Assessing nowcast accuracy of US GDP growth in real time: the role of booms and busts

Abstract

In this paper we reassess the forecasting performance of the Bayesian mixedfrequency model suggested in Carriero et al. (2015) in terms of point and density forecasts of the GDP growth rate using US macroeconomic data. Following Chauvet and Potter (2013), we evaluate the forecasting accuracy of the model relative to a univariate AR(2) model separately for expansions and recessions, as defined by the NBER business cycle chronology, rather than relying on a comparison of forecast accuracy over the whole forecast sample spanning from the first quarter of 1985 to the third quarter of 2011. We find that most of the evidence favouring the more sophisticated model over the simple benchmark model is due to relatively few observations during recessions, especially those during the Great Recession. In contrast, during expansions the gains in forecasting accuracy over the benchmark model are at best very modest. This implies that the relative forecasting performance of the models varies with business cycle phases. Ignoring this fact results in a distorted picture: the relative performance of the more sophisticated model in comparison with the naive benchmark model tends to be overstated during expansions and understated during recessions.

New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past?

“…The adopted mixedfrequency setup allows to monitor changes in the forecast accuracy as more information can be incorporated in the forecasting model from one month to another, in contrast to Chauvet and Potter (2013), where forecasts were made once per quarter. Siliverstovs (2020a) shows that, at first glance, the impressive reduction in the RMSFE over the benchmark AR(2) model up to 22% reported in Carriero et al (2015), when evaluated over the whole forecast sample from 1985Q1 until 2011Q3, is mainly driven by a few observations during recessions, with the most prominent contribution being traced to those observations during the Great Recession. Evaluation of the model's forecasting performance during NBER recessions and expansions indicates that, during expansions, the performance of this model is closely matched by that of the benchmark model, conforming with the conclusion of Chauvet and Potter (2013).…”

“…Second, during expansions, the forecasting performance of highly sophisticated models was matched by that of a simple univariate autoregressive benchmark model. Siliverstovs (2020a) extends the analysis of Chauvet and Potter (2013) to a different class of models, namely, models that combine data observed at the heterogeneous frequencies: quarterly GDP growth and several monthly economic and financial indicators, such as industry production, sentiment indices, labor-market and housing statistics, stock market index, and interest rates that are commonly used for assessing current economic conditions in the US. In particular, Siliverstovs (2020a) re-examines the forecasting performance of a multiple-indicator U-MIDAS-type model suggested in Carriero et al (2015).…”

“…The findings of Chauvet and Potter (2013) and Siliverstovs (2020a), reported for a single time series (US GDP growth), were extended in Siliverstovs and Wochner (2021) for each time series in the Stock-Watson dataset comprising more than 200 US macroeconomic variables. The aim of this exercise was to replicate the study of Stock and Watson (2002) on a more recent data vintage but evaluate the forecasting performance of the diffusion-index model separately for the NBER expansions and recessions in a similar way as done in Chauvet and Potter (2013).…”

“…which uses the average GDP growth rate as the forecast for the upcoming two quarters. As argued in Siliverstovs (2020a), this very simple model provides forecasts of US GDP growth that during NBER expansions match the predictive accuracy not only of an autoregressive model, but also of the mixed-frequency model of Carriero et al (2015). Both benchmark models are estimated using recursively expanding windows that start in 1970Q1.…”

Revisions in the Norwegian National Accounts: accuracy, unbiasedness and efficiency in preliminary figures

“…A recent special issue of Empirical B Håvard Hungnes Havard.Hungnes@ssb.no 1 Statistics Norway, Oslo, Norway Economics, see Kunst and Wagner (2020), focuses on forecasting of macroeconomic variables and on the consequence of final vintage of National Accounts figures not being available when forecasts (or nowcasts) are made. For example, Siliverstovs (2020) considers the problem of nowcasting (both point forecasts and density forecasts) when conditioning on variables that are preliminary and finds that a simple univariate model can be better than a sophisticated mixed frequency model to obtain good nowcasts. On the other hand, Claudio et al (2020) find that a mixed frequency model outperforms forecasts obtained by more traditional single-frequency models when applying data available in real time.…”