2006-4-26 08:48:00

[下载]Testing for Serial Correlation, Spatial Autocorrelation and Random Effects U

Testing for Serial Correlation, Spatial Autocorrelation and Random Effects

Using Panel Data

Badi Baltagi

50052.pdf
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ABSTRACT This paper considers a spatial panel data regression model with serial correlation on each spatial unit over time as well as spatial dependence between the spatial units at each point in time. In addition, the model allows for heterogeneity across the spatial units using random e ? ects. The paper then derives several Lagrange Multiplier tests for this panel data regression model including a joint test for serial correlation, spatial autocorrelation and random e ? ects. These tests draw upon two strands of earlier work. The first is the LM tests for the spatial error correlation model discussed in Anselin and Bera ( 1998) and in the panel data context by Baltagi, Song and Koh ( 2003). The second is the LM tests for the error component panel data model with serial correlation derived by Baltagi and Li ( 1995). Hence the joint LM test derived in this paper encompasses those derived in both strands of earlier works. In fact, in the context of our general model, the earlier LM tests become marginal LM tests that ignore either serial correlation over time or spatial error correlation. The paper then derives conditional LM and LR tests that do not ignore these correlations and contrast them with their marginal LM and LR counterparts. The small sample performance of these tests is investigated using Monte Carlo experiments. As expected, ignoring any correlation when it is significant can lead to misleading inference.

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Trevor

2006-4-26 08:53:00

Panel data models extended to spatial error autocorrelation or

a spatially lagged dependent variable
Elhorst, J. Paul

This paper surveys panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable. In particular, it focuses on the specification and estimation of four panel data models commonly used in applied research: the fixed effects model, the random effects model, the fixed coefficients model and the random coefficients model. This survey should prove useful for researchers in this area.

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2006-4-26 08:58:00

[下载]Stata module to support tests for autocorrelation on panel data

Stata module to support tests for autocorrelation on panel data

Christopher F Baum

These routines are minor modifications of official Stata commands ac, archlm, bgodfrey, durbina, dwstat, pac and wntestq which permit their use on a single time series of a panel dataset (as specified with an if or in qualifier). An if or in qualifier need not be used by durbina2 nor dwstat2. Dialogs are provided for each routine

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Trevor

2006-4-26 09:03:00

[下载]Lecture Notes: Specification And Estimation In Panel Data Models Under Distu

Specification And Estimation In Panel Data Models

Under Disturbance Autocorrelation

50061.pdf
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2006-4-26 09:15:00

[下载]Wendelin Schnedler.Panel Data Models

Wendelin Schnedler: Panel Data Models

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http://web.cenet.org.cn/web/Occidental/

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Trevor

2006-4-26 09:21:00

Panel Data Econometrics

Manuel Arellano

ISBN13: 9780199245291ISBN10: 0199245290 paper, 248 pages

Description

This book, by one of the world's leading experts on dynamic panel data, presents a modern review of some of the main topics in panel data econometrics. The author concentrates on linear models, and emphasizes the roles of heterogeneity and dynamics in panel data modeling. The book combines methods and applications, so will appeal to both the academic and practitioner markets.
The book is divided in four parts. Part I concerns static models, and deals with the problem of unobserved heterogeneity and how the availability of panel data helps to solve it, error component models, and error in variables in panel data.

Part II looks at time series models with error components. Its chapters deal with the problem of distinguishing between unobserved heterogeneity and individual dynamics in short panels, modeling strategies of time effects, moving average models, inference from covariance structures, the specification and estimation of autoregressive models with heterogeneous intercepts, and the impact of assumptions about initial conditions and heteroskedacity on estimation.

Part III examines dynamics and predeterminedness. Its two chapters consider alternative approaches to estimation from small and large T perspectives, looking at models with both strictly exogenous and lagged dependent variables allowing for autocorrelation of unknown form, models in which the errors are mean independent of current and lagged values of certain conditioning variables but not with their future values.
Together Parts II and III provide a synthesis, and unified perspective, of a vast literature that has had a significant impact on recent econometric practice. Part IV reviews the main results in the theory of generalized method of moments estimation and optimal instrumental variables.

Product Details

248 pages; 1 table; ISBN13: 978-0-19-924529-1ISBN10: 0-19-924529-0

About the Author(s)

Manuel Arellano is Professor at CEMFI in Madrid. He was previously a Visiting Professor of Economics at the University of Cambridge, and Editor of The Review of Economic Studies .