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Asymptotic theory for econometricians

Halbert White
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TLDR
The Linear Model and Instrumental Variables Estimators as mentioned in this paper have been used to estimate Asymptotic Covariance Matrices, and Central Limit Theory has been applied to this problem.
Abstract: 
The Linear Model and Instrumental Variables Estimators. Consistency. Laws of Large Numbers. Asymptotic Normality. Central Limit Theory. Estimating Asymptotic Covariance Matrices. Functional Central Limit Theory and Applications. Directions for Further Study. Solution Set. References. Index.

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Econometric Analysis of Cross Section and Panel Data

Jeffrey M. Wooldridge
TL;DR: This is the essential companion to Jeffrey Wooldridge's widely-used graduate text Econometric Analysis of Cross Section and Panel Data (MIT Press, 2001).
ReportDOI

A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix

Whitney K. Newey, +1 more
- 01 May 1987 - 
TL;DR: In this article, a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction is described.
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Econometric Analysis of Panel Data

Badi H. Baltagi
TL;DR: In this article, the authors proposed a two-way error component regression model for estimating the likelihood of a particular item in a set of data points in a single-dimensional graph.
Journal ArticleDOI

How Much Should We Trust Differences-In-Differences Estimates?

Marianne Bertrand, +2 more
- 01 Feb 2004 - 
TL;DR: In this article, the authors randomly generate placebo laws in state-level data on female wages from the Current Population Survey and use OLS to compute the DD estimate of its "effect" as well as the standard error of this estimate.

Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models / Søren Johansen

S Johansen
TL;DR: In this paper, the authors present the likelihood methods for the analysis of cointegration in VAR models with Gaussian errors, seasonal dummies, and constant terms, and show that the asymptotic distribution of the maximum likelihood estimator is mixed Gausssian.