Publications (16)9.03 Total impact
-
Article: First Difference MLE and Dynamic Panel Estimation
[show abstract] [hide abstract]
ABSTRACT: First difference maximum likelihood (FDML) seems an attractive estimation methodology in dynamic panel data modeling because differencing eliminates fixed effects and, in the case of a unit root, differencing transforms the data to stationarity, thereby addressing both incidental parameter problems and the possible effects of nonstationarity. This paper draws attention to certain pathologies that arise in the use of FDML that have gone unnoticed in the literature and that affect both finite sample peformance and asymptotics. FDML uses the Gaussian likelihood function for first differenced data and parameter estimation is based on the whole domain over which the log-likelihood is defined. However, extending the domain of the likelihood beyond the stationary region has certain consequences that have a major effect on finite sample and asymptotic performance. First, the extended likelihood is not the true likelihood even in the Gaussian case and it has a finite upper bound of definition. Second, it is often bimodal, and one of its peaks can be so peculiar that numerical maximization of the extended likelihood frequently fails to locate the global maximum. As a result of these pathologies, the FDML estimator is a restricted estimator, numerical implementation is not straightforward and asymptotics are hard to derive in cases where the peculiarity occurs with non-negligible probabilities. We investigate these problems, provide a convenient new expression for the likelihood and a new algorithm to maximize it. The peculiarities in the likelihood are found to be particularly marked in time series with a unit root. In this case, the asymptotic distribution of the FDMLE has bounded support and its density is infinite at the upper bound when the time series sample size T approaching infinity. As the panel width n approaching infinity the pathology is removed and the limit theory is normal. This result applies even for T fixed and we present an expression for the asymptotic distribution which does not depend on the time dimension. When n,T approaching infinity, the FDMLE has smaller asymptotic variance than that of the bias corrected MLE, an outcome that is explained by the restricted nature of the FDMLE.Yale: Cowles Foundation Working Papers. 01/2011; -
Article: X-Differencing and Dynamic Panel Model Estimation
[show abstract] [hide abstract]
ABSTRACT: This paper introduces a new estimation method for dynamic panel models with fixed effects and AR(p) idiosyncratic errors. The proposed estimator uses a novel form of systematic differencing, called X-differencing, that eliminates fixed effects and retains information and signal strength in cases where there is a root at or near unity. The resulting "panel fully aggregated" estimator (PFAE) is obtained by pooled least squares on the system of X-differenced equations. The method is simple to implement, free from bias for all parameter values, including unit root cases, and has strong asymptotic and finite sample performance characteristics that dominate other procedures, such as bias corrected least squares, GMM and system GMM methods. The asymptotic theory holds as long as the cross section (n) or time series (T) sample size is large, regardless of the n/T ratio, which makes the approach appealing for practical work. In the time series AR(1) case (n = 1), the FAE estimator has a limit distribution with smaller bias and variance than the maximum likelihood estimator (MLE) when the autoregressive coefficient is at or near unity and the same limit distribution as the MLE in the stationary case, so the advantages of the approach continue to hold for fixed and even small n. For panel data modeling purposes, a general-to-specific selection rule is suggested for choosing the lag parameter p and the procedure works in a standard manner, aiding practical implementation. The PFAE estimation method is also applicable to dynamic panel models with exogenous regressors. Some simulation results are reported giving comparisons with other dynamic panel estimation methods.Yale: Cowles Foundation Working Papers. 01/2010; -
Article: NOTES AND PROBLEMS LAD ASYMPTOTICS UNDER CONDITIONAL HETEROSKEDASTICITY WITH POSSIBLY INFINITE
[show abstract] [hide abstract]
ABSTRACT: Least absolute deviations (LAD) estimation of linear time series models is consid-ered under conditional heteroskedasticity and serial correlation. The limit theory of the LAD estimator is obtained without assuming the finite density condition for the errors that is required in standard LAD asymptotics. The results are particularly use-ful in application of LAD estimation to financial time series data.Econometric Theory 01/2010; 26:953-962. · 0.86 Impact Factor -
