Downloads

cwp511616.pdf
PDF | 396.19 KB
Recent developments in nonlinear panel data analysis allow identifying and estimating general dynamic systems. In this review we describe some results and techniques for nonparametric identification and flexible estimation in the presence of time-invariant and time-varying latent variables. This opens the possibility to estimate nonlinear reduced forms in a large class of structural dynamic models with heterogeneous agents. We show how such reduced forms may be used to document policy-relevant derivative effects, and to improve the understanding and facilitate the implementation of structural models.
Authors

Research Fellow Centre for Monetary and Financial Studies (CEMFI)
Manuel is a Research Fellow of the IFS and a Professor of Econometrics at CEMFI, Madrid.

Professor of Economics University of Chicago
Working Paper details
- DOI
- 10.1920/wp.cem.2016.5116
- Publisher
- The IFS
Suggested citation
Arellano, M and Bonhomme, S. (2016). Nonlinear panel data methods for dynamic heterogeneous agent models. London: The IFS. Available at: https://ifs.org.uk/publications/nonlinear-panel-data-methods-dynamic-heterogeneous-agent-models (accessed: 10 February 2025).
More from IFS
Understand this issue

Gender norms, violence and adolescent girls’ trajectories: Evidence from India
24 October 2022

Do tariffs work?
We discuss the economic consequences of tariffs, why governments use them, and whether they actually achieve their intended goals.
23 January 2025

What is this government’s ‘theory of growth’? Nobody knows
"Shifting the performance of an entire economy requires a long-term, consistent and persistent direction." Paul Johnson writes for the Times.
20 January 2025
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

Living standards, poverty and inequality in the UK: 2024
25 July 2024
Academic research

Prediction sets and conformal inference with censored outcomes
This paper provides estimation methods of such prediction sets given observed conditioning covariates when 𝑌 is censored or measured in intervals.
21 January 2025

Individual welfare analysis: Random quasilinear utility, independence and confidence bounds
We introduce a novel framework for individual-level welfare analysis.
13 December 2024

Inference for parameters identified by conditional moment restrictions using a generalized Bierens maximum statistic
Building on Bierens (1990), we propose penalized maximum statistics and combine bootstrap inference with model selection.
13 December 2024