Efficient estimation for error component seemingly unrelated nonparametric regression models

Multivariate panel data provides a unique opportunity in studying the joint evolution of multiple response variables over time. In this paper, we propose an error component seemingly unrelated nonparametric regression model to fit the multivariate panel data, which is more flexible than the traditional error component seemingly unrelated parametric regression. By applying the undersmoothing technique and taking both of the correlations within and among responses into account, we propose an efficient two-stage local polynomial estimation for the unknown functions. It is shown that the resulting estimators are asymptotically normal, and have the same biases as the standard local polynomial estimators, which are only based on the individual response, and smaller asymptotic variances. The performance of the proposed procedure is evaluated through a simulation study and a real data set.     

部分线性单指数动态面板模型的惩罚二次推断函数估计

本文将个体效应与模型的线性部分和非线性部分同时进行估计,构建了惩罚二次推断函数估计法,估计了个体内具有自相关结构的随机效应部分线性单指数动态面板模型。该方法的优点是能够克服由个体效应引起的内生性问题,因此无论滞后因变量处于线性部分还是处于非参数部分均可得到一致有效的估计。进一步,本文证明了该估计方法的一致性和渐近正态性,同时还用Monte Carlo模拟实验评估了该估计方法在有限样本下的表现。     

The specification of least informative error distributions

Maximum likelihood estimation and most statistical tests require a full specification of the error distribution in a model. Under suitable parametric restrictions we can derive least informative specifications. The autoregressive processes prove to be least informative under a few simple variance and covariance restrictions. For the singular multivariate error distribution in a sum-constrained model, several least informative error distributions are obtained using different parametric assumptions on the covariance structure. A combined maximum entropy — maximum likelihood approach provides an alternative to other recent proposals for covariance estimation in small samples.     

Estimating panel data models in the presence of endogeneity and selection

We consider estimation of panel data models with sample selection when the equation of interest contains endogenous explanatory variables as well as unobserved heterogeneity. Assuming that appropriate instruments are available, we propose several tests for selection bias and two estimation procedures that correct for selection in the presence of endogenous regressors. The tests are based on the fixed effects two-stage least squares estimator, thereby permitting arbitrary correlation between unobserved heterogeneity and explanatory variables. The first correction procedure is parametric and is valid under the assumption that the errors in the selection equation are normally distributed. The second procedure estimates the model parameters semiparametrically using series estimators. In the proposed testing and correction procedures, the error terms may be heterogeneously distributed and serially dependent in both selection and primary equations. Because these methods allow for a rather flexible structure of the error variance and do not impose any nonstandard assumptions on the conditional distributions of explanatory variables, they provide a useful alternative to the existing approaches presented in the literature.     

Non-Parametric Production Analysis of Pesticides Use in the Netherlands

Many previous empirical studies on the productivity of pesticides suggest that pesticides are under-utilized in agriculture despite the general held believe that these inputs are substantially over-utilized. This paper uses data envelopment analysis (DEA) to calculate non-parametric measures of the value of the marginal product of pesticides. Furthermore, the effect of pesticides on the value of the marginal product of productive inputs is investigated in order to analyze technical interdependence between pesticides and productive inputs. Results suggest, in general, substantial under-utilization of pesticides, which is consistent with earlier findings of parametric specifications.     

Non-Linearities in Electricity Demand and Temperature: Parametric Versus Non-Parametric Methods

This paper investigates the relationship between outside air temperature and the residential demand for space heating energy. These non-linearities are investigated empirically using high frequency panel data for a sample of UK households, and both parametric and non-parametric methods for identifying non-linearities are examined. The econometric evidence finds support for important non-linearities across the range of observed temperatures and points to limitations in the use of parametric functional forms.     

Binary response panel data models with sample selection and self‐selection

We consider estimating binary response models on an unbalanced panel, where the outcome of the dependent variable may be missing due to nonrandom selection, or there is self‐selection into a treatment. In the present paper, we first consider estimation of sample selection models and treatment effects using a fully parametric approach, where the error distribution is assumed to be normal in both primary and selection equations. Arbitrary time dependence in errors is permitted. Estimation of both coefficients and partial effects, as well as tests for selection bias, are discussed. Furthermore, we consider a semiparametric estimator of binary response panel data models with sample selection that is robust to a variety of error distributions. The estimator employs a control function approach to account for endogenous selection and permits consistent estimation of scaled coefficients and relative effects.     

