nep-ecm New Economics Papers
on Econometrics
Issue of 2010‒11‒06
nineteen papers chosen by
Sune Karlsson
Orebro University

  1. Moment Restriction-based Econometric Methods: An Overview By Naoto Kunitomo; Michael McAleer; Yoshihiko Nishiyama
  2. One-Step Robust Estimation of Fixed-Effects Panel Data Models By Aquaro, M.; Cizek, P.
  3. Semiparametric Estimation in Simultaneous Equations of Time Series Models By Jiti Gao; Peter C. B. Phillips
  4. Simultaneous Testing of Mean and Variance Structures in Nonlinear Time Series Models By Song Xi Chen; Jiti Gao
  5. IV Estimation of Panels with Factor Residuals By Robertson, Donald; Sarafidis, Vasilis; Symons, James
  6. Inference on Time-Invariant Variables using Panel Data: A Pre-Test Estimator with an Application to the Returns to Schooling By Jean-Bernard Chatelain; Kirsten Ralf
  7. Anova-type consistent estimators of variance components in unbalanced multi-way error components models By Giovanni S. F. Bruno
  8. Robust Estimation and Forecasting of the Capital Asset Pricing Model By Guorui Bian; Michael McAleer; Wing-Keung Wong
  9. DSGE model restrictions for structural VAR identification By Liu, Philip; Theodoridis, Konstantinos
  10. Nonlinear Cointegration, Misspecification and Bimodality By MArcelo Cunha Medeiros; Eduardo Mendes; Les Oxley
  11. Negative variance estimates in panel data models By Giorgio Calzolari; Laura Magazzini
  12. Estimation in Semiparametric Time Series Regression By Jia Chen; Jiti Gao; Degui Li
  13. Growth Rate Estimation in the presence of Unit Roots By Monojit Chatterji; Homagni Choudhury
  14. Do price and volatility jump together? By Jean Jacod; Viktor Todorov
  15. Jump-Diffusion Calibration using Differential Evolution By Ardia, David; Ospina, Juan; Giraldo, Giraldo
  16. The Impact of Protest Responses in Choice Experiments By Melina Barrio; Maria Loureiro
  17. Aggregation versus Heterogeneity in Cross-Country Growth Empirics. By Markus Eberhardt; Francis Teal
  18. Sensitivity and robustness in MDS configurations for mixed-type data: a study of the economic crisis impact on socially vulnerable Spanish people By Aurea Grané; Rosario Romera
  19. Estimating Marginal Returns to Education By Carneiro, Pedro; Heckman, James J.; Vytlacil, Edward

  1. By: Naoto Kunitomo (Faculty of Economics, University of Tokyo); Michael McAleer (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University); Yoshihiko Nishiyama (Institute of Economic Research, Kyoto University)
    Abstract: Moment restriction-based econometric modelling is a broad class which includes the parametric, semiparametric and nonparametric approaches. Moments and conditional moments themselves are nonparametric quantities. If a model is specified in part up to some finite dimensional parameters, this will provide semiparametric estimates or tests. If we use the score to construct moment restrictions to estimate finite dimensional parameters, this yields maximum likelihood (ML) estimates. Semiparametric or nonparametric settings based on moment restrictions have been the main concern in the literature, and comprise the most important and interesting topics. The purpose of this special issue on “Moment Restriction-based Econometric Methods” is to highlight some areas in which novel econometric methods have contributed significantly to the analysis of moment restrictions, specifically asymptotic theory for nonparametric regression with spatial data, a control variate method for stationary processes, method of moments estimation and identifiability of semiparametric nonlinear errors-in-variables models, properties of the CUE estimator and a modification with moments, finite sample properties of alternative estimators of coefficients in a structural equation with many instruments, instrumental variable estimation in the presence of many moment conditions, estimation of conditional moment restrictions without assuming parameter identifiability in the implied unconditional moments, moment-based estimation of smooth transition regression models with endogenous variables, a consistent nonparametric test for nonlinear causality, and linear programming-based estimators in simple linear regression.
