A quasi-negative binomial regression with an application to medical care data

Quality & Quantity - This paper introduced a Quasi-Negative Binomial Regression as an extension of Quasi-Negative Binomial to handle response count datasets modulated with covariates. In some...     

An application of data envelopment analysis to evaluate the efficiency level of the operational cost of Brazilian electricity distribution utilities

This paper shows efficiency indices for 60 Brazilian electricity distribution utilities. The efficiency scores are gauged by three DEA models. For both models, these quantities are evaluated under different contexts. One treats with respect to the regulator perspective. The others examine an alternative approach based on cluster analysis and restrictions on factor weights. It is worth pointing out that these developments can reduce the information asymmetry and improve the regulator's skill to compare the performance of the utilities, a fundamental in incentive regulation schemes.     

Local influence analysis for Poisson autoregression with an application to stock transaction data

In statistical diagnostics and sensitivity analysis, the local influence method plays an important role and has certain advantages over other methods in several situations. In this paper, we use this method to study time series of count data when employing a Poisson autoregressive model. We consider case‐weights, scale, data, and additive perturbation schemes to obtain their corresponding vectors and matrices of derivatives for the measures of slope and normal curvatures. Based on the curvature diagnostics, we take a stepwise local influence approach to deal with data with possible masking effects. Finally, our established results are illustrated to be effective by analyzing a stock transactions data set.     

Panel data methods for fractional response variables with an application to test pass rates

We revisit the effects of spending on student performance using data from the state of Michigan. In addition to exploiting a dramatic change in funding in the mid-1990s and subsequent nonsmooth changes, we propose nonlinear panel data models that recognize the bounded nature of the pass rate. Importantly, we show how to estimate average partial effects, which can be compared across many different models (linear and nonlinear) under different assumptions and estimated using many different methods. We find that spending has nontrivial and statistically significant effects, although the diminishing effect is not especially pronounced.     

Semiparametric binary regression models under shape constraints with an application to Indian schooling data

We consider estimation of the regression function in a semiparametric binary regression model defined through an appropriate link function (with emphasis on the logistic link) using likelihood-ratio based inversion. The dichotomous response variable Δ$\Delta$ is influenced by a set of covariates that can be partitioned as (X,Z)$(X,Z)$ where Z$Z$ (real valued) is the covariate of primary interest and X$X$ (vector valued) denotes a set of control variables. For any fixed X$X$, the conditional probability of the event of interest (Δ=1$\Delta =1$) is assumed to be a non-decreasing function of Z$Z$. The effect of the control variables is captured by a regression parameter β$\beta$. We show that the baseline conditional probability function (corresponding to X=0$X=0$) can be estimated by isotonic regression procedures and develop a likelihood ratio based method for constructing asymptotic confidence intervals for the conditional probability function (the regression function) that avoids the need to estimate nuisance parameters. Interestingly enough, the calibration of the likelihood ratio based confidence sets for the regression function no longer involves the usual χ²${\chi }^2$ quantiles, but those of the distribution of a new random variable that can be characterized as a functional of convex minorants of Brownian motion with quadratic drift. Confidence sets for the regression parameter β$\beta$ can however be constructed using asymptotically χ²${\chi }^2$ likelihood ratio statistics. The finite sample performance of the methods are assessed via a simulation study. The techniques of the paper are applied to data sets on primary school attendance among children belonging to different socio-economic groups in rural India.     

On the specification and estimation of macro rural-urban migration functions: with an application to Indian data

The authors suggest several extensions and modifications to the existing specifications of macro migration functions as suggested by Todar through a more careful investigation of the micro theory underlying migration decisions. They use a probabilistic migration model to examine internal migration in India and present evidence that migration tends first to rise and then to fall as rural income rises     

More powerful panel data unit root tests with an application to mean reversion in real exchange rates

Unit root tests, seeking mean or trend reversion, are frequently applied to panel data. We show that more powerful variants of commonly applied tests are readily available. Moreover, power gains persist when the modifications are applied to bootstrap procedures that may be employed when cross‐correlation of a rather general sort among individual panel members is suspected. Copyright © 2004 John Wiley & Sons, Ltd.     

