Health expenditure models: A comparison using panel data

In this article, we focus on the estimation of outpatient expenditures with panel data. We model the logarithm of expenditures and consider five different models. The first two are two-part and sample selection cross-section models. Two-part panel data models turn out to be inappropriate for dealing with expenditures. We thus estimate sample selection models with panel data: one without a lagged dependent variable and two with a lagged dependent variable. These two latter models differ in their assumptions on the variance of the residuals. Modelling heteroscedasticity may indeed be important to avoid the bias due to the retransformation problem. We show that lagged dependent variables are important factors for heteroscedasticity. For the models with state dependence, we provide a new solution to the initial conditions problem by controlling for generalised residuals. We establish that panel data models highly improve the correlation explained by the model in the time-series dimension without damaging the fit in the cross-section dimension. For all indicators of fit, the model with state dependence and heteroscedasticity seems to dominate the others.     

Moment Estimation of the Probit Model with an Endogenous Continuous Regressor

We propose a generalized method of moments (GMM) estimator with optimal instruments for a probit model that includes a continuous endogenous regressor. This GMM estimator incorporates the probit error and the heteroscedasticity of the error term in the first‐stage equation in order to construct the optimal instruments. The estimator estimates the structural equation and the first‐stage equation jointly and, based on this joint moment condition, is efficient within the class of GMM estimators. To estimate the heteroscedasticity of the error term of the first‐stage equation, we use the k‐nearest neighbour (k‐nn) non‐parametric estimation procedure. Our Monte Carlo simulation shows that in the presence of heteroscedasticity and endogeneity, our GMM estimator outperforms the two‐stage conditional maximum likelihood estimator. Our results suggest that in the presence of heteroscedasticity in the first‐stage equation, the proposed GMM estimator with optimal instruments is a useful option for researchers.     

Simulated Non-Parametric Estimation of Dynamic Models

This paper introduces a new class of parameter estimators for dynamic models, called simulated non-parametric estimators (SNEs). The SNE minimizes appropriate distances between non-parametric conditional (or joint) densities estimated from sample data and non-parametric conditional (or joint) densities estimated from data simulated out of the model of interest. Sample data and model-simulated data are smoothed with the same kernel, which considerably simplifies bandwidth selection for the purpose of implementing the estimator. Furthermore, the SNE displays the same asymptotic efficiency properties as the maximum-likelihood estimator as soon as the model is Markov in the observable variables. The methods introduced in this paper are fairly simple to implement, and possess finite sample properties that are well approximated by the asymptotic theory. We illustrate these features within typical estimation problems that arise in financial economics.     

Finite sample performance of specification tests for correlated random effects quantile panel regressions

We investigate the finite sample performance of the Abrevaya and Dahl (2008) test for coefficient heterogeneity for a correlated random effects ‘mean’ (CREM) panel quantile regression estimator. We assess size and power of the test over a range of sample sizes and panel dimensions. The test is undersized for small-to-moderate sample sizes and displays low power even with a high degree of heteroscedasticity. Size and power improve substantially in larger samples. Our results provide insight for applied researchers.     

Does the Confucius institute impact international travel to China? A panel data analysis

This article examines the impact of Confucius Institutes on inbound travel to China. We estimate a panel gravity model of inbound tourism flows to China between 2004 and 2010. We use a Poisson pseudo-maximum likelihood estimator to control for heteroscedasticity endemic in gravity models (Santos Silva and Tenreyro, 2006). We find that the presence of Confucius Institute(s) in the source country increases overall tourism in general and business and worker tourists in particular.     

EVIDENCE ON GENDER WAGE DISCRIMINATION IN PORTUGAL: PARAMETRIC AND SEMI‐PARAMETRIC APPROACHES

In this paper we consider several alternative approaches to analyze gender wage discrimination. Besides the traditional OLS estimator, we use two other approaches to control for sample selection bias problems: the parametric procedure suggested by Vella and Wooldridge, and the Li and Wooldridge semi‐parametric estimator. We study the case of Portugal, employing data from the European Community Household Panel. The results reveal that the discrimination estimates are sensitive to the different econometric approaches. In fact, when sample selection bias is taken into account, the discrimination values are reduced and are typically not significant.     

Markov regime-switching Beta-t-EGARCH

We suggest a Markov regime-switching (MS) Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model for U.S. stock returns. We compare the in-sample statistical performance of the MS Beta-t-EGARCH model with that of the single-regime Beta-t-EGARCH model. For both models we consider leverage effects for conditional volatility. We use data from the Standard Poor’s 500 (S&P 500) index and also a random sample that includes 50 components of the S&P 500. We study the outlier-discounting property of the single-regime Beta-t-EGARCH and MS Beta-t-EGARCH models. For the S&P 500, we show that for the MS Beta-t-EGARCH model extreme observations are discounted more for the low-volatility regime than for the high-volatility regime. The conditions of consistency and asymptotic normality of the maximum likelihood estimator are satisfied for both the single-regime and MS Beta-t-EGARCH models. All likelihood-based in-sample statistical performance metrics suggest that the MS Beta-t-EGARCH model is superior to the single-regime Beta-t-EGARCH model. We present an application to the out-of-sample density forecast performance of both models. The results show that the density forecast performance of the MS Beta-t-EGARCH model is superior to that of the single-regime Beta-t-EGARCH model.     