    Keywords: Moment restrictions, Parametric, semiparametric and nonparametric methods; Estimation; Testing; Robustness; Model misspecification.
    Date: 2010–10
  2. By: Aquaro, M.; Cizek, P. (Tilburg University, Center for Economic Research)
    Abstract: The panel-data regression models are frequently applied to micro-level data, which often suffer from data contamination, erroneous observations, or unobserved heterogeneity. Despite the adverse effects of outliers on classical estimation methods, there are only a few robust estimation methods available for fixed-effect panel data. Aiming at estimation under weak moment conditions, a new estimation approach based on two different data transformation is proposed. Considering several robust estimation methods applied on the transformed data, we derive the finite-sample, robust, and asymptotic properties of the proposed estimators including their breakdown points and asymptotic distribution. The finite-sample performance of the existing and proposed methods is compared by means of Monte Carlo simulations.
    Keywords: breakdown point;fixed effects;panel data;robust estimation
    JEL: C23
    Date: 2010
  3. By: Jiti Gao (School of Economics, University of Adelaide); Peter C. B. Phillips
    Abstract: A system of vector semiparametric nonlinear time series models is studied with possible dependence structures and nonstationarities in the parametric and nonparametric components. The parametric regressors may be endogenous while the nonparametric regressors are strictly exogenous. The parametric regressors may be stationary or nonstationary and the nonparametric regressors are nonstationary time series. Semiparametric least squares (SLS) estimation is considered and its asymptotic properties are derived. Due to endogeneity in the parametric regressors, SLS is not consistent for the parametric component and a semiparametric instrumental variable least squares (SIVLS) method is proposed instead. Under certain regularity conditions, the SIVLS estimator of the parametric component is shown to be consistent with a limiting normal distribution. Interestingly, the rate of convergence in the parametric component depends on the properties of the regressors. It has been shown that the conventional rate is still achievable even when nonstationarity is involved in both the regressors.
    Keywords: Dynamic simultaneous equation, endogeneity, exogeneity, non-stationarity, partially linear model, vector semiparametric regression
    JEL: C23 C25
    Date: 2010–10
  4. By: Song Xi Chen (Guanghua School of Management, Peking University); Jiti Gao (School of Economics, University of Adelaide)
    Abstract: This paper proposes a nonparametric simultaneous test for parametric specification of the conditional mean and variance functions in a time series regression model. The test is based on an empirical likelihood (EL) statistic that measures the goodness of fit between the parametric estimates and the nonparametric kernel estimates of the mean and variance functions. A unique feature of the test is its ability to distribute natural weights automatically between the mean and the variance components of the goodness{of{t. To reduce the dependence of the test on a single pair of smoothing bandwidths, we construct an adaptive test by maximizing a standardized version of the empirical likelihood test statistic over a set of smoothing bandwidths. The test procedure is based on a bootstrap calibration to the distribution of the empirical likelihood test statistic. We demonstrate that the empirical likelihood test is able to distinguish local alternatives which are different from the null hypothesis at an optimal rate.
    Keywords: Bootstrap, empirical likelihood, goodness{of{t test, kernel estimation, least squares empirical likelihood, rate-optimal test
    Date: 2010–10
  5. By: Robertson, Donald; Sarafidis, Vasilis; Symons, James
    Abstract: This paper considers panel data regression models with weakly exogenous or endogenous regressors and residuals generated by a multi-factor error structure. In this case, the standard dynamic panel estimators fail to provide consistent estimates of the parameters. We propose a new estimation approach, based on instrumental variables, which retains the traditional attractive features of method of moments estimators. One novelty of our approach is that we introduce new parameters to represent the unobserved covariances between the instruments and the factor component of the residual; these parameters are typically estimable when N is large. Some important estimation and identification issues are studied in detail. The finite-sample performance of the proposed estimators is investigated using simulated data. The results show that the method produces reliable estimates of the parameters over various parametrizations and is robust to large values of the autoregressive parameter and/or the variance of the factor loadings.
