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Hashem M Pesaran - One of the best experts on this subject based on the ideXlab platform.
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robust standard errors in transformed likelihood estimation of dynamic panel data models with cross sectional heteroskedasticity
Social Science Research Network, 2014Co-Authors: Kazuhiko Hayakawa, Hashem M PesaranAbstract:This paper extends the transformed maximum likelihood approach for estimation of dynamic panel data models by Hsiao, Pesaran and Tahmiscioglu (2002) to the case where the errors are cross-sectionally heteroskedastic. This extension is not trivial due to the incidental parameters problem that arises, and its implications for estimation and inference. We approach the problem by working with a mis-specified homoskedastic model. It is shown that the transformed maximum likelihood estimator continues to be consistent even in the presence of cross-sectional heteroskedasticity. We also obtain standard errors that are robust to cross-sectional heteroskedasticity of unknown form. By means of Monte Carlo simulation, we investigate the finite sample behavior of the transformed maximum likelihood estimator and compare it with various GMM estimators proposed in the literature. Simulation results reveal that, in terms of median absolute errors and accuracy of inference, the transformed likelihood estimator outperforms the GMM estimators in almost all cases.
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estimation and inference in short panel vector autoregressions with unit roots and cointegration
Econometric Theory, 2005Co-Authors: Michael Binder, Cheng Hsiao, Hashem M PesaranAbstract:This paper considers estimation and inference in panel vector autoregressions (PVARs) with fixed effects when the time dimension of the panel is finite, and the cross-sectional dimension is large. A Maximum Likelihood (ML) estimator based on a transformed likelihood function is proposed and shown to be consistent and asymptotically normally distributed irrespective of the unit root and cointegrating properties of the underlying PVAR model. The transformed likelihood framework is also used to derive unit root and cointegration tests in panels with short time dimension; these tests have the attractive feature that they are based on standard chi-square and normal distributed statistics. Examining Generalized Method of Moments (GMM) estimation as an alternative to our proposed ML estimator, it is shown that conventional GMM estimators based on standard orthogonality conditons break down if the underlying time series contain unit roots. Also, the implementation of extended GMM estimators making use of variants of homoskedasticity and stationarity restrictions as suggested in the literature in a univariate context is subject to difficulties. Monte Carlo evidence is adduced suggesting that the ML estimator and parameter hypothesis and cointegration tests based on it perform well in small sample; this is in marked contrast to the small sample performance of the GMM estimators.
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maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods
Journal of Econometrics, 2002Co-Authors: Cheng Hsiao, Hashem M Pesaran, Kamil A TahmisciogluAbstract:Abstract A transformed likelihood approach is suggested to estimate fixed effects dynamic panel data models. Conditions on the data generating process of the exogenous variables are given to get around the issue of “incidental parameters”. The maximum likelihood (MLE) and minimum distance estimator (MDE) are suggested. Both estimators are shown to be consistent and asymptotically normally distributed. A Hausman-type specification test is suggested to test the fixed versus random effects specification or conditions on the data generating process of the exogenous variables. Monte Carlo studies are conducted to evaluate the finite sample properties of the MLE, MDE, instrumental variable estimator (IV) and linear generalized method of moments estimator (GMM). It is shown that the likelihood approach appears to dominate the GMM approach both in terms of the bias and root mean square error of the estimators and the size and power of the test statistics.
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estimation and inference in short panel vector autoregressions with unit roots and cointegration
Computing in Economics and Finance, 2001Co-Authors: Michael Binder, Cheng Hsiao, Hashem M PesaranAbstract:This paper considers estimation and inference in panel vector autoregressions (PVARs) with fixed effects when the time dimension is finite and the cross-sectional dimension is large. A Maximum Likelihood (ML) estimator based on a transformed likelihood function is proposed and shown to be consistent and asymptotically normal irrespective of the unit-root and cointegrating properties of the underlying PVAR model. This transformed framework is also used to derive unit-root and cointegration tests, based on standard chi-squared and normally distributed statistics, in panels with a short time dimension. It is shown that the standard Generalized Method of Moments (GMM) estimator, examined as an alternative to the proposed ML estimator, breaks down if the underlying time series contain unit roots. Also, the extended GMM estimator, using variants of homoskedasticity and stationarity restrictions as suggested in a univariate context, is subject to difficulties. Monte Carlo evidence suggests that the ML estimator and the tests of hypotheses and cointegration that are based on it perform well in small samples in marked contrast to the performance of GMM estimators.
Manuel Arellano - One of the best experts on this subject based on the ideXlab platform.
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the time series and cross section asymptotics of dynamic panel data estimators
Econometrica, 2003Co-Authors: Javier Alvarez, Manuel ArellanoAbstract:In this paper we derive the asymptotic properties of within groups (WG), GMM and LIML estimators for an autoregressive model with random effects when both T and N tend to infinity. GMM and LIML are consistent and asymptotically equivalent to the WG estimator. When T/N->0 the fixed T results for GMM and LIML remain valid, but WG although consistent has an asymptotic bias in its asymptotic distribution. When T/N tends to a positive constant, the WG, GMM and LIML estimators exhibit negative asymptotic biases of order T,N and (2N-T), respectively. In addition, the crude GMM estimator that neglects the autocorrelation in first differenced errors is inconsistent as T/N->c>0, despite being consistent for fixed T. Finally, we discuss the properties of a random effects MLE with unrestricted initial conditions when both T and N tend to infinity.
