Vita e Pensiero

When the choice of one firm’s internationalisation regards the establishment of a subsidiary in a foreign country, then internationalization is a very complex process involving many variables. Some of them regard the features of the foreign countries in which Italian Small and Medium sized Enterprises (SMEs) formerly established subsidiaries; others regard the consequences of SMEs internationalisation through their economic performance. Through the joint analysis of two variable sets (about countries and firms) and through the statistical method (the Bayesian hierarchical mixed logit model) we are going to implement, we will be able to describe both the most significant characteristics of the firms that opened subsidiaries abroad and the characteristics of the country where the opening took place. The analysis concerns about 400 firms that started an internationalisation process before 2004. Keywords: Internationalisation, Bayesian hierarchical models, Markov Chain Monte Carlo, Bayesian mixed logit model.

This paper aims to analyse the socio-economic profile of indebted Italian households and possible changes of such profile occurred in the recession period (2001-2004) with respect to that of expansion (1998 – 2000). In order to pursue this aim, Multiple Correspondence Analysis (MCA) has been applied to the Burt matrix obtained by merging the 1998 and 2000 Surveys on Household Income and Wealth (SHIW) of the Bank of Italy, that is the expansion phase surveys, and setting as supplementary individuals the rows of the Burt matrix obtained by merging the 2002 and 2004 surveys (SHIW) of the Bank of Italy, that is the recession phase surveys. Through this double merging, it has been possible to analyse jointly the surveys conducted in two different but homogeneous years as far as macroeconomic trends are concerned. The analysis of the above mentioned data has highlighted that, on the whole, the socio-economic profile of indebted Italian households has undergone, in the recession period with respect to that of expansion, noteworthy changes, partly due to the gradual transformation of instruments used in the payment of instalments on more favourable terms and partly due to the high uncertainty with regard to the general economic perspectives which characterize the recession period. Keywords: household debt, purchasing behaviour, business cycle, multiple correspondence analysis.

This paper is a generalization of earlier studies by Venkatesan and Arumugam (2007) who considered the changes in the parameters of an autoregressive (AR) time series model in order to make Bayesian inference for the shift points and other parameters of a changing AR model. In this paper, the problem of gradual changes in the parameters of an AR model of pth order, through Bayesian mixture approach is considered. This model incorporates the beginning and end points of the interval of switch. Further, the Bayes estimates of the parameters are illustrated with the data generated from known model. Keywords: Autoregressive model, Bayesian estimation, Structural change, Mixture model, Numerical integration.

Inferential confidence intervals (CI) represent a widely used technique for testing the equality of the means of two Normal distributions by comparing two particular CI’s relative to the each mean. They allow simple graphic interpretations and result as being more informative than the traditional testing technique through the confidence levels. Yet they are unable to convey the information relative to the compatibility of the null hypothesis with the observed data usually provided by the p-value. The present paper identifies a new measure of such compatibility in the context of inferential CI’s and extends the technique to the non Normal case. Moreover, in case the null hypothesis is accepted, the problem of estimating the common mean is dealt with by means of the bounds of specific inferential CI’s. The proposed procedures are then applied to real data relative to foreign populations originating outside the European Union and sampled from the Italian territory.

In wildlife population studies one of the main goals is estimating the population density. Line transect sampling is a well established methodology for this purpose. The usual approach for estimating the density of the population of interest is to assume a particular model for the detection function. The estimates are extremely sensitive to the shape of the detection function, particularly to the socalled shoulder condition, which ensures that an animal is nearly certain to be detected if it is at a small distance from the observer. For instance, the half-normal model satisfies this condition whereas the negative exponential does not. So, testing whether the shoulder condition is consistent with the data is a primary concern. Since the problem of testing such a hypothesis is invariant under the group of scale transformations, in this paper we propose the uniformly most powerful test in the class of the scale invariant tests for the half-normal model against the negative exponential model. The asymptotic distribution of the test statistic is derived. The critical values and the power are tabulated via Monte Carlo simulations for small samples.

Data heterogeneity, within a (linear) regression framework, often suggest the use of a Clusterwise Linear Regression (CLR) procedure, which implies, among other things, the estimate of the appropriate number of clusters as well as the cluster membership of each unit. The approaches to the estimation of a CLR model are essentially based on the Ordinary Least Square (OLS) criterion or the likelihood criterion. In this paper, in a context of OLS approach, we propose an estimation of the model making use of an algorithm based on a threshold criterion for the determination coefficient of each cluster, to identify the appropriate number of clusters, and of a modified Spath’s algorithm, to estimate the cluster membership of each sample unit. A simulation design and an application to a real data-set show that the procedure outperforms other algorithms commonly used in literature.

Motivated by the misspecification problem in time series analysis, the nonparametric approach has quickly developed in the latest years. First models in literature were focused on the estimation of the conditional mean. It is well known that alongside the conditional mean it is important to study the series volatility (conditional variance). The following paper deals with nonparametric autoregression with multiplicative volatility and additive mean as studied by Yang et al. (1999). A new estimation procedure is here provided. The procedure uses the residual-based estimator, backfitting algorithm and the local polynomial estimation. Some applications with simulated and real data will be presented.