Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models

Victor Chernozhukov; Iván Fernández-Val; Chen Huang; Weining Wang

Home/Archive/arXiv

arXiv · 2402.00584

Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models

Victor Chernozhukov·Iván Fernández-Val·Chen Huang·Weining Wang

View original source ↗Read source document ↗Export metadata ↓

Abstract

The Arellano-Bond estimator is a fundamental method for dynamic panel data models, widely used in practice. It can be severely biased when the time series dimension of the data, $T$, is long. The source of the bias is the large degree of overidentification. We propose a simple two-step approach to deal with this problem. The first step applies LASSO to the cross-section data at each time period to select the most informative moment conditions, exploiting the approximately sparse structure of these conditions. The second step applies a linear instrumental variable estimator using the instruments constructed from the moment conditions selected in the first step. Using asymptotic sequences where the two dimensions of the panel grow with the sample size, we show that the new estimator is consistent and asymptotically normal under much weaker conditions on $T$ than the Arellano-Bond estimator. Our theory covers models with high-dimensional covariates including multiple lags of the dependent variable and strictly exogenous covariates, which are becoming common in modern applications. We illustrate our approach by applying it to weekly county-level panel data from the United States to study opening K-12 schools and other mitigation policies' short and long-term effects on COVID-19's spread.

Explore related subjects

econ.EM

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Victor Chernozhukov, Iván Fernández-Val, Chen Huang, Weining Wang. 2026-07-28. Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models. https://arxiv.org/abs/2402.00584

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Testing for Monotone Equilibrium Strategies in Games of Incomplete Information

This paper develops a unified framework for testing monotonicity of Bayesian Nash equilibrium strategies in unobserved types in games of incomplete information. We show that, under symmetric independent private types, monotonicity of differentiable equilibrium strategies is equivalent to monotonicity of a quasi-inverse strategy identified from observed actions. This allows the problem to be reformulated as testing a countable set of moment inequalities involving unconditional expectations. We propose a Cramer-von Mises-type statistic with bootstrap critical values. The method accommodates covariates and game heterogeneity. Monte Carlo simulations demonstrate finite-sample performance, and an application to procurement auctions illustrates cartel detection.

econ.EM↗

arXiv2026

Ordinal Distributional Change and Conservative Transition Benchmarks: Measurement, Identification, and Inference

Repeated cross-sections reveal changes in ordinal distributions but not the transitions producing them. I axiomatically characterize a threshold-weighted probability metric for ordinal change from threshold-crossing principles. For any threshold-additive ordinal geometry, the discrepancy coincides with the Wasserstein--1 distance induced by that ground metric and measures minimum displacement; its optimizing plans define conservative transition benchmarks. With missing outcomes, I derive sharp identified sets for the discrepancy and endpoint-conditioned benchmark plans. I develop finite-sample-valid projection inference using randomized Monte Carlo calibration and global search with an almost-sure convergence guarantee. Applied to Arab Barometer data, the framework documents a robust shift toward broader and more regular remittance receipt in Lebanon. The discrepancy interval remains well separated from zero after allowing for item nonresponse and sampling uncertainty, while benchmark bounds provide strong numerical evidence that least-displacement restructuring excludes movement toward less frequent receipt and requires reassignment from nonreceipt to recurrent receipt.

econ.EM↗

arXiv2026

A Stochastic Nested Fixed Point Algorithm for Large-Scale BLP Estimation

We develop a stochastic nested fixed point (SNFP) estimator for random coefficients logit demand models that updates model parameters using stochastic gradients and performs demand inversion one market at a time. Relative to the conventional nested fixed point (NFP) estimator, SNFP substantially reduces memory requirements and computational cost, making estimation feasible in very large datasets. We establish the large-$T$ (number of markets) asymptotic properties of the estimator under regularity conditions. We also characterize the effect of sharing one block of simulation draws across markets and show how to correct for it. Monte Carlo simulations show that the SNFP estimator achieves statistical accuracy comparable to the NFP estimator, and in our benchmark a single online pass estimates a model with 100 million markets in about 5.5 hours. An empirical application using scanner data further demonstrates the practical advantages of SNFP for large-scale demand estimation.

econ.EM↗