半参数趋势阈值面板模型及其参数估计

半参数趋势面板数据模型在社会经济问题的实证分析中具有很强的适用性,但现有的研究中,半参数趋势面板模型考虑了时间趋势的非线性,但没有考虑政策等因素对参数的影响。本文将结构突变理论引入截面相关下的半参数趋势面板模型,并基于PPLE方法,建立了有效估计量和识别程序。通过仿真实验和实证应用,验证了对于含有突变点的半参数趋势面板模型,EPPLE方法的参数估计是有效的。     

Large panels with common factors and spatial correlation

This paper considers methods for estimating the slope coefficients in large panel data models that are robust to the presence of various forms of error cross-section dependence. It introduces a general framework where error cross-section dependence may arise because of unobserved common effects and/or error spill-over effects due to spatial or other forms of local dependencies. Initially, this paper focuses on a panel regression model where the idiosyncratic errors are spatially dependent and possibly serially correlated, and derives the asymptotic distributions of the mean group and pooled estimators under heterogeneous and homogeneous slope coefficients, and for these estimators proposes non-parametric variance matrix estimators. The paper then considers the more general case of a panel data model with a multifactor error structure and spatial error correlations. Under this framework, the Common Correlated Effects (CCE) estimator, recently advanced by Pesaran (2006), continues to yield estimates of the slope coefficients that are consistent and asymptotically normal. Small sample properties of the estimators under various patterns of cross-section dependence, including spatial forms, are investigated by Monte Carlo experiments. Results show that the CCE approach works well in the presence of weak and/or strong cross-sectionally correlated errors.     

Fixed Effects and Random Effects Estimation of Higher-order Spatial Autoregressive Models with Spatial Autoregressive and Heteroscedastic Disturbances

Abstract

This paper develops a unified framework for fixed effects (FE) and random effects (RE) estimation of higher-order spatial autoregressive panel data models with spatial autoregressive disturbances and heteroscedasticity of unknown form in the idiosyncratic error component. We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the disturbance process and define both an RE and an FE spatial generalized two-stage least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators and derive their joint asymptotic distribution, which is robust to heteroscedasticity of unknown form in the idiosyncratic error component. Finally, we derive a robust Hausman test of the spatial random against the spatial FE model.     

Spatial heteroskedasticity and autocorrelation consistent estimation of covariance matrix

This paper considers spatial heteroskedasticity and autocorrelation consistent (spatial HAC) estimation of covariance matrices of parameter estimators. We generalize the spatial HAC estimator introduced by Kelejian and Prucha (2007) to apply to linear and nonlinear spatial models with moment conditions. We establish its consistency, rate of convergence and asymptotic truncated mean squared error (MSE). Based on the asymptotic truncated MSE criterion, we derive the optimal bandwidth parameter and suggest its data dependent estimation procedure using a parametric plug-in method. The finite sample performances of the spatial HAC estimator are evaluated via Monte Carlo simulation.     

A cross-country comparison of productivity growth using the generalised metafrontier Malmquist productivity index: with application to banking industries in Taiwan and China

This paper is an extension of the metafrontier Malmquist productivity index, which takes into account the effect of scale efficiency change in its decomposition for both the non-parametric and parametric frameworks. Meanwhile, the ‘catch-up’ in the index is also disintegrated as two components: pure technological catch-up and frontier catch-up. An empirical application that uses unbalanced panel data of the Taiwanese and Chinese commercial banking industry is also conducted under a parametric framework. The results reveal that the adverse scale efficiency change is the key factor to inducing the inferior productivity growth seen in Chinese banks compared with Taiwanese banks, which spotlights the importance of the scale efficiency change term on productivity measures. It also provides one possible explanation for the recent hot issue about the motives for the two shores of the Taiwan Straits advancing financial openness to each other and mutually signing a banking Memorandum of Understanding.     

Quantile regression under random censoring

Censored regression models have received a great deal of attention in both the theoretical and applied econometric literature. Most of the existing estimation procedures for either cross-sectional or panel data models are designed only for models with fixed censoring. In this paper, a new procedure for adapting these estimators designed for fixed censoring to models with random censoring is proposed. This procedure is then applied to the CLAD and quantile estimators of Powell (J. Econom. 25 (1984) 303, 32 (1986a) 143) to obtain an estimator of the coefficients under a mild conditional quantile restriction on the error term that is applicable to samples exhibiting fixed or random censoring. The resulting estimator is shown to have desirable asymptotic properties, and performs well in a small-scale simulation study.     

