Downloads
CWP381818.pdf
PDF | 541.63 KB
Factor structures or interactive effects are convenient devices to incorporate latent variables in panel data models. We consider fixed effect estimation of nonlinear panel single-index models with factor structures in the unobservables, which include logit, probit, ordered probit and Poisson specifications. We establish that fixed effect estimators of model parameters and average partial effects have normal distributions when the two dimensions of the panel grow large, but might suffer of incidental parameter bias. We show how models with factor structures can also be applied to capture important features of network data such as reciprocity, degree heterogeneity, homophily in latent variables and clustering. We illustrate this applicability with an empirical example to the estimation of a gravity equation of international trade between countries using a Poisson model with multiple factors.
Authors
Research Associate University College London and University of Oxford
Martin is an IFS Research Associate, a Fellow of the Nuffield College and a Professor in the Department of Economics at the University of Oxford.
Ivan Fernandez-Val
Mingli Chen
Working Paper details
- DOI
- 10.1920/wp.cem.2018.3818
- Publisher
- The IFS
Suggested citation
M, Chen and I, Fernandez-Val and M, Weidner. (2018). Nonlinear factor models for network and panel data. London: The IFS. Available at: https://ifs.org.uk/publications/nonlinear-factor-models-network-and-panel-data-0 (accessed: 29 March 2024).
More from IFS
Understand this issue
Gender norms, violence and adolescent girls’ trajectories: Evidence from India
24 October 2022
A mess has been made of Child Benefit, and the clear-up operation may not be easy
29 March 2024
Spring Budget 2024: What you need to know
7 March 2024
Policy analysis
IFS Deputy Director Carl Emmerson appointed to the UK Statistics Authority Methodological Assurance Review Panel
14 April 2023
ABC of SV: Limited Information Likelihood Inference in Stochastic Volatility Jump-Diffusion Models
We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Compu- tation which build likelihoods based on limited information.
12 August 2014
Is there really an NHS productivity crisis?
17 November 2023
Academic research
Sample composition and representativeness on Understanding Society
2 February 2024
Understanding Society: minimising selection biases in data collection using mobile apps
2 February 2024
Robust analysis of short panels
8 January 2024