    Keywords: Method of Moments; Dynamic Panel Data; Factor Residuals.
    JEL: C23
    Date: 2010–10–25
  6. By: Jean-Bernard Chatelain (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I); Kirsten Ralf (PSE - Paris-Jourdan Sciences Economiques - CNRS : UMR8545 - Ecole des Hautes Etudes en Sciences Sociales (EHESS) - Ecole des Ponts ParisTech - Ecole Normale Supérieure de Paris - ENS Paris)
    Abstract: This paper proposes a new pre-test estimator of panel data models including time invariant variables based upon the Mundlak-Krishnakumar estimator and an "unrestricted” Hausman-Taylor estimator. The paper evaluates the biases of currently used restricted estimators, omitting the average-over-time of at least one endogenous time-varying explanatory variable. Repeated Between, Ordinary Least Squares, Two stage restricted Between and Oaxaca-Geisler estimator, Fixed Effect Vector Decomposition, Generalized least squares may lead to wrong conclusions regarding the statistical significance of the estimated parameter values of time-invariant variables.
    Keywords: Time-Invariant Variables, Panel data, Time-Series Cross-Sections, Pre-Test Estimator, Mundlak Estimator, Fixed Effects Vector Decomposition
    Date: 2010–01–15
  7. By: Giovanni S. F. Bruno (Department of Economics, Bocconi University, Milan, Italy)
    Abstract: This paper introduces three new Anova-type consistent estimators of variance components for use in multi-way unbalanced error components models, with possibly non-normal errors and endogenous regressors. They are easy to compute and are proved to be consistent under mild regularity conditions. For the first time proofs of consistency for Anova estimators are offered under such a general class of models, providing novel insights into the impact of unbalancedness on the large-sample properties of the estimators. A battery of Monte Carlo experiments and an empirical application to US production data show that the estimators perform reasonably well, in comparison to unbiased methods incorporating finite-sample corrections.
    Keywords: variance components, Anova-type estimators, multi-way error components models, unbalancedness, endogenous regressors
    JEL: C23
    Date: 2010–10
  8. By: Guorui Bian (Department of Statistics, East China Normal University); Michael McAleer (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University); Wing-Keung Wong (Department of Economics, Hong Kong Baptist University)
    Abstract: In this paper, we develop a modified maximum likelihood (MML) estimator for the multiple linear regression model with underlying student t distribution. We obtain the closed form of the estimators, derive the asymptotic properties, and demonstrate that the MML estimator is more appropriate for estimating the parameters of the Capital Asset Pricing Model by comparing its performance with least squares estimators (LSE) on the monthly returns of US portfolios. The empirical results reveal that the MML estimators are more efficient than LSE in terms of the relative efficiency of one-step-ahead forecast mean square error in small samples.
    Keywords: Maximum likelihood estimators, Modified maximum likelihood estimators, Student t family, Capital asset pricing model, Robustness.
    JEL: C1 C2 G1
    Date: 2010–10
  9. By: Liu, Philip (International Monetary Fund); Theodoridis, Konstantinos (Bank of England)
    Abstract: The identification of reduced-form VAR model had been the subject of numerous debates in the literature. Different sets of identifying assumptions can lead to very different conclusions in the policy debate. This paper proposes a theoretically consistent identification strategy using restrictions implied by a DSGE model. Monte Carlo simulations suggest the proposed identification strategy is successful in recovering the true structural shocks from the data. In the face of misspecified model restrictions, the data tend to push the identified VAR responses away from the misspecified model and closer to the true data generating process.
    Keywords: VAR identification; model misspecification; DSGE model
    JEL: E52 F41
    Date: 2010–10–28
    Abstract: We show that the asymptotic distribution of the ordinary least squares estimator in a cointegration regression may be bimodal. A simple case arises when the intercept is erroneously omitted from the estimated model or in nonlinear-in-variables models with endogenous regressors. In the latter case, a solution is to use an instrumental variable estimator. The core results in this paper also generalises to more complicated nonlinear models involving integrated time series.
