Identification Methods for Social Interactions Models with Unknown Networks

Hon Ho Kwok

A chapter in The Econometrics of Networks, 2020, vol. 42, pp 27-59 from Emerald Group Publishing Limited

Abstract: This chapter develops a set of two-step identification methods for social interactions models with unknown networks, and discusses how the proposed methods are connected to the identification methods for models with known networks. The first step uses linear regression to identify the reduced forms. The second step decomposes the reduced forms to identify the primitive parameters. The proposed methods use panel data to identify networks. Two cases are considered: the sample exogenous vectors spanRn (long panels), and the sample exogenous vectors span a proper subspace ofRn(short panels). For the short panel case, in order to solve the sample covariance matrices’ non-invertibility problem, this chapter proposes to represent the sample vectors with respect to a basis of a lower-dimensional space so that we have fewer regression coefficients in the first step. This allows us to identify some reduced form submatrices, which provide equations for identifying the primitive parameters.

Keywords: Adjacency matrix; Change of basis; Jordan canonical form; Matrix triangularization; Network; Spatial weight matrix; C31; C33 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320200000042007

DOI: 10.1108/S0731-905320200000042007

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