KNOWLEDGE SPILLOVERS AND OUTPUT PER WORKER: AN INDUSTRY‐LEVEL ANALYSIS FOR OECD COUNTRIES

This study analyzes the impact of knowledge spillovers on output per worker at the industry level using a primal production function approach. The article makes three different contributions to the international spillovers literature: (1) it identifies trade‐related spillovers under alternative assumptions regarding the information transferred through imports; (2) it explores the importance of horizontal and vertical foreign direct investment (FDI) in knowledge spillovers; and (3) it looks at how institutional factors determine the impact of FDI‐related spillovers on productivity. The main findings of the study are: (1) international knowledge spillover is an important driver of industry output per worker, and the magnitude of this spillover effect varies with alternative assumptions about the information content embodied in imports, while high technology industries benefit significantly more from import‐related knowledge spillovers; and (2) the gains from FDI spillovers are primarily horizontal, but when institutional factors are considered, countries with stronger protection of intellectual property rights and a high “ease of doing business” tend to experience a substantial increase in the effectiveness of both horizontal and vertical FDI‐related spillovers. (JEL E24, F1, F6, O3, O4)     

Depressive Symptomatology in Greek Preadolescents: The Role of Parenting Style Perceptions

The relationships between preadolescents’ perceptions of their parents’ parenting style and depressive symptomatology were examined in a sample of 640 Greek preadolescents (mean age = 11.82 years, SD = .83). Children completed the Paternal Style and Dimensions Questionnaire, the Maternal Style and Dimensions Questionnaire, and the Children's Depression Inventory. According to the results, there is a significant link between parenting style (both for mothers and fathers) and depressive symptoms as manifested by preadolescents. In this study, gender differences were not found. Furthermore, the results indicate that preadolescents’ perception of their parents’ style is a significant factor in predicting depressive symptomatology. Finally, the present findings highlight the influence of parenting styles on children's psychological wellbeing.     

Straddling Two Continents: Social Policy and Welfare Politics in Turkey

There has been an increasing academic interest in understanding the dynamics of social policy in the Middle East and developing a conceptual ‘model’ to account for the particular characteristics of welfare arrangements in the countries of the region. While part of this framework, Turkey represents an exceptional case due to the Europeanization processes the country is undergoing in various policy areas, including social policy. The influence of the European Union on the shape of Turkish social policy, as illustrated by the government's recent reforms in the labour market and social security domains, is hereby used to outline the position of Turkey vis‐à‐vis both the Southern European welfare regime and the Middle Eastern pattern. This article seeks to assess the dynamics of Turkish social policy in light of the country's political, and socio‐economic dynamics, as well as the external influence exerted by the EU and international financial institutions. The aim is to examine Turkish welfare arrangements in a comparative manner and consider its suitability with reference to either of the two models. Looking at major trends in social security and the labour market, the article argues for a Turkish ‘hybrid’ model embodying the characteristics of both. Subject to EU explicit pressures for reform absent elsewhere in the Middle East, the data nevertheless show that Turkey has yet to make the qualitative leap forward that could place it firmly within the Southern European welfare group.     

Automatic Block-Length Selection for the Dependent Bootstrap

We review the different block bootstrap methods for time series, and present them in a unified framework. We then revisit a recent result of Lahiri [Lahiri, S. N. (1999b). Theoretical comparisons of block bootstrap methods, Ann. Statist. 27:386-404] comparing the different methods and give a corrected bound on their asymptotic relative efficiency; we also introduce a new notion of finite-sample “attainable” relative efficiency. Finally, based on the notion of spectral estimation via the flat-top lag-windows of Politis and Romano [Politis, D. N., Romano, J. P. (1995). Bias-corrected nonparametric spectral estimation. J. Time Series Anal. 16:67-103], we propose practically useful estimators of the optimal block size for the aforementioned block bootstrap methods. Our estimators are characterized by the fastest possible rate of convergence which is adaptive on the strength of the correlation of the time series as measured by the correlogram.     

A bootstrap test for time series linearity

A bootstrap algorithm is proposed for testing Gaussianity and linearity in stationary time series, and consistency of the relevant bootstrap approximations is proven rigorously for the first time. Subba Rao and Gabr (1980) and Hinich (1982) have formulated some well-known nonparametric tests for Gaussianity and linearity based on the asymptotic distribution of the normalized bispectrum. The proposed bootstrap procedure gives an alternative way to approximate the finite-sample null distribution of such test statistics. We revisit a modified form of Hinich's test utilizing kernel smoothing, and compare its performance to the bootstrap test on several simulated data sets and two real data sets—the S&P 500 returns and the quarterly US real GNP growth rate. Interestingly, Hinich's test and the proposed bootstrapped version yield substantially different results when testing Gaussianity and linearity of the GNP data.     

The asymptotic size and power of the augmented Dickey–Fuller test for a unit root

It is shown that the limiting distribution of the augmented Dickey–Fuller (ADF) test under the null hypothesis of a unit root is valid under a very general set of assumptions that goes far beyond the linear AR(∞) process assumption typically imposed. In essence, all that is required is that the error process driving the random walk possesses a continuous spectral density that is strictly positive. Furthermore, under the same weak assumptions, the limiting distribution of the ADF test is derived under the alternative of stationarity, and a theoretical explanation is given for the well-known empirical fact that the test's power is a decreasing function of the chosen autoregressive order p. The intuitive reason for the reduced power of the ADF test is that, as p tends to infinity, the p regressors become asymptotically collinear.     

Joint models with multiple longitudinal outcomes and a time-to-event outcome: a corrected two-stage approach

Joint models for longitudinal and survival data have gained a lot of attention in recent years, with the development of myriad extensions to the basic model, including those which allow for multivariate longitudinal data, competing risks and recurrent events. Several software packages are now also available for their implementation. Although mathematically straightforward, the inclusion of multiple longitudinal outcomes in the joint model remains computationally difficult due to the large number of random effects required, which hampers the practical application of this extension. We present a novel approach that enables the fitting of such models with more realistic computational times. The idea behind the approach is to split the estimation of the joint model in two steps: estimating a multivariate mixed model for the longitudinal outcomes and then using the output from this model to fit the survival submodel. So-called two-stage approaches have previously been proposed and shown to be biased. Our approach differs from the standard version, in that we additionally propose the application of a correction factor, adjusting the estimates obtained such that they more closely resemble those we would expect to find with the multivariate joint model. This correction is based on importance sampling ideas. Simulation studies show that this corrected two-stage approach works satisfactorily, eliminating the bias while maintaining substantial improvement in computational time, even in more difficult settings.

     

Model-based clustering using copulas with applications

The majority of model-based clustering techniques is based on multivariate normal models and their variants. In this paper copulas are used for the construction of flexible families of models for clustering applications. The use of copulas in model-based clustering offers two direct advantages over current methods: (i) the appropriate choice of copulas provides the ability to obtain a range of exotic shapes for the clusters, and (ii) the explicit choice of marginal distributions for the clusters allows the modelling of multivariate data of various modes (either discrete or continuous) in a natural way. This paper introduces and studies the framework of copula-based finite mixture models for clustering applications. Estimation in the general case can be performed using standard EM, and, depending on the mode of the data, more efficient procedures are provided that can fully exploit the copula structure. The closure properties of the mixture models under marginalization are discussed, and for continuous, real-valued data parametric rotations in the sample space are introduced, with a parallel discussion on parameter identifiability depending on the choice of copulas for the components. The exposition of the methodology is accompanied and motivated by the analysis of real and artificial data.