Measuring and Predicting Heterogeneous Recessions

This paper conducts an empirical analysis of the heterogeneity of recessions in monthly U.S. coincident and leading indicator variables. Univariate Markovswitching models indicate that it is appropriate to allow for two distinct recession regimes, corresponding with ‘mild’ and ‘severe’ recessions. All downturns start with a mild decline in the level of economic activity. Contractions that develop into severe recessions mostly correspond with periods of substantial credit squeezes as suggested by the ‘financial accelerator’ theory. Multivariate Markov-switching models that allow for phase shifts between the cyclical regimes of industrial production and the Conference Board Leading Economic Index confirm these findings.Business cycle, phase shifts, regime-switching models, Bayesian analysis

The Trade and FDI Effects of EMU Enlargement

This paper considers the nature and the distribution of trade and FDI effects of a potential enlargement of the European Monetary Union (EMU) to the ten countries that obtained EU membership in 2004. One-way and two-way error component gravity models are estimated using a dataset of unbalanced panel data that combines bilateral trade flows among 29 countries and the distribution of outward FDI stocks among these countries. The results reveal a complementarity between trade and investment and a relationship between trade and exchange rate volatility that depends on the sign of bilateral trade balances. Using a simulation-based technique, we find that estimates of FDI effects of EMU range between 18.5 percent for Poland and 30 percent for Hungary.EMU, exchange rate volatility, foreign investment, trade diversion, vertical integration

Modelling latent and actual entrepreneurship

The determinants of latent (i.e., desired) and actual entrepreneurship are analysed in two ways with nearly 8,000 observations from the 2004 “Flash Eurobarometer survey on Entrepreneurship” covering the 25 European Union member states and the United States. Both methods lead to new and extensive insights in the interrelation of both concepts. First, latent and actual entrepreneurship are investigated simultaneously in a bivariate probit setting. The perception of lack of financial support, the perception of administrative complexities, and the perception of lack of sufficient information do not have significant direct impacts on latent entrepreneurship. This points at indirect effects of these variables on latent entrepreneurship via actual entrepreneurship. Second, four groups of individuals are distinguished, based on their involvement in both measures of entrepreneurship. The analysis enables us for example to discuss the determinants of ‘necessity entrepreneurship’. Results show that the perception of administrative complexities is a significant obstacle in setting up a business, irrespective of the declared preference for self-employment, while the perception of financial constraints does not have a significant influence. Also, necessity entrepreneurs are characterized by a relatively low education level compared to those who are neither latent nor actual entrepreneurs. Each model has its own merits. The multinomial model enables researchers to perform group-wise analyses, while the bivariate probit model makes is possible to take into account the importance of latent entrepreneurship without explicitly including latent entrepreneurship in the set of explanatory variables.

Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration

Cointegration occurs when the long run multiplier of a vector autoregressive model exhibits rank reduction. Priors and posteriors of the parameters of the cointegration model are therefore proportional to priors and posteriors of the long run multiplier given that it has reduced rank. Rank reduction of the long run multiplier is modelled using a decomposition resulting from its singular value decomposition. It specifies the long run multiplier matrix as the sum of a matrix that equals the product of the adjustment parameters and the cointegrating vectors, i.e. the cointegration specification, and a matrix that models the deviation from cointegration. Priors and posteriors for the parameters of the cointegration model are obtained by restricting the latter matrix to zero in the prior and posterior of the unrestricted long run multiplier. The special decomposition of the long run multiplier results in unique posterior densities. This theory leads to a complete Bayesian framework for cointegration analysis. It includes prior specification, simulation schemes for obtaining posterior distributions and determination of the cointegration rank via Bayes factors. We illustrate the analysis with several simulated series, the UK data of Hendry and Doornik (1994) and the Danish data of Johansen and Juselius (1990)

Modeling the impact of forecast-based regime switches on macroeconomic time series

Forecasts of key macroeconomic variables may lead to policy changes of governments, central banks and other economic agents. Policy changes in turn lead to structural changes in macroeconomic time series models. To describe this phenomenon we introduce a logistic smooth transition autoregressive model where the regime switches depend on the forecast of the time series of interest. This forecast can either be an exogenous expert forecast or an endogenous forecast generated by the model. Results of an application of the model to US inflation shows that (i) forecasts lead to regime changes and have an impact on the level of inflation; (ii) a relatively large forecast results in actions which in the end lower the inflation rate; (iii) a counterfactual scenario where forecasts during the oil crises in the 1970s are assumed to be correct leads to lower inflation than observed