The Durbin Watson statistic is a test statistic used in statistics to detect autocorrelation in the residuals from a regression analysis. The Durbin Watson statistic will always assume a value between 0 and 4. A value of DW = 2 indicates that there is no autocorrelation.

Auto correlation is a characteristic of data which shows the degree of similarity between the values of the same variables over successive time intervals. This post explains what autocorrelation is, types of autocorrelation - positive and negative autocorrelation, as well as how to diagnose and test for auto correlation.

DSS Data Consultant . Finding the question is often more important than finding the answer Definition 1: The autocorrelation (aka serial correlation) between the data is cov(e i, e j). We say that the data is autocorrelated (or there exists autocorrelation) if cov(e i, e j) ≠ 0 for some i ≠ j. First-order autocorrelation occurs when consecutive residuals are correlated. Panel data or cross-sectional timeseries are observationson a panel of i units or cases over t time periods. Most panel data commands start with xtFor an overview type helpxt.

6. Random Effects Model: Maximum Likelihood Estimation. Panel Data Structures 7. Extensions of Effects Models; Time Varying Fixed Effects, Heteroscedasticity, Measurement Error, Spatial Autocorrelation 8.

6. Random Effects Model: Maximum Likelihood Estimation. Panel Data Structures 7. Extensions of Effects Models; Time Varying Fixed Effects, Heteroscedasticity, Measurement Error, Spatial Autocorrelation 8. Instrumental Variables; The Hausman-Taylor Estimator, GMM Estimation. 9. GMM Estimation, Dynamic Models, Arellano/Bond/Bover, Schmidt and Ahn 10.

Professor William Greene Department of Economics Office:MEC 7-90, Ph. 998-0876 e-mail:wgreene@stern.nyu.edu URL: http://people.stern.nyu.edu/wgreene. Return to course home page.

Basic Panel Data Commands in STATA . Panel data refers to data that follows a cross section over time—for example, a sample of individuals surveyed repeatedly for a number of years or data for all 50 states for all Census years. • reshape There are many ways to organize panel data.

t(75) Constant 0.571 0.109 5.24 lnav_yrs_sch_1970 0.6925 0.0746 9.28. 1 011. log GDP Panel Data and Autocorrelation and Heteroscedasticity tests Posted 11-20-2012 10:32 PM (1117 views) Hi . How can I test autocorrelation and heteroscedasticity of Auto correlation is a characteristic of data which shows the degree of similarity between the values of the same variables over successive time intervals. This post explains what autocorrelation is, types of autocorrelation - positive and negative autocorrelation, as well as how to diagnose and test for auto correlation.

The approach is used to test first-order serial st: Autocorrelation in Panel Data, xtregar and xtreg. Dear all, I am using Stata 11 to analyze a panel data composed of 279 observations, derived from 31 regions over a 9-year period. In order to check for autocorrelation on several models, I ran the Wooldridge test by inputting the -xtserial- command. 6. Random Effects Model: Maximum Likelihood Estimation. Panel Data Structures 7. Extensions of Effects Models; Time Varying Fixed Effects, Heteroscedasticity, Measurement Error, Spatial Autocorrelation 8.
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Christopher F Baum, 2003. "PANELAUTO: Stata module to support tests for autocorrelation on panel data," Statistical Software Components S435102, Boston College Department of Economics, revised 26 Nov 2003.

This small tutorial contains extracts from the help files/ Stata manual which is available from the web. It is intended to help you at the start.
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Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 1472) = 88.485 Prob > F = 0.0000 4See [XT] xt for more information about this dataset. 5Because the measure of education, highest grade completed, is time-invariant, it cannot be included in the model.

The column to the right shows the last eight of these values, moved “up” one row, with the first value deleted. Autocorrelation refers to the degree of correlation between the values of the same variables across different observations in the data. The concept of autocorrelation is most often discussed in the context of time series data in which observations occur at different points in time (e.g., air temperature measured on different days of the month).