Panel estimators and the identification of firm-specific efficiency levels in parametric,semiparametric and nonparametric settings

The paper analyzes a number of competing approaches to modeling efficiency in panel studies. The specifications considered include the fixed effects stochastic frontier, the random effects stochastic frontier, the Hausman–Taylor random effects stochastic frontier, and the random and fixed effects stochastic frontier with an AR(1) error. I have summarized the foundations and properties of estimators that have appeared elsewhere and have described the model assumptions under which each of the estimators have been developed. I discuss parametric and nonparametric treatments of time varying efficiency including the Battese–Coelli estimator and linear programming approaches to efficiency measurement. Monte Carlo simulation is used to compare the various estimators and to assess their relative performances under a variety of misspecified settings. A brief illustration of the estimators is conducted using U.S. banking data.     

Econometric analysis of copyrights

This paper explores the impact of copyrights on firm value and on the demand for firm output. Using panel data on franchise value and ticket sales from the National Football League over the 1991–2000 period, we analyze the effect of copyrights (in this case, team logos) using several parametric estimators, the Arellano and Bond [1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277–297] dynamic panel data estimator, and a semi-non-parametric method based on difference-in-differences propensity score matching. We find a negative effect of logo changes on franchise value that is robust across multiple specifications. In addition, logo changes also appear to have a moderate positive, albeit not particularly robust, impact on ticket sales.     

Bayesian estimation of agent-based models

We consider Bayesian inference techniques for agent-based (AB) models, as an alternative to simulated minimum distance (SMD). Three computationally heavy steps are involved: (i) simulating the model, (ii) estimating the likelihood and (iii) sampling from the posterior distribution of the parameters. Computational complexity of AB models implies that efficient techniques have to be used with respect to points (ii) and (iii), possibly involving approximations. We first discuss non-parametric (kernel density) estimation of the likelihood, coupled with Markov chain Monte Carlo sampling schemes. We then turn to parametric approximations of the likelihood, which can be derived by observing the distribution of the simulation outcomes around the statistical equilibria, or by assuming a specific form for the distribution of external deviations in the data. Finally, we introduce Approximate Bayesian Computation techniques for likelihood-free estimation. These allow embedding SMD methods in a Bayesian framework, and are particularly suited when robust estimation is needed. These techniques are first tested in a simple price discovery model with one parameter, and then employed to estimate the behavioural macroeconomic model of De Grauwe (2012), with nine unknown parameters.     

The use of time series processes to model the error structure of earnings in a longitudinal data analysis

This study presents an error structure that offers a rich statistical framework for panel data analysis. It includes as special cases most of the error specifications found in longitudinal studies of wages and earnings. A general set of procedures for choosing a specification of this error structure and estimating its parameters appears in the first part of this study. The last section applies these procedures to fit an error structure for wages and earnings of prime-age males using data from the Michigan Panel of Income Dynamics.     

Scale economies in electricity distribution: a semiparametric analysis

We estimate the costs of distributing electricity using data on municipal electric utilities in Ontario, Canada for the period 1993–5. The data reveal substantial evidence of increasing returns to scale with minimum efficient scale being achieved by firms with about 20,000 customers. Larger firms exhibit constant or decreasing returns. Utilities which deliver additional services (such as water/sewage), have significantly lower costs, indicating the presence of economies of scope. Our basic specifications comprise semiparametric variants of the translog cost function where output enters non‐parametrically and remaining variables (including their interactions with output) are parametric. We rely upon non‐parametric differencing techniques and extend a previous differencing test of equality of non‐parametric regression functions to a panel data setting. Copyright © 2000 John Wiley & Sons, Ltd.     

基于AEPD分布和ALD分布的VaR模型

本文以成熟市场和新兴市场的六个主要的市场指数为例,将更精确反映金融资产收益率典型事实的AEPD分布和ALD分布运用于股票市场VaR的度量。并与其它常见的非参、半参和参数法VaR模型进行全面比较。实证表明,对于参数法模型,误差项服从ALD分布和正态分布的GARCH族模型分别当且仅当在度量低分位数和高分位数水平下的VaR值时表现优异;而误差项服从AEPD分布的GARCH族模型在度量各种分位数水平下的VaR值时均取得不错的效果。另外对于CAViaR模型,它们在度量VaR时与参数法中表现最好的AR-GJR-GARCH-AEPD(ALD)两个模型效果相当。