A Poisson ridge regression estimator

The standard statistical method for analyzing count data is the Poisson regression model, which is usually estimated using maximum likelihood (ML) method. The ML method is very sensitive to multicollinearity. Therefore, we present a new Poisson ridge regression estimator (PRR) as a remedy to the problem of instability of the traditional ML method. To investigate the performance of the PRR and the traditional ML approaches for estimating the parameters of the Poisson regression model, we calculate the mean squared error (MSE) using Monte Carlo simulations. The result from the simulation study shows that the PRR method outperforms the traditional ML estimator in all of the different situations evaluated in this paper.     

On ridge estimators for the negative binomial regression model

The negative binomial (NB) regression model is very popular in applied research when analyzing count data. The commonly used maximum likelihood (ML) estimator is very sensitive to highly intercorrelated explanatory variables. Therefore, a NB ridge regression estimator (NBRR) is proposed as a robust option of estimating the parameters of the NB model in the presence of multicollinearity. To investigate the performance of the NBRR and the traditional ML approach the mean squared error (MSE) is calculated using Monte Carlo simulations. The simulated result indicated that some of the proposed NBRR methods should always be preferred to the ML method.     

REASSESSING SEGMENTATION IN THE LABOUR MARKET: AN APPLICATION FOR ITALY 1995–2004

The aim of this paper is to test for the presence of dualism in a standard wage regression. The disparity in wages between primary and secondary workers, according to labour market segmentation theory, is not provided by worker characteristics, but rather by job characteristics. A standard way to assess this situation is by looking at the estimated coefficients in a standard regression for comparable workers across different labour market segments. In an attempt to avoid arbitrary modelling choices, we deploy mixture regression methods which allow for endogenous determination of the number of existing labour market segments. Using Italian data, our modelling strategy outlines stark differences in returns to human capital between homogeneous workers in different markets. Thus, future policies should consider these findings when implementing labour market measures.     

BOOTSTRAPPING COVARIATE UNIT ROOT TESTS: AN APPLICATION TO INFLATION RATES

This paper proposes a bootstrap procedure for the covariate point optimal tests (CP_T) of Elliott and Jansson. Although the covariate tests enjoy large power gains over the traditional univariate unit root tests, our simulations show that they still suffer from severe size distortions at finite samples. Through simulations, we demonstrate the superiority of the bootstrap procedure in the sense that it can yield desirable size and power properties for the CP_T tests when the Akaike's information criterion is used. Moreover, we show the empirical relevance of the bootstrap tests by applying them to inflation in the G‐10 countries, and then obtain strong evidence against the unit root hypothesis for most countries at the 5% significance level.     

FROM DISCRETE TO CONTINUOUS‐TIME TRANSITION MATRICES IN INTRA‐DISTRIBUTION DYNAMICS ANALYSIS: AN APPLICATION TO PER CAPITA WEALTH IN EUROPE

Previous studies focusing on the intra‐distribution dynamics analysis have usually computed, in a Markov chain framework, discrete‐time transition matrices. Such an approach, however, can involve some limitations, especially when using stock variables. In order to illustrate the importance of the time‐scale issue when estimating transition matrices, this paper applies both discrete and continuous‐time approaches to a set of cross‐national European data on per capita wealth for the period 2000–10. The results reveal, on the one hand, that the continuous‐time estimation provides a most accurate estimation of transition probabilities and, on the other, that the differences between both approaches are especially remarkable in the long‐term equilibrium distribution.