models varies considerably over time, in line with the forecasting literature. Model selection based on sequential application of information criteria can outperform benchmarks. However, the results highly depend on the selection method chosen. In contrast, pooling of nowcast models provides an overall very stable nowcast performance over time. Copyright © 2012 John Wiley & Sons, Ltd.

Nowcasting GDP in Real Time: A Density Combination Approach

“…The two papers most closely related to ours are Kuzin et al (2013) and Mitchell et al (2013). Kuzin et al (2013) study pooling versus model selection for nowcasting, finding that pooling provides more stable and, in most cases, better point nowcasts than model selection.…”

“…When nowcasting, we want to exploit this information. Following the notation in Kuzin et al (2013), quarterly GDP growth is denoted y tq , where t q is the quarterly time index t q = 1, 2, 3, . .…”

“…Kuzin et al (2013) study pooling versus model selection for nowcasting, finding that pooling provides more stable and, in most cases, better point nowcasts than model selection. Our analysis confirms these results, when evaluating density nowcasts for GDP growth utilizing 120 variables, grouped into 15 data block releases, during each month of a quarter.…”

Electricity Price Forecasting by Averaging Dynamic Factor Models

“…Empirically, the improvements of using forecast combination instead of a "best" model have been shown for different types of models (for instance, see [23][24][25]) and in various research areas [15,26]. However, [1] points out that forecast combination techniques have not been fully exploited for electricity prices.…”

“…Another way to combine forecasts would be to employ the median prediction [24]. Alternatively, some authors employ a combination regression of the form:…”