Reducing government bank presence in the Brazilian financial system Why and how

This paper examines the motives for government banking in Brazil. The dependency of the government financial institutions for financial support on the Federal Government are shown to have created loopholes that have made problematic the execution of monetary policy. The recent Federal Government program to reduce the presence of state financial institutions in the financial system is described and examined. The linkage of this program to the renegotiation of state government debt to the Federal Government under subsidized conditions is found to have been critical to state government acceptance of the privatization, extinction, transformation, and restructuring of state government financial institutions.     

Sample size determination of binomial data with the presence of misclassification

We propose a double sampling scheme with two classifiers to address the problem of optimal sample size when misclassification among binomial observations is observed. The classifiers vary with respect to the classifying cost and precision. Furthermore, since the data are unknown, an additional constraint is set on the probability of observing ``undesirable' data. The method is developed following the Bayesian point of view.     

Bayesian inference for the mover–stayer model in continuous time with an application to labour market transition data

This paper presents Bayesian inference procedures for the continuous time mover–stayer model applied to labour market transition data collected in discrete time. These methods allow us to derive the probability of embeddability of the discrete‐time modelling with the continuous‐time one. A special emphasis is put on two alternative procedures, namely the importance sampling algorithm and a new Gibbs sampling algorithm. Transition intensities, proportions of stayers and functions of these parameters are then estimated with the Gibbs sampling algorithm for individual transition data coming from the French Labour Force Surveys collected over the period 1986–2000. Copyright © 2003 John Wiley & Sons, Ltd.     

Exploring the development and transfer of career capital in an international governmental organization

Abstract

This study investigates global career self-management behaviors of staff in an international governmental organization (IGO). The literature on global careers argues that individuals should maximize their career capital, operationalized in the intelligent careers (IC) concept as competencies, social networks, and motivations of persons related to their careers. The IC concept implies that career capital is transferable and argues that IC components are interrelated and self-reinforcing. We explored these assumptions through a case study in a United Nations (UN) organization. Using the IC framework we undertook 29 semi-structured interviews with international assignees, HR, and operational experts and conducted one focus group discussion with seven staffing coordinators. We found that the UN organization had high barriers to career capital transfer between head office and field stations. Therefore, the IGO staff experienced conflicting demands in terms of their career capital behaviors. Many staff did not focus on maximizing their career-relevant capabilities or social networks. Instead, they pursued international careers that intentionally sacrificed internal career progression in favor of their humanitarian aid duties. The research adds to the insights of the global careers literature and refines our understanding of the relationship of the organizational center to its foreign affiliates. The findings expose potentially contradictory behavioral implications of elements of the IC concept and call for a context-sensitive refinement. Managerial implications for resourcing, development, career management, and retention are discussed.     

A smoothed maximum score estimator for the binary choice panel data model with an application to labour force participation

In a binary choice panel data model with individual effects and two time periods, Manski proposed the maximum score estimator based on a discontinuous objective function and proved its consistency under weak distributional assumptions. The rate of convergence is low ( N ^1/3) and its limit distribution cannot easily be used for statistical inference. In this paper we apply the idea of Horowitz to smooth Manski's objective function. The resulting smoothed maximum score estimator is consistent and asymptotically normal with a rate of convergence that can be made arbitrarily close to N ^1/2, depending on the strength of the smoothness assumptions imposed. The estimator can be applied to panels with more than two time periods and to unbalanced panels. We apply the estimator to analyze labour force participation of married Dutch females.     

GMM estimators with improved finite sample properties using principal components of the weighting matrix,with an application to the dynamic panel data model

GMM estimators have poor finite sample properties in highly overidentified models. With many moment conditions the optimal weighting matrix is poorly estimated. We suggest using principal components of the weighting matrix. This effectively drops some of the moment conditions. Our simulations, done in the context of the dynamic panel data model, show that the resulting GMM estimator has better finite sample properties than the usual two-step GMM estimator, in the sense of smaller bias and more reliable standard errors.     

