# [ECM] Econométrie: working papers (RePEc, 08/11/2010)

Source : NEP (New Economics Papers) | RePEc﻿

• Moment Restriction-based Econometric Methods: An Overview
 Date: 2010-10 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) URL: http://d.repec.org/n?u=RePEc:kyo:wpaper:734&r=ecm 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.
• One-Step Robust Estimation of Fixed-Effects Panel Data Models
 Date: 2010 By: Aquaro, M. Cizek, P. (Tilburg University, Center for Economic Research) URL: http://d.repec.org/n?u=RePEc:dgr:kubcen:2010110&r=ecm 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
• Semiparametric Estimation in Simultaneous Equations of Time Series Models
 Date: 2010-10 By: Jiti Gao (School of Economics, University of Adelaide) Peter C. B. Phillips URL: http://d.repec.org/n?u=RePEc:adl:wpaper:2010-26&r=ecm 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 p roperties 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
• Simultaneous Testing of Mean and Variance Structures in Nonlinear Time Series Models
 Date: 2010-10 By: Song Xi Chen (Guanghua School of Management, Peking University) Jiti Gao (School of Economics, University of Adelaide) URL: http://d.repec.org/n?u=RePEc:adl:wpaper:2010-28&r=ecm 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 distingu ish 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
• IV Estimation of Panels with Factor Residuals
 Date: 2010-10-25 By: Robertson, Donald Sarafidis, Vasilis Symons, James URL: http://d.repec.org/n?u=RePEc:pra:mprapa:26166&r=ecm 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 rob ust 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
• Inference on Time-Invariant Variables using Panel Data: A Pre-Test Estimator with an Application to the Returns to Schooling
 Date: 2010-01-15 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) URL: http://d.repec.org/n?u=RePEc:hal:wpaper:hal-00492039_v1&r=ecm 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
• Anova-type consistent estimators of variance components in unbalanced multi-way error components models
 Date: 2010-10 By: Giovanni S. F. Bruno (Department of Economics, Bocconi University, Milan, Italy) URL: http://d.repec.org/n?u=RePEc:cri:cespri:kites34_wp&r=ecm 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
• Robust Estimation and Forecasting of the Capital Asset Pricing Model
 Date: 2010-10 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) URL: http://d.repec.org/n?u=RePEc:kyo:wpaper:735&r=ecm 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
• DSGE model restrictions for structural VAR identification
 Date: 2010-10-28 By: Liu, Philip (International Monetary Fund) Theodoridis, Konstantinos (Bank of England) URL: http://d.repec.org/n?u=RePEc:boe:boeewp:0402&r=ecm 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
• Nonlinear Cointegration, Misspecification and Bimodality
 Date: 2010-10 By: MArcelo Cunha Medeiros (Department of Economics, PUC-Rio) Eduardo Mendes (DEPARTMENT OF STATISTICS, NORTHWESTERN UNIVERSITY,) Les Oxley (DEPARTMENT OF ECONOMICS, CANTERBURY UNIVERSITY,) URL: http://d.repec.org/n?u=RePEc:rio:texdis:577&r=ecm 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.
• Negative variance estimates in panel data models
 Date: 2010-10 By: Giorgio Calzolari (University of Florence) Laura Magazzini (Department of Economics (University of Verona)) URL: http://d.repec.org/n?u=RePEc:ver:wpaper:15/2010&r=ecm 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
• Estimation in Semiparametric Time Series Regression
 Date: 2010-10 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) URL: http://d.repec.org/n?u=RePEc:adl:wpaper:2010-27&r=ecm 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.
• Growth Rate Estimation in the presence of Unit Roots
 Date: 2010-10 By: Monojit Chatterji Homagni Choudhury URL: http://d.repec.org/n?u=RePEc:dun:dpaper:245&r=ecm 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 containin g 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
 Date: 2010-10 By: Jean Jacod Viktor Todorov URL: http://d.repec.org/n?u=RePEc:arx:papers:1010.4990&r=ecm 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.