Analyzing a panel of seasonal time series: Does seasonality in industrial production converge across Europe?

In this paper we consider deterministic seasonal variation in quarterly industrial production for several European countries, and we address the question whether this variation has become more similar across countries over time. Due to economic and institutional factors, one may expect convergence across business cycles. When these have similar characteristics as seasonal cycles, one may perhaps also find convergence in seasonality. To this aim, we propose a method that is based on treating the set of production series as a panel. By testing for the relevant parameter restrictions for moving window samples, we examine the hypothesis of convergence in deterministic seasonality while allowing for seasonal unit roots. Our main empirical finding is that there is no evidence for convergence in seasonality.     

Labor income and risky investments: can part-time farmers compete?

In the developed countries, a majority of farm households receive at least as much income from nonfarm sources as from the farm. Such part-time farms have survived inspite of lower returns than full-time farms. This paper considers when lower returns to part-time farming could be compensated by risk-reduction due to diversification of income sources. The paper uses a dynamic portfolio choice model with labor income. The model and results could be applied in other contexts as well.     

Spurious principal components

The principal component regression (PCR) is often used to forecast macroeconomic variables when there are many predictors. In this letter, we argue that it makes sense to pre-whiten the predictors before including these in a PCR. With simulation experiments, we show that without such pre-whitening, spurious principal components can appear and that these can become spuriously significant in a PCR. With an illustration to annual inflation rates for five African countries, we show that non-spurious principal components can be genuinely relevant in empirical forecasting models.     

Estimating Transition Probabilities from a Time Series of Independent Cross Sections

This paper considers the implementation of a nonstationary, heterogeneous Markov model for the analysis of a binary dependent variable in a time series of independent cross sections. The model, previously considered by M offitt (1993), offers the opportunity to estimate entry and exit transition probabilities and to examine the effects of time-constant and time-varying covariates on the hazards. We show how ML estimates of the parameters can be obtained by Fisher's method-of-scoring and how to estimate both fixed and time-varying covariate effects. The model is exemplified with an analysis of the labor force participation decision of Dutch women using data from the Socio-economic Panel (SEP) study conducted in the Netherlands between 1986 and 1995. We treat the panel data as independent cross sections and compare the employment status sequences predicted by the model with the observed sequences in the panel. Some open problems concerning the application of the model are also discussed.     

On forecasting cointegrated seasonal time series

We analyze periodic and seasonal cointegration models for bivariate quarterly observed time series in an empirical forecasting study. We include both single equation and multiple equation methods for those two classes of models. A VAR model in first differences, with and without cointegration restrictions, and a VAR model in annual differences are also included in the analysis, where they serve as benchmark models. Our empirical results indicate that the VAR model in first differences without cointegration is best if one-step ahead forecasts are considered. For longer forecast horizons however, the VAR model in annual differences is better. When comparing periodic versus seasonal cointegration models, we find that the seasonal cointegration models tend to yield better forecasts. Finally, there is no clear indication that multiple equations methods improve on single equation methods.     

Can neoclassical economics become social economics?

This essay has both a general and a specific purpose. Its general purpose is to pose the question: Can neoclassical economics be social economics? Its answer to this general question is: Yes, but only if it abandons its methodological soul; that is, by abandoning methodological individualism, positivism, and ahistoricism, and expressly and systematically adopting a methodological perspective which is holistic, normative, and historical. Its specific purpose is to identify and examine the major elements in the economics of one leading figure in the historical development of neoclassical economics who self-consciously attempted to combine, to paraphrase Schumpeter, a neoclassical head with a social economics heart: Alfred Marshall.