Estimation and Inference in Spatial Models with Dominant Units

174 Pages Posted: 23 Jan 2020

See all articles by M. Hashem Pesaran

M. Hashem Pesaran

University of Southern California - Department of Economics; University of Cambridge - Trinity College (Cambridge)

Cynthia Fan Yang

Florida State University - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: January 15, 2020

Abstract

In spatial econometrics literature estimation and inference are carried out assuming that the matrix of spatial or network connections has uniformly bounded absolute column sums in the number of units, n, in the network. This paper relaxes this restriction and allows for one or more units to have pervasive effects in the network. The linear-quadratic central limit theorem of Kelejian and Prucha (2001) is generalized to allow for such dominant units, and the asymptotic properties of the GMM estimators are established in this more general setting. A new biascorrected method of moments (BMM) estimator is also proposed that avoids the problem of weak instruments by self-instrumenting the spatially lagged dependent variable. Both cases of homoscedastic and heteroskedastic errors are considered and the associated estimators are shown to be consistent and asymptotically normal, depending on the rate at which the maximum column sum of the weights matrix rises with n. The small sample properties of GMM and BMM estimators are investigated by Monte Carlo experiments and shown to be satisfactory. An empirical application to sectoral price changes in the US over the pre- and post-2008 financial crisis is also provided. It is shown that the share of capital can be estimated reasonably well from the degree of sectoral interdependence using the input-output tables, despite the evidence of dominant sectors being present in the US economy.

Keywords: spatial autoregressive models, central limit theorems for linear-quadratic forms, dominant units, GMM, bias-corrected method of moments (BMM), US input-output analysis, capital share

JEL Classification: C130, C210, C230, R150

Suggested Citation

Pesaran, M. Hashem and Yang, Cynthia Fan, Estimation and Inference in Spatial Models with Dominant Units (January 15, 2020). CESifo Working Paper No. 7563, Available at SSRN: https://ssrn.com/abstract=3362016

M. Hashem Pesaran (Contact Author)

University of Southern California - Department of Economics

3620 South Vermont Ave. Kaprielian (KAP) Hall 300
Los Angeles, CA 90089
United States

University of Cambridge - Trinity College (Cambridge) ( email )

United Kingdom

Cynthia Fan Yang

Florida State University - Department of Economics ( email )

Tallahassee, FL 30306-2180
United States

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