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Direct Standard Errors for Regressions with Spatially Autocorrelated Residuals
Author(s)
Date Issued
2020-03
Date Available
2020-07-23T15:32:59Z
Abstract
Regressions using data with known locations are increasingly used in empirical economics, and several standard error corrections are available to deal with the fact that their residuals tend to be spatially correlated. Unfortunately, different corrections commonly return significance levels that vary by several orders of magnitude, leaving the researcher uncertain as to which, if any, is valid. This paper proposes instead an extremely fast and simple procedure to derive standard errors directly from the spatial correlation structure of regression residuals. Importantly, because the estimated covariance matrix gives optimal weights to predict each residual as a linear combination of all residuals, the reliability of these standard errors is self-checking by construction. The approach extends immediately to instrumental variables, and balanced and unbalanced panels, as well as a wide class of nonlinear models. A step by step guide to estimating these standard errors is given in the accompanying tutorials.
Type of Material
Working Paper
Publisher
University College Dublin. School of Economics
Start Page
1
End Page
24
Series
UCD Centre for Economic Research Working Paper Series
WP2020/06
Copyright (Published Version)
2020 the Author
Classification
C21
C23
Language
English
Status of Item
Not peer reviewed
This item is made available under a Creative Commons License
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