    Keywords: Cointegration, nonlinearity, bimodality, misspecification, instrumental variables, asymptotic theory.
    Date: 2010–10
  11. By: Giorgio Calzolari (University of Florence); Laura Magazzini (Department of Economics (University of Verona))
    Abstract: Negative values for estimated variances can arise in a panel data context. Empirical and theoretical literature dismisses the problem as not serious and a practical solution is to replace negative variances by its boundary value, i.e. zero. While this is not a concern when the individual variance components is "small" with respect to idiosyncratic variance component (making it indistinguishable from zero in practice), we claim that a negative estimated variance can also arise with a "large" individual variance component, when the orthogonality condition between the individual effects and regressors fails. Estimation problems are considered in the (feasible) generalized least squares and maximum likelihood frameworks.
    Keywords: Panel data, random effect estimation, negative variances, maximum likelihood
    JEL: C23
    Date: 2010–10
  12. By: Jia Chen (School of Economics, University of Adelaide); Jiti Gao (School of Economics, University of Adelaide); Degui Li (School of Economics, University of Adelaide)
    Abstract: In this paper, we consider a semiparametric time series regression model and establish a set of identication conditions such that the model under discussion is both identiable and estimable. We then discuss how to estimate a sequence of local alternative functions nonparametrically when the null hypothesis does not hold. An asymptotic theory is established in each case. An empirical application is also included.
    Date: 2010–10
  13. By: Monojit Chatterji; Homagni Choudhury
    Abstract: This study addresses the issue of the presence of a unit root on the growth rate estimation by the least-squares approach. We argue that when the log of a variable contains a unit root, i.e., it is not stationary then the growth rate estimate from the log-linear trend model is not a valid representation of the actual growth of the series. In fact, under such a situation, we show that the growth of the series is the cumulative impact of a stochastic process. As such the growth estimate from such a model is just a spurious representation of the actual growth of the series, which we refer to as a “pseudo growth rate”. Hence such an estimate should be interpreted with caution. On the other hand, we highlight that the statistical representation of a series as containing a unit root is not easy to separate from an alternative description which represents the series as fundamentally deterministic (no unit root) but containing a structural break. In search of a way around this, our study presents a survey of both the theoretical and empirical literature on unit root tests that takes into account possible structural breaks. We show that when a series is trendstationary with breaks, it is possible to use the log-linear trend model to obtain well defined estimates of growth rates for sub-periods which are valid representations of the actual growth of the series. Finally, to highlight the above issues, we carry out an empirical application whereby we estimate meaningful growth rates of real wages per worker for 51 industries from the organised manufacturing sector in India for the period 1973-2003, which are not only unbiased but also asymptotically efficient. We use these growth rate estimates to highlight the evolving inter-industry wage structure in India.
    Keywords: Growth Rate, CAGR, AAGR, Unit Root, Trend Stationary, Structural Breaks, Real Wages, Inter-Industry Wage Structure
    JEL: C12 C13 C22 J31
    Date: 2010–10
  14. By: Jean Jacod; Viktor Todorov
    Abstract: We consider a process $X_t$, which is observed on a finite time interval $[0,T]$, at discrete times $0,\Delta_n,2\Delta_n,\ldots.$ This process is an It\^{o} semimartingale with stochastic volatility $\sigma_t^2$. Assuming that $X$ has jumps on $[0,T]$, we derive tests to decide whether the volatility process has jumps occurring simultaneously with the jumps of $X_t$. There are two different families of tests for the two possible null hypotheses (common jumps or disjoint jumps). They have a prescribed asymptotic level as the mesh $\Delta_n$ goes to $0$. We show on some simulations that these tests perform reasonably well even in the finite sample case, and we also put them in use on S&P 500 index data.
