Markov Switching with Endogenous Number of Regimes and Leading Indicators in a Real-Time Business Cycle Forecast
This paper uses several macroeconomic and financial indicators within a Markov Switching (MS) framework to predict the turning points of the business cycle. The presented model is applied to monthly German real-time data covering the recession and the recovery after the financial crisis. We show how to take advantage of combining single MSARX forecasts with the adjusting of the number of regimes on the real-time path, which both lead to higher forecast accuracy through the non-linearity of the underlying data-generating process. Adjusting the number of regimes implies distinguishing between recessions which are either normal or extraordinary, i.e. specifically determining as early as possible the point in time from which the recession in the aftermath of the financial crisis structurally exceeded previous ones. In fact it turns out that the Markov Switching model can signal quite early whether a conventional recession will occur or whether an economic downturn will be more pronounced.
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