Pseudolikelihood estimation of the stochastic frontier model

Stochastic frontier analysis is a popular tool to assess firm performance. Almost universally it has been applied using maximum likelihood (ML) estimation. An alternative approach, pseudolikelihood (PL) estimation, which decouples estimation of the error component structure and the production frontier, has been adopted in both the non-parametric and panel data settings. To date, no formal comparison has yet to be conducted comparing these methods in a standard, parametric cross-sectional framework. We produce a comparison of these two competing methods using Monte Carlo simulations. Our results indicate that PL estimation enjoys almost identical performance to ML estimation across a range of scenarios and performance metrics, and for certain metrics, outperforms ML estimation when the distribution of inefficiency is incorrectly specified.     

Aggregation biases in production functions: a panel data analysis of Translog models

The value of panel data for micro-units in exploring aggregated relationships empirically is substantial, since such data are not only useful for estimation at both the micro- and the macro-level, but also allow comparison of properties of relationships aggregated formally from the micro-level with those resulting from aggregation by analogy in an informal way. Using panel data from manufacturing plants, we consider biases in the aggregation of the Translog production function, which does not generally satisfy the strict mathematical conditions for perfect aggregation. Linear and log-linear aggregation in the presence of random coefficients are considered. Under linear aggregation across plants, departures between geometric and arithmetic means of inputs as well as correlation between log-inputs, contribute substantially not only to biases in the output volume, but also to instability in derived input and scale elasticities and biases in total factor productivity growth. Overall, the biases under log-linear aggregation are smaller.     

Seemingly unrelated regressions with spatial error components

This article considers various estimators using panel data seemingly unrelated regressions (SUR) with spatial error correlation. The true data generating process (DGP) is assumed to be SUR with spatial error of the autoregressive or moving average type. Moreover, the remainder term of the spatial process is assumed to follow an error component structure. Both maximum likelihood (ML) and generalized moments (GM) methods of estimation are used. Using Monte Carlo experiments, we check the performance of these estimators and their forecasts under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous versus homogeneous panel data models.     

Panel data inference under spatial dependence

This paper focuses on inference based on the standard panel data estimators of a one-way error component regression model when the true specification is a spatial error component model. Among the estimators considered, are pooled OLS, random and fixed effects, maximum likelihood under normality, etc. The spatial effects capture the cross-section dependence, and the usual panel data estimators ignore this dependence. Two popular forms of spatial autocorrelation are considered, namely, spatial autoregressive random effects (SAR-RE) and spatial moving average random effects (SMA-RE). We show that when the spatial coefficients are large, test of hypothesis based on the standard panel data estimators that ignore spatial dependence can lead to misleading inference.     

SPATIAL PANELS: RANDOM COMPONENTS VERSUS FIXED EFFECTS*

This article investigates spatial panel data models with a space–time filter in disturbances. We consider their estimation by both fixed effects and random effects specifications. With a between equation properly defined, the difference of the random versus fixed effects models can be highlighted. We show that the random effects estimate is a pooling of the within and between estimates. A Hausman‐type specification test and an Lagrangian multiplier test are proposed for the testing of the random components specification versus the fixed effects specification.     

Autocorrelation and masked heterogeneity in panel data models estimated by maximum likelihood

In a panel data model with random effects, when autocorrelation in the error is considered, (Gaussian) maximum likelihood estimation produces a dramatically large number of corner solutions: the variance of the random effect appears (incorrectly) to be zero, and a larger autocorrelation is (incorrectly) assigned to the idiosyncratic component. Thus heterogeneity could (incorrectly) be lost in applications to panel data with customarily available time dimension, even in a correctly specified model. The problem occurs in linear as well as nonlinear models. This article aims at pointing out how serious this problem can be (largely neglected by the panel data literature). A set of Monte Carlo experiments is conducted to highlight its relevance, and we explain this unpleasant effect showing that, along a direction, the expected log-likelihood is nearly flat.     

Estimating elasticities of residential energy demand from panel county data using dynamic random variables models with heteroskedastic and correlated error terms

A dynamic random variables model correcting for heteroskedastic and correlated error terms over time and space and dynamic demand and using panel county data offers consistent and efficient elasticity estimates of residential electricity and natural gas demands. The model developed by Swamy [Swamy, P.A.V.B., 1974. Linear models with random coefficients. In: P. Zarembka (Eds.), Frontiers in Econometrics, Academic Press, London, pp. 143–168.] with a modification suggested by Maddala et al. [Maddala, G.S., Trost, R.P., Li, H., Joutz, F., 1997. Estimation of short-run and long-run elasticities of energy demand from panel data using shrinkage estimators. Journal of Business and Economic Statistics 15, 90–100.] uses a panel of selected California counties for the years 1983–1997 to yield elasticity estimates that differ from those obtained from more standard panel data procedures.     