    Date: 2010–10
  15. By: Ardia, David; Ospina, Juan; Giraldo, Giraldo
    Abstract: The estimation of a jump-diffusion model via Differential Evolution is presented. Finding the maximum likelihood estimator for such processes is a tedious task due to the multimodality of the likelihood function. The performance of the Differential Evolution algorithm is compared to standard optimization techniques.
    Keywords: Jump-diffusion; maximum likelihood; optimization; Differential Evolution
    JEL: C13 C22 C61 C1
    Date: 2010–10–16
  16. By: Melina Barrio (Universidade de Santiago de Compostela); Maria Loureiro (Universidade de Santiago de Compostela)
    Abstract: Not much attention has been given to protest responses in choice experiments (CE). Using follow-up statements, we are able to identify protest responses and compute welfare estimates with and without the inclusion of such protest responses. We conclude that protest responses are fairly common in CE, and their analysis affects the statistical performance of the empirical models. In particular, when the sample is corrected by protests, our results come from utility consistent models. Thus, future choice experiments should consider the role of protest responses as contingent valuation studies have done.
    Keywords: Protest Responses, Choice Experiments
    JEL: Q01 Q10 Q50
    Date: 2010–10
  17. By: Markus Eberhardt; Francis Teal
    Abstract: The cross-country growth literature commonly uses aggregate economy datasets such as the Penn World Table (PWT) to estimate homogeneous production function or convergence regression models. Against the background of a dual economy framework this paper investigates the potential bias arising when aggregate economy data instead of sectoral data is adopted in macro production function regressions. Using a unique World Bank dataset we estimate production functions in agriculture and manufacturing for a panel of 41 developing and developed countries (1963-1992). We employ novel empirical methods which can accommodate technology heterogeneity, variable nonstationarity and the breakdown of the standard crosssection independence assumption. We then investigate the potential for biased estimates due to aggregation and empirical misspecification, relying on both theory and Monte Carlo simulations. We confirm substantial bias in the technology coefficients using data for a stylised aggregate economy made up of agricultural and manufacturing sectors and a matched PWT dataset.
    JEL: C23 O47 O11
    Date: 2010
  18. By: Aurea Grané; Rosario Romera
    Abstract: Multidimensional scaling (MDS) techniques are initially proposed to produce pictorial representations of distance, dissimilarity or proximity data. Sensitivity and robustness assessment of multivariate methods is essential if inferences are to be drawn from the analysis. To our knowledge, the literature related to MDS for mixed-type data, including variables measured at continuous level besides categorical ones, is quite scarce. The main motivation of this work was to analyze the stability and robustness of MDS configurations as an extension of a previous study on a real data set, coming from a panel-type analysis designed to assess the economic crisis impact on Spanish people who were in situations of high risk of being socially excluded. The main contributions of the paper on the treatment of MDS configurations for mixed-type data are: (i) to propose a joint metric based on distance matrices computed for continuous, multi-scale categorical and/or binary variables, (ii) to introduce a systematic analysis on the sensitivity of MDS configurations and (iii) to present a systematic search for robustness and identification of outliers through a new procedure based on geometric variability notions.
    Keywords: Gower distance, MDS configurations, Mixed-type data, Outliers identification, Related metric scaling, Survey data
    Date: 2010–09
  19. By: Carneiro, Pedro (University College London); Heckman, James J. (University of Chicago); Vytlacil, Edward (Yale University)
    Abstract: This paper estimates the marginal returns to college for individuals induced to enroll in college by different marginal policy changes. The recent instrumental variables literature seeks to estimate this parameter, but in general it does so only under strong assumptions that are tested and found wanting. We show how to utilize economic theory and local instrumental variables estimators to estimate the effect of marginal policy changes. Our empirical analysis shows that returns are higher for individuals more likely to attend college. We contrast the returns to well-defined marginal policy changes with IV estimates of the return to schooling. Some marginal policy changes inducing students into college produce very low returns.
    Keywords: returns to schooling, marginal return, average return, marginal treatment effect
    JEL: J31
    Date: 2010–10

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