Choosing the share bond by using qualitative dependent variable models in Turkey

In this paper whether the bond according to a certain criterion for one time period in the future with the classic logit models and for a certain time period in the future with the panel logit model is successful or not have been forecasted. For this purpose financial ratios of the industrial companies listed on Istanbul Stock Exchange in Turkey over the period 1995–2001 were used. The results indicated that different financial data is effective in the different models used for different terms i.e., the models are different for each other.     

Forecasting banking crises with dynamic panel probit models

Banking crises are rare events, but when they occur, their consequences are often dramatic. The aim of this paper is to contribute to the toolkit of early warning models that is available to policy makers by exploring the dynamics and exuberances embedded in a panel dataset that covers 22 European countries over four decades (from 1970Q1 to 2012Q4). The in- and out-of-sample forecast performances of several (dynamic) probit models are evaluated, with the objective of developing common vulnerability indicators with early warning properties. The results obtained show that adding dynamic components and exuberance indicators to the models improves the performances of early warning models significantly.     

Quasi-maximum likelihood estimation of parameters in a multivariate poisson process

Asymptotic normality and quick consistency of quasi-maximum likelihood estimators of parameters in a multivariate Poisson process are proved. Possible application of the results obtained to the problem of unfolding histograms is briefly discussed.     

A method to obtain new copulas from a given one

Given a strictly increasing continuous function φ from [0, 1] to [0, 1] and its pseudo-inverse φ^[−1], conditions that φ must satisfy for C_φ(x₁, . . . ,x_n)=φ^[−1](C(φ(x₁), . . . ,φ(x_n))) to be a copula for any copula C are studied. Some basic properties of the copulas obtained in this way are analyzed and several examples of generator functions φ that can be used to construct copulas C_φ are presented. In this manner, a method to obtain from a given copula C a variety of new copulas is provided. This method generalizes that used to construct Archimedean copulas in which the original copula C is the product copula, and it is related with mixtures     

Supporting high-tech start-ups: Lessons from Italian technology policy

Public intervention is often advocated in high-tech sectors in order to solve market imperfections that limit the activity of new technology-based firms (NTBFs). Whilst some European countries have adopted national government support policies which explicitly target this type of firm; in Italy, as in other EU countries, there are no public support measures specifically designed for NTBFs. So, a question arises: to what extent are the NTBFs of these countries able to get funds from public administration? The paper focuses on the Italian case, and after a review of the available national public subsidies, through empirical and econometric analyses, it investigates whether horizontal general-purpose direct support mechanisms at national level permit an efficient allocation of public funds. The results highlight the need for more specific and customised policy measures towards NTBFs.     

Modeling and predicting U.S. recessions using machine learning techniques

The most representative machine learning techniques are implemented for modeling and forecasting U.S. economic activity and recessions in particular. An elaborate, comprehensive, and comparative framework is employed in order to estimate U.S. recession probabilities. The empirical analysis explores the predictive content of numerous well-followed macroeconomic and financial indicators, but also introduces a set of less-studied predictors. The predictive ability of the underlying models is evaluated using a plethora of statistical evaluation metrics. The results strongly support the application of machine learning over more standard econometric techniques in the area of recession prediction. Specifically, the analysis indicates that penalized Logit regression models, k-nearest neighbors, and Bayesian generalized linear models largely outperform ‘original’ Logit/Probit models in the prediction of U.S. recessions, as they achieve higher predictive accuracy across long-, medium-, and short-term forecast horizons.     

Multivariate Clustered Data Analysis in Developmental Toxicity Studies

In this paper we review statistical methods for analyzing developmental toxicity data. Such data raise a number of challenges. Models that try to accommodate the complex data generating mechanism of a developmental toxicity study, should take into account the litter effect and the number of viable fetuses, malformation indicators, weight and clustering, as a function of exposure. Further, the size of the litter may be related to outcomes among live fetuses. Scientific interest may be in inference about the dose effect, on implications of model misspecification, on assessment of model fit, and on the calculation of derived quantities such as safe limits, etc. We describe the relative merits of conditional, marginal and random-effects models for multivariate clustered binary data and present joint models for both continuous and discrete data.     