Pricing to Market of German Exporters: Evidence from Panel Data

This paper investigates pricediscrimination of German exporters across differentforeign markets. We examine the degree of pass-throughof exchange rate fluctuations in the pricing of 70export items. The model is estimated using panel dataon export unit values. Parameter estimation relies onGMM first difference, fixed effects, LAD, OLS firstdifference, and the random coefficients model. Themain results for 70 manufactured goods and 15destination countries between 1990–1994 are: Thedegree of pricing to market differs among destinationsand products. Highest pricing to market is observedfor U.S., Japan, Italy and Spain. Pricing to market ismore prevalent in exports of chemicals and fertilisersthan in machinery products.     

Government spending and economic growth in the OECD countries

Using panel data from 1995 to 2011 for 34 OECD countries, we examine the effects of government consumption spending, public social spending, and public investment on economic growth. We use a generalized method of moments estimation technique to solve inconsistency problems with fixed effects and random effects panel estimation. We find that an increase in public social spending has a significant negative effect on subsequent economic growth. Government consumption spending and public investment have no significant effect on subsequent economic growth.     

Estimation of a Tobit model with unknown censoring threshold

Conventional wisdom suggests that only the estimated intercept is affected by imposition of a zero censoring threshold on a Tobit model. This is true for Heckman-Lee estimation. For maximum likelihood (ML) estimation, however, it is only true if the censoring threshold is known and is subtracted from the dependent variable. Failure to properly transform the dependent variable prior to ML estimation of a zero threshold Tobit model will generally bias the coefficient estimates. A long neglected topic is ML estimation of a Tobit model with common, but unknown, censoring threshold. This paper shows that the ML estimator of the censoring threshold is the minimum order statistic from the observed subsample, and that existing software for estimation of a zero-threshold Tobit model is easily adapted to include estimation of the censoring threshold.     

STOCHASTIC ANALYSIS OF INPUT–OUTPUT MULTIPLIERS ON THE BASIS OF USE AND MAKE TABLES

Although technical coefficients are estimated on the basis of flow data (use and make matrices), they are rarely treated as random variables. If this is done, an error term is added to the coefficients, rather than derived from the distribution of the data. Even so, the calculation of multipliers, by means of the Leontief inverse, is difficult. Due to the nonlinearity of this operation, the multiplier estimates are biased. By going back to the flow data, this paper provides unbiased and consistent employment and output multipliers estimates for the Andalusian economy. Rectangular use and make matrices are accommodated and problems associated with the construction and estimation of technical coefficients and the Leontief inverse are circumvented.     

城乡收入差距影响因素的非参数逐点回归解析

文章利用1987-2008年我国的相关统计数据构建了非参数面板数据模型,采用非参数逐点回归估计方法,估计了我国的教育投入、教育产出、城市化水平、经济开放度、产业结构和政府经济行为等变量对城乡收入差距的影响,刻画了各影响因素关于自变量系数的动态演进趋势。研究表明:各影响因素关于自变量系数的逐点估计结果共有四种类型:"倒U型"、"U型"、"上升型"和"下降型";各影响因素对缩小城乡收入差距都有积极的作用,但在不同时期所起的作用不同。     

Asset Pricing and Cost of Equity in the Tunisian Banking Sector: Panel Data Evidence

In spite of popularity and theoretical simplicity of the one-factor Capital Asset Pricing Model (CAPM) used in the valuation of financial assets, researchers are more concerned with the important extension proposed by Fama and French (1993) , that is, the Three-Factor Pricing Model (TFPM). Alongside beta, average stock returns could be explained by some size and book-to-market supplementary effects. With these two complementary models, estimation of the cost of equity is carried out for the Tunisian banking sector. In order to account for inter-individual heterogeneity, estimation of parameters is conducted according to random coefficient specifications within the context of panel data analysis.     

A model of attrition and income for dynamic longitudinal surveys

This paper develops a random effects model of attrition and income applicable to a dynamic longitudinal survey such as the Survey of Income and Program Participation. Based on the partial dynamic adjustment hypothsis, this study finds that the speed of adjustment of income is not instantaneous as suggeted in many past studies using annual panel data. Also, the short-run coefficients are much smaller than their long-run counterparts, and the coefficient estimates differ substantially among individuals with divergent socioeconomic characteristics. Caution should therefore be exercised when applying dynamic panel data to models with the assumption of an instantaneous speed of adjustment.     