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the time series and cross section asymptotics of dynamic panel data estimators
Econometrica, 2003Co-Authors: Javier Alvarez, Manuel ArellanoAbstract:In this paper we derive the asymptotic properties of within groups (WG), GMM, and LIML estimators for an autoregressive model with random effects when both T and N tend to infinity. GMM and LIML are consistent and asymptotically equivalent to the WG estimator. When T/N→ 0 the fixed T results for GMM and LIML remain valid, but WG, although consistent, has an asymptotic bias in its asymptotic distribution. When T/N tends to a positive constant, the WG, GMM, and LIML estimators exhibit negative asymptotic biases of order 1/T, 1/N, and 1/(2N−T), respectively. In addition, the crude GMM estimator that neglects the autocorrelation in first differenced errors is inconsistent as T/N→c>0, despite being consistent for fixed T. Finally, we discuss the properties of a random effects pseudo MLE with unrestricted initial conditions when both T and N tend to infinity.
D J Atkinson - One of the best experts on this subject based on the ideXlab platform.
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model predictive mras estimator for sensorless induction motor drives
IEEE Transactions on Industrial Electronics, 2016Co-Authors: Yaman B Zbede, Shady Gadoue, D J AtkinsonAbstract:This paper presents a novel predictive model reference adaptive system (MRAS) speed estimator for sensorless induction motor (IM) drives applications. The proposed estimator is based on the finite control set-model predictive control (FCS-MPC) principle. The rotor position is calculated using a search-based optimization algorithm which ensures a minimum speed tuning error signal at each sampling period. This eliminates the need for a proportional–integral (PI) controller which is conventionally employed in the adaption mechanism of MRAS estimators. Extensive experimental tests have been carried out to evaluate the performance of the proposed estimator using a 2.2-kW IM with a field-oriented control (FOC) scheme employed as the motor control strategy. Experimental results show improved performance of the MRAS scheme in both open- and closed-loop sensorless modes of operation at low speeds and with different loading conditions including regeneration. The proposed scheme also improves the system robustness against motor parameter variations and increases the maximum bandwidth of the speed loop controller.
Sahadeb Sarkar - One of the best experts on this subject based on the ideXlab platform.
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testing for a unit root in an ar 1 time series using irregularly observed data
Journal of Time Series Analysis, 1996Co-Authors: Dong Wan Shin, Sahadeb SarkarAbstract:. For an AR(1) model having a unit root with nonconsecutively observed or missing data we consider the ordinary least squares estimator, the one-step Newton-Raphson estimator and an ordinary least squares type estimator which is a simple approximation of the Newton-Raphson estimator. It is shown that the limiting distributions of these estimators of the unit root are the same as those of the regression estimators as tabulated by Dickey and Fuller (Distribution of the estimators for autoregressive time series with a unit root. J. Am. Statist. Assoc. 74 (1979), 427–31) for the complete data situation. Simulation results show that our proposed unit root tests perform very well for small samples.
Javier Alvarez - One of the best experts on this subject based on the ideXlab platform.
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the time series and cross section asymptotics of dynamic panel data estimators
Econometrica, 2003Co-Authors: Javier Alvarez, Manuel ArellanoAbstract:In this paper we derive the asymptotic properties of within groups (WG), GMM and LIML estimators for an autoregressive model with random effects when both T and N tend to infinity. GMM and LIML are consistent and asymptotically equivalent to the WG estimator. When T/N->0 the fixed T results for GMM and LIML remain valid, but WG although consistent has an asymptotic bias in its asymptotic distribution. When T/N tends to a positive constant, the WG, GMM and LIML estimators exhibit negative asymptotic biases of order T,N and (2N-T), respectively. In addition, the crude GMM estimator that neglects the autocorrelation in first differenced errors is inconsistent as T/N->c>0, despite being consistent for fixed T. Finally, we discuss the properties of a random effects MLE with unrestricted initial conditions when both T and N tend to infinity.
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the time series and cross section asymptotics of dynamic panel data estimators
Econometrica, 2003Co-Authors: Javier Alvarez, Manuel ArellanoAbstract:In this paper we derive the asymptotic properties of within groups (WG), GMM, and LIML estimators for an autoregressive model with random effects when both T and N tend to infinity. GMM and LIML are consistent and asymptotically equivalent to the WG estimator. When T/N→ 0 the fixed T results for GMM and LIML remain valid, but WG, although consistent, has an asymptotic bias in its asymptotic distribution. When T/N tends to a positive constant, the WG, GMM, and LIML estimators exhibit negative asymptotic biases of order 1/T, 1/N, and 1/(2N−T), respectively. In addition, the crude GMM estimator that neglects the autocorrelation in first differenced errors is inconsistent as T/N→c>0, despite being consistent for fixed T. Finally, we discuss the properties of a random effects pseudo MLE with unrestricted initial conditions when both T and N tend to infinity.