Nonparametric Generalized Least Squares in Applied Regression Analysis

This paper compares a nonparametric generalized least squares (NPGLS) estimator to parametric feasible GLS (FGLS) and variants of heteroscedasticity robust standard error estimators (HRSE) in an applied setting. NPGLS consistently estimates the unknown scedastic function and produces more efficient parameter estimates than HRSE. We apply these various approaches for handling heteroscedasticity to data on professor rankings obtained from RateMyProfessors.com. We find that the statistical significance of key variables differs across seven versions of HRSE, leading to different conclusions, and a standard parametric approach to FGLS suffers from misspecification. NPGLS combines the virtues of both of these parametric approaches.     

Non‐discriminatory Trade Policies in Panel Structural Gravity Models: Evidence from Monte Carlo Simulations

This paper provides Monte Carlo (MC) simulation evidence on the performance of methods used for identifying the effects of nondiscriminatory trade policy (NDTP) variables in panel structural gravity models. The benchmarked methods include a fixed effect (FE) estimator that utilizes data on intra national trade flows, the bonus‐vetus (BV) and the two‐stage fixed effect (FE‐2S) estimator. The results indicate that only the FE estimates are unbiased and consistent under very general assumptions of the data generating process. The favourable asymptotic properties of the FE estimator unfold as the number of period T increases.     

Semiparametric and nonparametric testing for long memory: A Monte Carlo study

The finite sample properties of three semiparametric estimators, several versions of the modified rescaled range, MRR, and three versions of the GHURST estimator are investigated. Their power and size for testing for long memory under short-run effects, joint short and long-run effects, heteroscedasticity andt-distributions are given using Monte Carlo methods. The MRR with the Bartlett window is generally robust with the disadvantage of a relatively small power. The trimmed Whittle likelihood has high power in general and is robust except for large short-run effects. The tests are applied to changes in exchange rate series (daily data) of 6 major countries. The hypothesis of no fractional integration is rejected for none of the series.     

Trade Costs,Resource Reallocation and Productivity in Developing Countries

New trade models with heterogeneous firms suggest that international trade plays an important role in reallocating resources from low to high productivity plants. We use plant‐level data from Chile and measures of trade costs that include tariffs and freight rates to analyze the importance of this trade‐induced market selection process. We find that trade costs affect the reallocation process through the various channels predicted by the theory; however, the effects are approximately half of those reported for the US. We also find that while the tariff rate is responsible for preventing some of the reallocation, transportation costs have the most limiting role in terms of the number of channels affected.     

中国反倾销申诉寒蝉效应的实证分析——基于动态面板模型的GMM检验

本文基于中国2001年-2008年对外反倾销案例的面板数据,通过构造横截面单元为中国对外反倾销调查的动态面板数据模型,应用一阶差分广义矩估计方法考察了中国实施反倾销措施及其贸易自由化对中国进口贸易的影响。实证结果表明,贸易自由化对中国进口贸易的促进作用效果非常显著;中国反倾销立案调查对进口贸易有一定的震慑作用,但并未起到限制进口贸易的作用,也就是说中国对外反倾销并非是要构筑贸易壁垒体系,更没有将之作为抵消贸易自由化效应的政策工具。     

Intra-tourism trade,income distribution and tourism endowment: an econometric investigation

Empirical investigation (Nowak et al., 2012) points out that vertical intra-industry trade (VIIT) in Europe is the dominant type of intra-industry trade (IIT) in the tourism sector. This article is the first in tourism literature to test separately the determinants of vertically and horizontally differentiated services, using the most recent models in the theory of IIT. We examine bilateral trade among all trading partners of the sample of European countries, covering the period from 2000 to 2008. We show that differences in gross domestic product per capita and the income-distribution overlap, as well as cultural proximity, are the most significant driving forces behind VIIT for European countries. Geographic distance has a negative effect, whereas specific tourism endowments and relative size of the economies are less conclusive. These results confirm theory predictions and most of the empirical findings related to the pattern of VIIT for the manufacturing sector. As expected, we find that determinants of VIIT cannot explain horizontal intra-industry trade in tourism. We suggest two alternative methods of estimation: generalized least squares logistic function and the fractional logit estimator. We conclude that there are common factors explaining IIT in the manufacturing and tourism trades.     

