Panel data with high-dimensional factors: inference on treatment effects with an application to sampled networks

Wang, Jasmine ORCID: 0009-0004-7201-2702 (2018) Panel data with high-dimensional factors: inference on treatment effects with an application to sampled networks. . University of Chicago, Chicago, US.

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Abstract

Factor models are widely used in economics to capture unobserved aggregate shocks and individual reactions to the shocks. While the existing literature focuses on models with a small and fixed number of factors, we develop a new method in this study to allow for a large and growing number of factors under a sparsity assumption on the factor loadings. We call the new approach the High-Dimensional Interactive Fixed Effects (HD-IFE) estimator. We provide conditions under which the new estimator is consistent and asymptotically normal. We apply the HD-IFE estimator to the estimation of peer-effects models when the researcher only observes a sample of individuals and the connections among them. In this setting, missing nodes and connections create an endogeneity problem for standard regression analysis, whereas the new estimator provides consistent peer-effects estimates. The sparsity condition of the new estimator assumes that each individual is only affected by a small subset of factors. This is a plausible condition in our empirical application when network connections are sparse, as we observe in a wide range of real-world networks. Monte Carlo simulations demonstrate that when the data generating process contains a large number of factors, the HD-IFE estimator recovers the treatment-effects coefficients and latent factors well, whereas the existing low-dimensional methods in the literature underperform. Empirically, we apply the peereffects model to examine the existence of tacit collusion on price in the Houston gasoline retail market, for which we obtain different findings by using the new estimator and the low-dimensional ones.

Item Type:	Monograph (Working Paper)
Additional Information:	© 2018 The Author
Divisions:	Economics
Subjects:	H Social Sciences > HB Economic Theory
JEL classification:	C - Mathematical and Quantitative Methods > C3 - Econometric Methods: Multiple; Simultaneous Equation Models; Multiple Variables; Endogenous Regressors > C33 - Models with Panel Data
Date Deposited:	29 Nov 2019 12:42
Last Modified:	11 Dec 2024 19:32
URI:	http://eprints.lse.ac.uk/id/eprint/102706

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