Article: GAUSSIAN INFERENCE IN AR(1) TIME SERIES WITH OR WITHOUT A UNIT ROOT
[show abstract] [hide abstract]
ABSTRACT: This paper introduces a simple first-difference-based approach to estimation and inference for the AR(1) model. The estimates have virtually no finite-sample bias and are not sensitive to initial conditions, and the approach has the unusual advantage that a Gaussian central limit theory applies and is continuous as the autoregressive coefficient passes through unity with a uniform rate of convergence. En route, a useful central limit theorem (CLT) for sample covariances of linear processes is given, following Phillips and Solo (1992, Annals of Statistics, 20, 971 1001). The approach also has useful extensions to dynamic panels.Econometric Theory 02/2008; 24(03):631-650. · 0.86 Impact Factor -
Article: GMM Estimation for Dynamic Panels with Fixed Effects and Strong Instruments at Unity
[show abstract] [hide abstract]
ABSTRACT: This paper develops new estimation and inference procedures for dynamic panel data models with fixed effects and incidental trends. A simple consistent GMM estimation method is proposed that avoids the weak moment condition problem that is known to affect conventional GMM estimation when the autoregressive coefficient (rho) is near unity. In both panel and time series cases, the estimator has standard Gaussian asymptotics for all values of rho in (-1, 1] irrespective of how the composite cross section and time series sample sizes pass to infinity. Simulations reveal that the estimator has little bias even in very small samples. The approach is applied to panel unit root testing.02/2007; -
Article: GMM with Many Moment Conditions
[show abstract] [hide abstract]
ABSTRACT: This paper provides a first order asymptotic theory for generalized method of moments (GMM) estimators when the number of moment conditions is allowed to increase with the sample size and the moment conditions may be weak. Examples in which these asymptotics are relevant include instrumental variable (IV) estimation with many (possibly weak or uninformed) instruments and some panel data models that cover moderate time spans and have correspondingly large numbers of instruments. Under certain regularity conditions, the GMM estimators are shown to converge in probability but not necessarily to the true parameter, and conditions for consistent GMM estimation are given. A general framework for the GMM limit distribution theory is developed based on epiconvergence methods. Some illustrations are provided, including consistent GMM estimation of a panel model with time varying individual effects, consistent limited information maximum likelihood estimation as a continuously updated GMM estimator, and consistent IV structural estimation using large numbers of weak or irrelevant instruments. Some simulations are reported.Econometrica 12/2005; 74(1):147 - 192. · 2.98 Impact Factor -
Article: GMM with Many Moment Conditions
[show abstract] [hide abstract]
ABSTRACT: This paper provides a first order asymptotic theory for generalized method of moments (GMM) estimators when the number of moment conditions is allowed to increase with the sample size and the moment conditions may be weak. Examples in which these asymptotics are relevant include instrumental variable (IV) estimation with many (possibly weak) instruments and some panel data models covering moderate time spans and with correspondingly large numbers of instruments. Under certain regularity conditions, the GMM estimators are shown to converge in probability but not necessarily to the true parameter. A prominent role in the asymptotic theory is played by two different sources of signal emanating from the moment conditions themselves and from the variability across moment conditions. When the moment conditions are weak, convergence holds because variation across the moment conditions produces a signal that is sufficient in itself to achieve convergence. However, this signal may not be sufficiently informative about the true value of the parameter being estimated, in which case the limit may not correspond to the true parameter. Conditions under which GMM estimators are consistent under such circumstances are given. Some preliminary theory characterizing the limit distribution is provided and a small simulation study is reported09/2004; -
Article: Closest Moment Estimation under General Conditions Chirok Han and Robert de Jong
[show abstract] [hide abstract]
ABSTRACT: This paper considers Closest Moment (CM) estimation with a general distance function, and avoids the assumption of nonsingular quadratic local behavior. The results of Manski (1983), Newey (1988), P otscher and Prucha (1997), and de Jong and Han (2002) are obtained as special cases. Consistency and a root-n rate of convergence are obtained under mild conditions on the distance function and on the moment conditions. Asymptotic normality is obtained as a special case when the distance function displays nonsingular quadratic behavior.03/2002; -