Analysis of variance on indirectly measured variables

Abstract  When observations from a normal distribution can only be obtained indirectly by counting the number of subjects responding to a previously chosen dose, parameter estimates can be obtained by using probit analysis. Well-known is the maximum likelihood technique of parameter estimation, less known is the approach by weighted least squares. The latter approach is followed to compare the parameters of several normal distributions by testing their equality, in analogy with the analysis of variance. A practical situation gave rise to this study and it is worked out at the end of the paper.     

Forecasting distress in cooperative banks: The role of asset quality

This paper analyzes the drivers of financial distress that were experienced by small Italian cooperative banks during the latest deep recession, focusing mainly on the importance of bank capital as a predictor of bankruptcy for Italian nonprofit banks. The analysis aims to build an early-warning model that is suitable for this type of bank.The results reveal non-monotonic effects of bank capital on the probability of failure. In contrast to distress models for for-profit banks, non-performing loans, profitability, liquidity, and management quality have a negligible predictive value. The findings also show that unreserved impaired loans have an important impact on the probability of bank distress. Moreover, the loan–loss ratio provision on substandard loans constitutes a suitable antibody against bank distress. Overall, the results are robust in terms of both the methodology (i.e., frequentist and Bayesian approaches) and the sample used (i.e., cooperative banks in Italy and euro-area countries).     

A Spatial Model of the Impact of Bankruptcy Law on Entrepreneurship

Abstract

This paper employs spatial econometrics techniques to estimate the impact of bankruptcy regulation on small firm formation. The estimation of the model is computationally challenging due to the joint appearance of a lagged endogenous variable and the unobserved heterogeneity which requires modelling of initial conditions as described in Heckman (1981). We test for the joint significance of the state dummy variables in a way that can be viewed as an interesting alternative to the Hausman procedure. This was important for our analysis since, as sometimes happens in finite samples, the estimated variance–covariance matrix was not positive semi-definite. We found that the predicted probability of starting a business is 25% higher in states with higher bankruptcy exemptions than their neighbours relative to states with lower exemptions than their neighbours.

Un modèle spatial de l'impact des lois sur la faillite sur la création d'entreprises

Résumé La présente communication emploie des techniques d’économétrie spatiale pour évaluer l'impact de la réglementation en matière de faillite sur la constitution de petites entreprises. L'estimation du modèle pose des difficultés sur le plan computationnel en raison de l'apparition conjointe d'une variable endogène décalée et de l'hétérogénéité non observée, qui rend nécessaire la modélisation de conditions initiales, de la façon décrite par Heckman (1981). Nous testons la signification conjointe des variables indicatrices de l’état d'une façon qui peut être considérée comme une alternative intéressante à la procédure de Hausman. Ceci était important pour notre analyse, car, comme nous le relevons parfois dans des échantillons finis, la matrice variance–covariance estimée n’était pas semi-définie positive. Nous en concluons que la probabilité prévisible du lancement d'une affaire est plus élevée de l'ordre de 25% dans les états qui appliquent des exemptions pour les faillites supérieures à celles des pays avoisinants, par rapport aux états qui appliquent des exemptions inférieures à celles de leurs voisins.

Un modelo espacial del impacto de la ley de bancarrotas sobre las iniciativas empresariales

Résumén Este artículo emplea técnicas de econometría espacial para estimar el impacto de las normativas de bancarrotas sobre la formación de empresas pequeñas. La valoración del modelo es computacionalmente desafiante, debido a la aparición conjunta de una variable endógena rezagada y heterogeneidad inadvertida que requieren la modelación de las condiciones iniciales, como se describe en Heckman (1981). Ensayamos la significancia conjunta de las variables de prueba estatales de una forma que puede percibirse como una alternativa interesante al procedimiento Hausman. Esto fue importante para nuestro análisis, ya que, como ocurre a veces con muestras finitas, la matriz estimada de varianza–covarianza no fue semidefinitiva positiva. Descubrimos que la probabilidad predicha de iniciar un negocio es un 25% mayor en los estados con mayores exenciones de bancarrota que sus vecinos, en relación con estados con menos exenciones que sus vecinos.

     

Dynamic Macroeconomics

David Romer, Advanced Macroeconomics. Stephen J. Turnovsky, Methods of Macroeconomic Dynamics.      

基于价值工程方法改进的多维Logit模型研究

通过引入价值工程方法,改进基于随机效用理论的多维Logit模型,形成VEL模型,对VEL模型进行了功能分析和广义出行成本分析,最后论述出行人群的划分方式和集计化。