Abstract
In this paper, we reassess the forecasting performance of the Bayesian mixed-frequency 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 1985Q1–2011Q3. 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.
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Notes
We distinguish between plain and crisscrossed cells both coloured in grey. The description of the crisscrossed cells is provided in Sect. 3.3 below.
In the case of monthly quarterly data, p equals three, such that the number of regressors effectively triples.
Observe that at each forecast origin, lagged values of the dependent variable, \(y_{t-i}\), belong to different GDP data vintages. For the sake of notational simplicity we have suppressed this notation.
Recall from Eq. (6) that the log score are computed as the negative (logarithmic) value of the predictive density at the realised outturn value, i.e. the intersection of the vertical black line with the blue and red lines for models with constant and stochastic volatility, respectively.
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The paper has benefited from comments made by Niklas Ahlgren, the editor, two anonymous referees and participants of a research seminar held at the Department of Economics and Finance, the Tallinn University of Technology (Estonia), by participants of the 18th IWH-CIREQ-GW Macroeconometric Workshop: Mixed-Frequency Data in Macroeconomics and Finance (Halle, Germany), the 22nd Annual International Conference on Macroeconomic Analysis and International Finance (Rethymno, Greece), the 1st Baltic Economic Conference (Vilnius, Lithuania), the 1st Vienna Workshop on Economic Forecasting 2018 (Austria) as well as the Workshop: Online/House Prices and Nowcasting at the Swansea University (UK). All computations were performed in R and RcppArmadillo (R Development Core Team 2008). Any remaining errors are the sole responsibility of the author. The views expressed in this paper are those of the author and do not necessarily reflect those of the Bank of Latvia.
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Siliverstovs, B. Assessing nowcast accuracy of US GDP growth in real time: the role of booms and busts. Empir Econ 58, 7–27 (2020). https://doi.org/10.1007/s00181-019-01704-6
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DOI: https://doi.org/10.1007/s00181-019-01704-6