GMM estimation of panel data models with time-varying slope coefficients

A difference/system generalized method of moments (GMM) model that imposes time-constant coefficients is common in empirical studies using panel data. However, a rejection by the Sargan–Hansen test is sometimes a serious concern for researchers. We highlight the fact that the Sargan–Hansen test for GMM estimators applied to panel data is a joint test of valid orthogonality conditions and coefficient stability over time. A possible reason for a rejection is therefore that the slope coefficients vary over time. One solution is to estimate an empirical model in which the coefficients are time specific. We apply this solution to the system GMM estimator of simple nondynamic Cobb–Douglas production functions for a selection of Swedish industries and find that relaxing the assumption of constant slope coefficients results in more satisfactory outcomes of the Sargan–Hansen test.     

Convenient estimators for the panel probit model: Further results

Bertschek and Lechner (1998) propose several variants of a GMM estimator based on the period specific regression functions for the panel probit model. The analysis is motivated by the complexity of maximum likelihood estimation and the possibly excessive amount of time involved in maximum simulated likelihood estimation. But, for applications of the size considered in their study, full likelihood estimation is actually straightforward, and resort to GMM estimation for convenience is unnecessary. In this note, we reconsider maximum likelihood based estimation of their panel probit model then examine some extensions which can exploit the heterogeneity contained in their panel data set. Empirical results are obtained using the data set employed in the earlier study. Helpful comments and suggestions by Irene Bertschek and Michael Lechner are gratefully acknowledged. This paper has also benefited from comments by two anonymous referees and from seminar participants at the Center for Health Economics at the University of York. Any remaining errors are the responsibility of the author.     

Democracy and economic growth in Sub-Saharan Africa: a panel data approach

This paper studies the link between democracy and economic development for 28 countries of Sub-Saharan Africa for the period 1980–2005 in a panel data framework. A democracy index constructed from the Freedom House indices. A variety of panel data unit root and cointegration tests are applied. The variables are found to be integrated of order one and cointegrated. The Blundell–Bond system generalized methods-of-moments is employed to conduct a panel error-correction mechanism based causality test within a vector autoregressive structure. Economic growth is found to cause democracy in the short-run, while bidirectionality is uncovered in the long-run. In addition, the long-run coefficients are estimated through the panel fully modified ordinary least squares and dynamic ordinary least squares methods. Democracy has a positive impact on GDP and vice versa. These results lend support to the virtuous cycle hypothesis.     

Economic Growth and Labor Standards: Evidence from a Dynamic Panel Data Model

The relations between economic growth and international labor standards are explored in a panel of 121 countries from 1974 to 2004. A large literature has empirically tested the neoclassical and endogenous growth models using cross-sectional or panel regressions. Here, the growth model is augmented with labor standards. A dynamic panel data model is used to account for the endogeneity of the determinants of economic growth and labor standards. Two measures of labor standards are used: the rate of work injuries and the rate of strikes and lockouts . The estimation results show that higher levels of labor standards are associated with higher rates of economic growth.     

Influence of body image in urbanized areas: differences in long-term changes in teenage body mass index between boys and girls in Japan

Japanese prefecture level panel data for the period 1986?C2003 was used to analyze and compare the determinants of teenage body mass index (BMI) by sex. Major findings through random effects estimation were as follows: BMI was not influenced by the degree of population density in males and younger females. However, the BMI of 16-year-old females was markedly lower in more densely populated residential areas. These findings suggest that girls who reach adolescence are more likely to diet and that this tendency is more distinct in more urbanized areas.     

APPLICATION OF PANEL DATA MODELS IN BENCHMARKING ANALYSIS OF THE ELECTRICITY DISTRIBUTION SECTOR*

ABSTRACT ** : This paper explores the application of several panel data models in measuring productive efficiency of the electricity distribution sector. Stochastic Frontier Analysis has been used to estimate the cost‐efficiency of 59 distribution utilities operating over a nine‐year period in Switzerland. The estimated coefficients and inefficiency scores are compared across three different panel data models. The results indicate that individual efficiency estimates are sensitive to the econometric specification of unobserved firm‐specific heterogeneity. This paper shows that alternative panel models such as the ‘true’ random effects model proposed by Greene (2005) could be used to explore the possible impacts of unobserved firm‐specific factors on efficiency estimates. When these factors are specified as a separate stochastic term, the efficiency estimates are substantially higher suggesting that conventional models could confound efficiency differences with other unobserved variations among companies. On the other hand, refined specification of unobserved heterogeneity might lead to an underestimation of inefficiencies by mistaking potential persistent inefficiencies as external factors. Given that specification of inefficiency and heterogeneity relies on non‐testable assumptions, there is no conclusive evidence in favour of one or the other specification. However, this paper argues that alternative panel data models along with conventional estimators can be used to obtain approximate lower and upper bounds for companies' efficiency scores.