A COMPUTATIONALLY PRACTICAL SIMULATION ESTIMATION ALGORITHM FOR DYNAMIC PANEL DATA MODELS WITH UNOBSERVED ENDOGENOUS STATE VARIABLES*

This article develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate the estimator has good small sample properties. We apply the estimator to a model of female labor supply and show that the rarely used Polya model fits the data substantially better than the popular Markov model. The Polya model also produces far less state dependence and many fewer race effects and much stronger effects of education, young children, and husband's income on female labor supply decisions.     

Evaluating Social Policy by Experimental and Nonexperimental Methods

Although it is important to establish causal relationships in social policy evaluation, the effects are difficult to observe due to sample selection. To evaluate the performance of estimators designed to handle sample selection bias, we analyse data from a Norwegian rehabilitation project with a randomised experimental design. The data permit us to compare the performance of different nonexperimental estimators with the experimental results. In our case study we find that nonexperimental evaluation based on sample selection estimators with selection terms that fail to meet conventional levels of statistical significance is highly unreliable. The difference in difference estimator and propensity score matching estimators perform better in our context.
JEL classification : C 51; J 24; H 43; I 12     

Dynamic conditional score models of degrees of freedom: filtering with score-driven heavy tails

This article extends the quasi-autoregressive (QAR) plus Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) dynamic conditional score (DCS) model. For the new DCS model, the degrees of freedom parameter is time varying and tail thickness of the error term is updated by the conditional score. We compare the performance of QAR plus Beta-t-EGARCH with constant degrees of freedom (benchmark model) and QAR plus Beta-t-EGARCH with time-varying degrees of freedom (extended model). We use data from the Standard and Poor’s 500 (S&P 500) index, and a random sample of its 150 components that are from different industries of the United States (US) economy. For the S&P 500, all likelihood-based model selection criteria support the extended model, which identifies extreme events with significant impact on the US stock market. We find that for 59% of the 150 firms, the extended model has a superior statistical performance. The results suggest that the extended model is superior for those industries, which produce products that people usually are unwilling to cut out of their budgets, regardless of their financial situation. We perform an application to compare the density forecast performance of both DCS models. We perform an application to Monte Carlo value-at-risk for both DCS models.     

Trade protection and bureaucratic corruption: an empirical investigation

Abstract .  We examine whether protectionist trade policies lead to increased bureaucratic corruption. Using multiple measures of corruption and trade policies, we find strong evidence that corruption is significantly higher in countries with protectionist trade policies. These results are robust to endogeneity concerns. Next, a panel-data-based GMM methodology is used to estimate a dynamic model of corruption. This estimator controls for country-specific effects, potential endogeneity of trade policy, and existence of measurement errors afflicting the corruption data. The paper strengthens the case for trade liberalization and argues that trade reforms may lead to improvements in governance.     

Semiparametric Estimation of Simultaneous-Equation Microeconometric Models with Index Restrictions

This article introduces semiparametric methods for the estimation of simultaneous-equation microeconometric models with index restrictions. The methods are motivated by a semiparametric minimum-distance procedure, which unifies the estimation of both regression-type and linear or nonlinear simultaneous-equation models without emphasis on the construction of instrumental variables. Single-equation and systematic estimation methods and optimal weighting procedures are considered. The estimators are √ n -consistent and asymptotically normal. For the estimation of nonparametric regression and some sample selection models where the variances of disturbances are functions of the same indices, the optimal weighted estimator attains Chamberlain's efficient bound for models with conditional moment restrictions. The weighted estimator is shown to be optimal within a class of semiparametric instrumental variables estimators.
JEL classification numbers: C14, C24, C34.     

Variable selection problem in the censored regression models

In this paper we discuss the variable selection problem for the censored regression models. The Schme-Hahn (1979) estimator for the censored normal model and the Buckley-James (1979) estimator for the non-parametric censored model are discussed. It is shown, through the EM algorithm, that the variable selection problem for these estimators can be converted into a variable selection problem in a standard linear regression model. We show that the expectation of maximum likelihood residuals converges to zero in large samples.     

Nonparametric methods for estimating and testing for constant betas in asset pricing models

In this article, we study the performance of a smoothing spline method in estimating and testing for constant betas in two well-known asset pricing models, the usual market model and the three-factor model. The spline estimator is computed taking into account the conditional heteroscedasticity of the errors. Using the right model and estimation procedure for the variance term plays a crucial role in gaining efficiency in beta estimators. A simulation study shows the good performance of our method; in all the scenarios simulated, it outperforms the benchmark rolling estimator. The method enables users to obtain confidence intervals and to test for the significance and constancy of betas. Finally, the method is applied to US data, comprising 25 portfolios formed based on size and the ratio of book equity to market equity. The results show that the time-variability of the betas plays an important role, mainly when sensitivity to the HML factor is considered.