Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model

Abstract

We estimate a Markow-switching dynamic factor model with three states based on six leading business cycle indicators for Germany preselected from a broader set using the Elastic Net soft-thresholding rule. The three states represent expansions, normal recessions and severe recessions. We show that a two-state model is not sensitive enough to reliably detect relatively mild recessions when the Great Recession of 2008/2009 is included in the sample. Adding a third state helps to clearly distinguish normal and severe recessions, so that the model identifies reliably all business cycle turning points in our sample. In a real-time exercise the model detects recessions timely. Combining the estimated factor and the recession probabilities with a simple GDP forecasting model yields an accurate nowcast for the steepest decline in GDP in 2009Q1 and a correct prediction of the timing of the Great Recession and its recovery one quarter in advance.

Measuring the Business Cycle Chronology with a Novel Business Cycle Indicator for Germany

“…Nevertheless, our data coverage remains narrow compared with analyses applying large-scale dynamic factor models, like the one by Galli ( 2018 ). However, more recent contributions in this field tend to indicate that smaller sets of indicators capture more reliably business cycle dynamics than larger sets do (Aastveit et al, 2016 ; Camacho & Martinez-Martin, 2015 ; Carstensen et al, 2020 ).…”

“…To counter these limitations, this paper introduces the Business Cycle Indicator (BCI) for Germany, offering a timely measure of business cycle developments and a reliable basis for monthly business cycle dating, which have been missing so far (Carstensen et al, 2020 ). The only available monthly chronology disseminated by the Economic Cycle Research Institute (ECRI) lacks the important methodological background needed to assess its reliability.…”

The response of CO2emissions to the business cycle: New evidence for the U.S.

“…See https://www.nber.org/cycles.html.19 The relatively poor results for the CF filtered data are not surprising, as we find no statistical evidence for a regimedependent behavior of the emissions elasticity based on this filtering method (seeTable 3).20 The recessions data can be freely accessed from https://www.nber.org/cycles.html.21 There is a large strand of literature employing Markov-switching models for the U.S. Among others, for an overview seeCarstensen et al (2017).…”

Accelerating Peak Dating in a Dynamic Factor Markov-Switching Model

“…The DFMS model has also been successfully applied to macroeconomic data of many other countries, see e.g. Norway by Aastveit et al (2016), Germany by Carstensen et al (2020) and Japan by Watanabe et al (2003) among others, and today still remains a topic of interest. Recently, for example, Camacho et al (2018) investigate the effects of ragged edges for the DFMS model and Doz et al (2020) allow for time-varying long-run growth rates.…”