Investigating Institutional,Economic and Social Determinants of European Regions for Firm Growth Through Employment Generation

This paper examines the influence of the institutional, economic and social characteristics of a region on firm growth through employment generation across 14 European countries for the time period 2010–2013. Theoretically, we utilize the resource‐based view alongside insights from institutional theory to develop a conceptual framework that captures the influence of regional characteristics on firm employment growth. Based on this framework, our empirical results indicate that not only does firm growth depend on the firm‐specific characteristics found in the literature, but regional attributes also significantly impact firm growth in a heterogeneous way for different firm types. In line with the heterogeneous nature of firm growth, our results point to significant differences in the influence of institutional, economic and social characteristics on firm growth in different‐sized groups and across different rates of growth distribution. The implications of our study suggest the importance of managers and policy‐makers realizing which firms are mostly expected to benefit from the external environment, and which in turn can be planned via tailored policy reform by regional governments and firm‐level strategy‐making by managers.     

Interlocking directorship across Italian listed companies: evidence from a natural experiment

The financial crisis of 2008 and the introduction of the Interlocking Ban in 2011 (for financial firms) deeply influenced the Italian corporate governance system. The aim of the present study is to investigate to what extent personal ties among the directors of Italian listed companies have changed after these two events. We describe the evolution and dimension of the phenomenon of interlocking directorship (ID) for all the Italian listed companies over the period 1998–2013 using different methodologies. Social network analysis discloses the existence of clusters of companies whose links remain dense after the crisis, while connections to the peripheral units of the system decrease, reducing the overall connectedness. Results reveal that, over the period, there is a reduction in the cumulation ratio which still remains high and mainly due to a high number of directorships for multiple directors. This reduction is more severe after 2008 when both the financial crisis and the Interlocking Ban occur. In disentangling the two effects we observe ID reduction during and after the crisis, also for non-financial firms, confirming the general tendency of a decline in national board interlocking networks.     

Copulas Constructed from Horizontal Sections

In analogy with the study of copulas whose diagonal sections have been fixed, we study the set _h of copulas for which a horizontal section h has been given. We first show that this set is not empty, by explicitly writing one such copula, which we call horizontal copula. Then we find the copulas that bound both below and above the set _h. Finally, we determine the expressions for Kendall's tau and Spearman's rho for the horizontal and the bounding copulas.     

Testing the lucas critique: A review

Claims that the parameters of an econometric model are invariant under changes in either policy rules or expectations processes entail super exogeneity and encompassing implications. Super exogeneity is always potentially refutable, and when both implications are involved, the Lucas critique is also refutable. We review the methodological background; the applicability of the Lucas critique; super exogeneity tests; the encompassing implications of feedback and feedforward models; and the role of incomplete information. The approach is applied to money demand in the u.S.A. to examine constancy, exogeneity, and encompassing, and reveals the Lucas critique to be inapplicable to the model under analysis.     

Influence diagnostics in heteroscedastic and/or autoregressive nonlinear elliptical models for correlated data

In this paper, we propose nonlinear elliptical models for correlated data with heteroscedastic and/or autoregressive structures. Our aim is to extend the models proposed by Russo et al. 22 by considering a more sophisticated scale structure to deal with variations in data dispersion and/or a possible autocorrelation among measurements taken throughout the same experimental unit. Moreover, to avoid the possible influence of outlying observations or to take into account the non-normal symmetric tails of the data, we assume elliptical contours for the joint distribution of random effects and errors, which allows us to attribute different weights to the observations. We propose an iterative algorithm to obtain the maximum-likelihood estimates for the parameters and derive the local influence curvatures for some specific perturbation schemes. The motivation for this work comes from a pharmacokinetic indomethacin data set, which was analysed previously by Bocheng and Xuping 1 under normality.     

Evaluation of Recursive Detection Methods for Turning Points in Financial Time Series

Timely identification of turning points in economic time series is important for planning control actions and achieving profitability. This paper compares sequential methods for detecting peaks and troughs in stock values and deciding the time to trade. Three semi‐parametric methods are considered: double exponential smoothing, time‐varying parameters and prediction error statistics. These methods are widely used in monitoring, forecasting and control, and their common features are recursive computation and exponential weighting of observations. The novelty of this paper is the selection of smoothing and alarm coefficients for maximisation of the gain (the difference in level between subsequent peaks and troughs) of sample data. The methods are compared on applications to leading financial series and with simulation experiments.     

Effectiveness of potent antiretroviral therapy on progression of human immunodeficiency virus: Bayesian modelling and model checking via counterfactual replicates

Summary.  We analyse data from a seroincident cohort of 457 homosexual men who were infected with the human immunodeficiency virus, followed within the multicentre Italian Seroconversion Study. These data include onset times to acquired immune deficiency syndrome (AIDS), longitudinal measurements of CD4⁺ T-cell counts taken on each subject during the AIDS-free period of observation and the period of administration of a highly active antiretro- viral therapy (HAART), for the subset of individuals who received it. The aim of the study is to assess the effect of HAART on the course of the disease. We analyse the data by a Bayesian model in which the sequence of longitudinal CD4⁺ cell count observations and the associated time to AIDS are jointly modelled at an individual subject's level as depending on the treatment. We discuss the inferences obtained about the efficacy of HAART, as well as modelling and computation difficulties that were encountered in the analysis. These latter motivate a model criticism stage of the analysis, in which the model specification of CD4⁺ cell count progression and of the effect of treatment are checked. Our approach to model criticism is based on the notion of a counterfactual replicate data set Z ^c . This is a data set with the same shape and size as the observed data, which we might have observed by rerunning the study in exactly the same conditions as the actual study if the treated patients had not been treated at all. We draw samples of Z ^c from a null model M ₀, which assumes absence of treatment effect, conditioning on data collected in each subject before initiation of treatment. Model checking is performed by comparing the observed data with a set of samples of Z ^c drawn from M ₀.     

Robust nonparametric estimation of the intensity function of point data

Intensity functions—which describe the spatial distribution of the occurrences of point processes—are useful for risk assessment. This paper deals with the robust nonparametric estimation of the intensity function of space–time data from events such as earthquakes. The basic approach consists of smoothing the frequency histograms with the local polynomial regression (LPR) estimator. This method allows for automatic boundary corrections, and its jump-preserving ability can be improved with robustness. We derive a robust local smoother from the weighted-average approach to M-estimation and we select its bandwidths with robust cross-validation (RCV). Further, we develop a robust recursive algorithm for sequential processing of the data binned in time. An extensive application to the Northern California earthquake catalog in the San Francisco, CA, area illustrates the method and proves its validity.