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Found 5 results for '"asymptotic expansions" "bias approximation" "dynamic panel." ', showing 1-5
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1. Maurice J.G. Bun & Jan F. Kiviet (2002): The Effects of Dynamic Feedbacks on LS and MM Estimator Accuracy in Panel Data Models

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   The finite sample behaviour is analysed of particular least squares (LS) andmethod of moments (MM) estimators in panel data models with individual effectsand both a lagged dependent variabIe regressor and another explanatory variabIewhich may be affected by lagged feedbacks from the dependent variabIe. Asymp-totic expansions indicate that the order of magnitude of bias of (generalized) MMestimators tends to increase with the number of moment conditions exploited. ... Simulation re-sults corroborate our theoretical findings and show that in small samples of modelswith dynamic feedbacks none of the techniques examined dominates. However, asimple bias corrected LS estimator which presupposes strict exogeneity is found tobe rather robliSt, showing often smaller root mean squared errors than GMM.
   RePEc:tin:wpaper:20020101  Save to MyIDEAS
2. Maurice J.G. Bun & Jan F. Kiviet (2002): On the Diminishing Returns of Higher-order Terms in Asymptotic Expansions of Bias

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   The relative magnitudes are compared of successive terms in a higher-order asymptotic expansion of the bias of the LSDV estimator in dynamic panels. We find that the leading term accounts for the major part of the actual bias in small samples. This implies that bias correction procedures can be based on relatively simple bias approximation formulas.
   RePEc:tin:wpaper:20020099  Save to MyIDEAS
3. Bun, Maurice J. G. & Kiviet, Jan F. (2003): On the diminishing returns of higher-order terms in asymptotic expansions of bias

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   The relative magnitudes are compared of successive terms in a higher-order asymptotic expansion of the bias of the LSDV estimator in dynamic panels. We find that the leading term accounts for the major part of the actual bias in small samples. This implies that bias correction procedures can be based on relatively simple bias approximation formulas.
   RePEc:eee:ecolet:v:79:y:2003:i:2:p:145-152  Save to MyIDEAS
4. Maurice J. G. Bun (2000): Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix

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   By using asymptotic expansion techniques approximation formulae are developed for the bias of ordinary and generalized Least Squares Dummy Variable (LSDV) estimators in dynamic panel data models. Earlier results on bias approximation in first-order stable dynamic panel data models are extended to higher-order dynamic models with general disturbance covariance structure. ... The bias approximations are used to construct bias corrected estimators which are then applied to quarterly data from 14 European Union countries. ... The empirical results show that in general plausible long-run effects are obtained by the bias corrected estimators. Moreover, bias correction can be substantial underlining the importance of more refined estimation techniques.
   RePEc:ecm:wc2000:0511  Save to MyIDEAS
5. Giovanni S.F. Bruno (2004): Approximating the bias of the LSDV estimator for dynamic panel data models

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   It is well known that the LSDV estimator for dynamic panel data models is not consistent for N large and finite T. ... Kiviet (1995) uses asymptotic expansion techniques to approximate the small sample bias of the LSDV estimator to also include terms of at most order 1/NT, thus offering a method to correct the LSDV estimator for samples where N is small or only moderately large. ... Bruno (2004) extends the bias approximation formulas in Bun and Kiviet (2003) to accommodate unbalanced panels with a strictly exogenous selection rule. This paper describes the Stata codes used in Bruno (2004) to compute the bias approximations and carry out the Monte Carlo experiment estimating the actual LSDV bias for various data generating processes. ... Moreover, the approximations are always accurate with a decreasing contribution to the actual bias of the higher order terms.
   RePEc:boc:usug04:2  Save to MyIDEAS