Article: Closest moment estimation under general conditions
[show abstract] [hide abstract]
ABSTRACT: This paper considers Closest Moment (CM) estimation with a general distance func-tion, and avoids the assumption of nonsingular quadratic local behavior. The results of Manski (1983), Newey (1988), Pötscher and Prucha (1997), and de Jong and Han (2002) are obtained as special cases. Consistency and a root-n rate of convergence are obtained under mild conditions on the distance function and on the moment conditions. Asymptotic normality is obtained as a special case when the distance function displays nonsingular quadratic behavior.02/2002; -
Article: THE PROPERTIES OF Lp-GMM ESTIMATORS
[show abstract] [hide abstract]
ABSTRACT: This paper considers generalized method of moment type estimators for which a criterion function is minimized that is not the standard quadratic distance measure but instead is a general Lp distance measure. It is shown that the resulting estimators are root-n consistent but not in general asymptotically normally distributed, and we derive the limit distribution of these estimators. In addition, we prove that it is not possible to obtain estimators that are more efficient than the usual L2-GMM estimators by considering Lp-GMM estimators. We also consider the issue of the choice of the weight matrix for Lp-GMM estimators.Econometric Theory 02/2002; 18(02):491-504. · 0.86 Impact Factor -
Article: The properties of L_p-GMM estimators
[show abstract] [hide abstract]
ABSTRACT: This paper considers Generalized Method of Moment-type estimators for which a criterion function is minimized that is not the "standard" quadratic distance measure, but instead is a general L p distance measure. It is shown that the resulting estimators are root-n consistent, but not in general asymptotically normally distributed, and we derive the limit distribution of these estimators. In addition, we prove that it is not possible to obtain estimators that are more e#cient than the "usual" L 2 -GMM estimators by considering L p -GMM estimators. We also consider the issue of the choice of the weight matrix for L p -GMM estimators. Keywords: L p GMM, Generalized method of moment, L p distance 1 Introduction Since Lars Peter Hansen's (1982) original formulation, Generalized Method of Moment (GMM) estimation has become an extremely important and popular estimation technique in economics. This is due to the fact that economic theory usually implies moment conditions that are exploited in...06/2000; -
Article: Asymptotic distribution of factor augmented estimators for panel regression
[show abstract] [hide abstract]
ABSTRACT: In this paper we derive an asymptotic theory for linear panel regression augmented with estimated common factors. We give conditions under which the estimated factors can be used in place of the latent factors in the regression equation. For the principal components estimate of the factor space it is shown that these conditions are satisfied when T/N→0 and N/T3→0 under regularity. Monte Carlo studies verify the asymptotic theory.Journal of Econometrics. 169(1):48-53. -
Article: LAD ASYMPTOTICS UNDER CONDITIONAL HETEROSKEDASTICITY WITH POSSIBLY INFINITE ERROR DENSITIES
Econometric Theory 26(03):953-962. · 0.86 Impact Factor -
Article: UNIFORM ASYMPTOTIC NORMALITY IN STATIONARY AND UNIT ROOT AUTOREGRESSION
Econometric Theory 27(06):1117-1151. · 0.86 Impact Factor -
Article: Closest Moment Estimationunder General Conditions
[show abstract] [hide abstract]
ABSTRACT: This paper considers Closest Moment (CM) estimation with a general distance function, and avoids the assumption of nonsingular quadratic local behavior. The results of Manski [1983], Newey [1988], Pötscher and Prucha [1997], and DE Jong and Han [2002] are obtained as special cases. Consistency and a root-n rate of convergence are obtained under mild conditions on the distance function and on the moment conditions. We derive the limit distribution of CM estimators in a general setting, and show that the limit distribution is not necessarily normal. Asymptotic normality is obtained as a special case when the distance function displays nonsingular quadratic behavior.Annales d'économie et de statistique -
Article: Infinite Density at the Median and the Typical Shape of Stock Return Distributions
Journal of Business and Economic Statistics 29(2):282-294. · 1.78 Impact Factor
Top co-authors
Top Journals
Institutions
-
2010–2011
-
University of Auckland
- Department of Economics
Auckland, Auckland, New Zealand
-
-
2007–2010
-
Yale University
New Haven, CT, USA
-
-
2005
-
Victoria University of Wellington
- School of Economics and Finance
Wellington, Wellington, New Zealand
-
