How to forecast economic cycles after the 2008-2009 recession?
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Catherine Doz*, Laurent Ferrara and Pierre-Alain Pionnier
Macroeconomic forecasts are often made by separating two objectives: providing the growth rate of the variable of interest (usually GDP) and detecting turning points in the economic cycle. Yet the 2008-09 Great Recession, and the period of weak growth that followed in most advanced economies, showed that it could be crucial to combine these two approaches. To do that, macroeconomic forecasters need models accounting for: (i) sudden and deep recessions, (ii) periods of increasing macroeconomic volatility, and (iii) fluctuations in trend GDP growth.
In this article, Doz, Ferrara and Pionnier offer an econometric model that integrates these three characteristics, and show that their model both anticipates the detection of turning points in the US economic cycle and improves GDP forecasts since the last recession. Their model is an extension of the Markov-Switching Dynamic Factor Model (MS-DFM). In a DFM, economic series are assumed to be linked to one or several latent variables called factors, which give them common dynamic properties. In this case, one factor summarizes the dynamics of five variables (quarterly GDP and the four monthly variables used by the National Bureau of Economic Research to date turning points in the US economic cycle) and represents the underlying state of the economy. In addition, the MS-DFM introduces regime shifts in economic dynamics: the average value of the underlying factor during periods of recession differs from its average value during periods of expansion, and the probability of the economy being in one state or the other is updated at each date according to the latest available macroeconomic information.
The authors extend the MS-DFM model by adding two new characteristics to it. On the one hand, they introduce the possibility that the amplitude of factor fluctuations (volatility) also has two different regimes: a low-volatility and a high-volatility regime. The probability of switching from one macroeconomic volatility regime to the other, as well as the values associated with that volatility are part of the estimated parameters. On the other hand, they allow trend GDP growth to change over time. Once the parameters are calculated, the model can evaluate the probability that economic cycle turning points have occurred and forecast future GDP values.
The authors’ findings first show that accounting for volatility switches clearly improves the identification of economic cycle turning points, especially during the period of low volatility prevailing since the mid-1980s (the “Great Moderation”). It is an important result for the detection of future recessions because, according to their model, the US economy is back to low volatility state after an interruption during the Great Recession. Moreover, their findings reveal a gradual decrease in the US trend GDP growth that began a few years before the 2008-09 recession and continued thereafter, which contributes to the current debate on the US productivity and growth slowdown. Finally, a real-time forecasting exercise, using the values of variables available at each date to forecast GDP, shows that the model has a better forecasting performance than a model that would not account for regime switches.
Original title of the article: Business Cycle Dynamics after the Great Recession: An Extended Markov-Switching Dynamic Factor Model
Published in: PSE Working Papers and OECD Statistics Working Papers
Available at: https://ideas.repec.org/p/oec/stdaaa/2020-01-en.html
Photo credit: Gajus (Shutterstock)
* PSE Member