The term structure, leading indicators, and recessions: evidence from Switzerland, 1974–2017

This paper studies the predictive power for recessions of the slope of the Swiss term structure using monthly data for 1974–2017. Dynamic probit models indicate that the term structure contains information useful for predicting recessions for horizons up to 19 months. Whether the economy is currently in recession or not is also useful for forecasting recessions. These relationships prove stable over the sample. Robustness tests indicate that the KOF business course indicator and some monetary aggregates contain different information from the term structure which can improve the in- and out-of-sample fit of the model.

Joint Prediction of Turning Points in Credit and Business Cycles: Cross-Country Analysis

This paper provides a joint analysis of business and credit cycles with a focus on unobservable factors affecting both cycles, at the cross-country level. Using quarterly data for 19 developed countries and Russia for the period from 1994 to 2018, we build a system of two dynamic probit models, which includes a cross-correlation between the errors of the equations governing the probability of a recession and the probability of credit crisis. The results show that, first, our system allows us to correctly predict 91% of episodes of joint realization of macroeconomic and credit crises and 89% of non-crisis periods in the training sample, and 92% and 95% respectively in the testing sample. Second, switching from two independent regression models to a system of correlated equations significantly (by 16 percent- age points) increases the share of correctly predicted crisis episodes while only slightly (by 7 percentage points) reducing the proportion of correctly predicted non-crisis episodes. Third, our system can predict an approaching crisis earlier, by 1–4 quarters, in comparison with similar single models. Our results complement the literature on forecasting recessions and credit crises. Fourth, it is revealed that the models which have been constructed on developed countries allow one to predict crisis events for Russia. The model we have constructed correctly predicts 100% of joint crisis episodes and 92% of joint non-crisis episodes in the training sample as well as 86% of joint crisis and 90% of joint non-crisis episodes in the testing sample for Russia.

Does Inequality Harm Economic Development and Democracy?

This article examines whether economic inequality undermines economic development and democracy in the long run. After reviewing the literature on the effect of inequality on economic development and democracy, it considers three approaches that have been put forward to explain why inequality harms the economy and democracy: (1) the political economy approach, (2) the social unrest approach, and (3) the credit market imperfections approach. A complete data set on inequality is generated using three measures of inequality: the capital share data set of Ortega and Rodriguez (2006), the Gini coefficients data set of Solt (2009), and the income Gini coefficients of the “Estimated Household Income Inequality” (EHII) data set, developed by the University of Texas Inequality Project (UTIP). The article then tests the relationship between inequality and democracy using dynamic probit models.