Understanding Autocorrelation in Econometrics
Explore the concept of autocorrelation in econometrics, its implications on least squares estimator, and the need for alternative estimators. Learn about Newey-West robust standard errors, residual plots, Lagrange Multiplier test, and the Durbin-Watson test in econometric analysis.
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Presentation Transcript
9.1 Introduction 9.2 Estimating a Model with Autocorrelation 9.3 Testing for Autocorrelation Principles of Econometrics, 3rd Edition Slide 9-2
Figure 9.1 Principles of Econometrics, 3rd Edition Slide 9-3
= ( , f x x , ,...) y x (9.1) 1 2 t t t t = ( , ) x y f y (9.2) 1 t t t = + = ( ) f x ( ) y e e f e (9.3) 1 t t t t t Principles of Econometrics, 3rd Edition Slide 9-4
Figure 9.2(a) Time Series of a Stationary Variable Principles of Econometrics, 3rd Edition Slide 9-5
Figure 9.2(b) Time Series of a Nonstationary Variable that is Slow Turning or Wandering Principles of Econometrics, 3rd Edition Slide 9-6
Figure 9.2(c) Time Series of a Nonstationary Variable that Trends Principles of Econometrics, 3rd Edition Slide 9-7
The existence of autocorrelated errors implies: The least squares estimator is still a linear and unbiased estimator, but it is no longer best. There is another estimator with a smaller variance. The standard errors usually computed for the least squares estimator are incorrect. Confidence intervals and hypothesis tests that use these standard errors may be misleading. Principles of Econometrics, 3rd Edition Slide 9-8
Newey-West Robust Standard Errors. Principles of Econometrics, 3rd Edition Slide 9-9
Residual Plot Lagrange Multiplier Test Durbin-Waston Test Slide 9-10 Principles of Econometrics, 3rd Edition