Pseudo empirical likelihood method in the presence of missing data

In this paper, we propose an estimator for the population mean when some observations on the study and auxiliary variables are missing from the sample. The proposed estimator is valid for any unequal probability sampling design, and is based upon the pseudo empirical likelihood method. The proposed estimator is compared with other estimators in a simulation study.     

Dating and forecasting turning points by Bayesian clustering with dynamic structure: a suggestion with an application to Austrian data

The information contained in a large panel dataset is used to date historical turning points and to forecast future ones. We estimate groups of series with similar time series dynamics and link the groups with a dynamic structure. The dynamic structure identifies a group of leading and a group of coincident series. Robust results across data vintages are obtained when series‐specific information is incorporated in the design of the prior group probability distribution. The forecast evaluation confirms that the Markov switching panel with dynamic structure performs well when compared to other specifications. Copyright © 2009 John Wiley & Sons, Ltd.     

Estimation of a survival curve with randomly censored data in the presence of a covariate

This paper deals with the estimation of a survival curve in models with random right censoring and dependent censoring mechanism. We consider a specific dependent censorship model in which conditional on a covariate, the survival and censoring times are assumed to be independent. We investigate asymptotic properties of a corrected version of a survival curve estimator introduced by Cheng (1989). In particular we show uniform strong consistency and weak convergence to a Gaussian process. Comparisons of this estimator with the well-known Kaplan-Meier-estimator are included. Finally, some examples illustrate how the estimator performs. Received: February 2000     

Testing for unit roots in the presence of uncertainty over both the trend and initial condition

In this paper we provide a joint treatment of two major problems that surround testing for a unit root in practice: uncertainty as to whether or not a linear deterministic trend is present in the data, and uncertainty as to whether the initial condition of the process is (asymptotically) negligible or not. We suggest decision rules based on the union of rejections of four standard unit root tests (OLS and quasi-differenced demeaned and detrended ADF unit root tests), along with information regarding the magnitude of the trend and initial condition, to allow simultaneously for both trend and initial condition uncertainty.     

房地产市场中的政府行为外部效应解析

1988年建立的土地有偿使用制度,实现了我国土地产权的市场化,对培育土地市场和促进经济发展发挥了重要作用,但土地产权的界定不够明晰,缺少外部效应内部化的政策机制,引发土地市场的一系列问题.借助新制度经济学的分析框架,从土地产权的角度分析了政府行为对土地市场的影响,并运用特征价格法,对广州市地铁二号线和内环路建设的实例,分析评价了政府行为对土地价值的影响.最后从完善土地产权的角度探讨了政府在土地市场中的角色和作用,指出由于缺乏对政府行为引起的"得益"和"受损"的应对机制,导致政府利益受损和土地市场的不公平与不确定性,需从土地产权角度出发,完善相关政策.     

Biased Bayesian learning with an application to the risk-free rate puzzle

Based on the axiomatic framework of Choquet decision theory, we develop a closed-form model of Bayesian learning with ambiguous beliefs about the mean of a normal distribution. In contrast to rational models of Bayesian learning the resulting Choquet Bayesian estimator results in a long-run bias that reflects the agent's ambiguity attitudes. By calibrating the standard equilibrium conditions of the consumption based asset pricing model we illustrate that our approach contributes towards a resolution of the risk-free rate puzzle. For a plausible parameterization we obtain a risk-free rate in the range of 3.5–5%. This is 1–2.5% closer to the empirical risk-free rate than according calibrations of the rational expectations model.     

Armax model specification testing,with an application to unemployment in the Netherlands

In this paper we present a consistent standard normal model specification test for ARMAX models. The null hypothesis is that the ARMAX model represents the conditional expectation of the dependent variable relative to the entire past of the economic vector time series process under review. This null is tested against the general alternative hypothesis that the null is false. The test is applied to testing the rational expectations-natural rate (RE-NR) hypothesis for the Netherlands according to the approach of Sargent (1976). It appears that RE-NR hypothesis has to be rejected.