Total factor productivity and shareholder returns in banking

This paper examines shareholder value drivers in European banking focusing on the efficiency and productivity features of individual banks. In particular, we analyse the value relevance of bank cost efficiency and total factor productivity (TFP) (in all its components, including technological change, pure technical efficiency change and scale efficiency change) to see how these influence shareholder value creation in European banking. The paper focuses on the French, German, Italian and U.K. banking systems over the period 1995–2002 and includes both listed and non-listed banks. We find that TFP changes best explain variations in shareholder value (measured by market-adjusted returns, MAR, for listed banks and by the ratio of EVA_bkg to invested capital at time t−1 for non-listed banks). In both samples, we also find that technological change seems to be the most important component of TFP influencing shareholder value creation in European banking.     

Can the Augmented Solow Model with Migration Explain the Italian Internal Brain Drain?

We extend the Dolado et al. (1994) model to both inflows and outflows of migrants and assume that they have a human capital endowment that contributes to increase/decrease the stock of human capital in the receiving/sending economy. We derive the conditional convergence equation in which the impact of migration flows on the growth rate is disentangled in a pure quantity effect and in a quality or composition effect of immigration and emigration rates that accounts for the relative human capital endowment of migrants with respect to resident population. Next, we test the model with Italian regional data for the 1970–2005 time period. We find that the model provides a good explanation of the Italian experience. The quantity effect is negative for the immigration rate and positive for the emigration rate, whereas the composition effect is positive for immigration and negative for emigration. Finally, we separate the centre‐north from the south and find that the composition effect of emigration is stronger for the latter. We interpret these results as a clear evidence of a brain drain from the Mezzogiorno to the centre‐northern regions.     

全球金融危机过后中美两国对中美关系的构想及认识研究

世界金融危机后,随着美国国际地位的下滑和中国国力快速提升,美国对中国的戒心也随之不断增加,在这样的背景下,中美两国间的认识将会发生怎样的变化?对这一议题进行分析对于中美关系的未来展望意义重大。根据中美之间的相互认识与构想来看,中美两国在经济方面维持密切的合作关系,但中国和美国依然就在核心利益方面进行着相互竞争。而且可以预测在未来的国际舞台上,中国将以更积极的姿态,更明确的立场处理中美关系。因此,中国对于核心利益的坚定态度、中美间的矛盾,以及基于相互尊重的对等的中美关系,所有这些都必将让中美间的竞争更加激烈。     

Genetic programming as a means for programming computers by natural selection

Many seemingly different problems in machine learning, artificial intelligence, and symbolic processing can be viewed as requiring the discovery of a computer program that produces some desired output for particular inputs. When viewed in this way, the process of solving these problems becomes equivalent to searching a space of possible computer programs for a highly fit individual computer program. The recently developed genetic programming paradigm described herein provides a way to search the space of possible computer programs for a highly fit individual computer program to solve (or approximately solve) a surprising variety of different problems from different fields. In genetic programming, populations of computer programs are genetically bred using the Darwinian principle of survival of the fittest and using a genetic crossover (sexual recombination) operator appropriate for genetically mating computer programs. Genetic programming is illustrated via an example of machine learning of the Boolean 11-multiplexer function and symbolic regression of the econometric exchange equation from noisy empirical data.Hierarchical automatic function definition enables genetic programming to define potentially useful functions automatically and dynamically during a run, much as a human programmer writing a complex computer program creates subroutines (procedures, functions) to perform groups of steps which must be performed with different instantiations of the dummy variables (formal parameters) in more than one place in the main program. Hierarchical automatic function definition is illustrated via the machine learning of the Boolean 11-parity function.     

The Beta Product Distribution with Complex Parameters

The family consisting of the distributions of products of two independent beta variables is extended to include cases where some of the parameters are not positive but negative or complex. This “beta product” distribution is expressible as a Meijer G function. An example (from risk theory) where such a distribution arises is given: an infinite sum of products of independent random variables is shown to have a distribution that is the product convolution of a complex-parameter beta product and an independent exponential. The distribution of the infinite sum is a new explicit solution of the stochastic equation X = (in law) B(X + C). Characterizations of some G distributions are also proved.     

On L 1-consistency of kernel-type density estimator for stationary markov processes

A convergence result for kernel type density estimators, proved by Devroye and Gyrofi (1985), is extended to stationary Markov processess satisfying (G ₂-condition introduced by Rosenblatt (1970).     

A Bayesian Time Series Model of Multiple Structural Changes in Level,Trend, and Variance

We investigate whether seasonal-adjustment procedures are, at least approximately, linear data transformations. This question was initially addressed by Young and is important with respect to many issues including estimation of regression models with seasonally adjusted data. We focus on the X-11 program and rely on simulation evidence, involving linear unobserved component autoregressive integrated moving average models. We define a set of properties for the adequacy of a linear approximation to a seasonal-adjustment filter. These properties are examined through statistical tests. Next, we study the effect of X-11 seasonal adjustment on regression statistics assessing the statistical significance of the relationship between economic variables. Several empirical results involving economic data are also reported.     

The modified permutation entropy-based independence test of time series

Abstract

In time series, it is essential to check the independence of data by means of a proper method or an appropriate statistical test before any further analysis. Therefore, among different independence tests, a powerful and productive test has been introduced by Matilla-García and Marín via m-dimensional vectorial process, in which the value of the process at time t includes m-histories of the primary process. However, this method causes a dependency for the vectors even when the independence assumption of random variables is considered. Considering this dependency, a modified test is obtained in this article through presenting a new asymptotic distribution based on weighted chi-square random variables. Also, some other alterations to the test have been made via bootstrap method and by controlling the overlap. Compared with the primary test, it is obtained that not only the modified test is more accurate but also, it possesses higher power.     

Workload Process,Waiting Times,and Sojourn Times in a Discrete Time MMAP[K]/SM[K]/1/FCFS Queue

Abstract

In this paper, we study the total workload process and waiting times in a queueing system with multiple types of customers and a first-come-first-served service discipline. An M/G/1 type Markov chain, which is closely related to the total workload in the queueing system, is constructed. A method is developed for computing the steady state distribution of that Markov chain. Using that steady state distribution, the distributions of total workload, batch waiting times, and waiting times of individual types of customers are obtained. Compared to the GI/M/1 and QBD approaches for waiting times and sojourn times in discrete time queues, the dimension of the matrix blocks involved in the M/G/1 approach can be significantly smaller.