arXiv subjects

Jason R. Blevins

Publications and source records attributed to Jason R. Blevins.

5 recordsLinked to original sources

arXiv2025

Semiparametric Estimation of Fractional Integration: An Evaluation of Local Whittle Methods

Fractionally integrated time series, exhibiting long memory with slowly decaying autocorrelations, are frequently encountered in economics, finance, and related fields. Since the seminal work of Robinson (1995), a variety of semiparametric local Whittle estimators have been proposed for estimating the memory parameter $d$, each with a distinct range of validity and different robustness properties, leaving applied researchers to decide which to use and under what conditions. This paper offers a practitioner's guide to six such estimators. Using a common Monte Carlo design, we map how each estimator behaves under short-run dynamics, unknown means, and time trends -- the conditions under which each remains reliable and the characteristic way each breaks down. This reveals a tension between efficiency and robustness: the exact local Whittle estimator uniquely pairs the lowest asymptotic variance with an unrestricted parameter range, but requires the mean and trend to be handled with care. We then illustrate these failure modes, along with the difficulties introduced by structural breaks, on several macroeconomic, financial, and climate time series, where a na\"{i}vely applied estimator can report near-stationarity for a series that better-matched methods identify as strongly nonstationary. The resulting guidance on estimator choice and bandwidth selection is anchored by exact reproductions of published results from this literature, along with open source replication code and datasets.

econ.EM↗

arXiv2025

Identification and Estimation of Continuous-Time Dynamic Discrete Choice Games

This paper considers the theoretical, computational, and econometric properties of continuous time dynamic discrete choice games with stochastically sequential moves, introduced by Arcidiacono, Bayer, Blevins, and Ellickson (2016). We consider identification of the rate of move arrivals, which was assumed to be known in previous work, as well as a generalized version with heterogeneous move arrival rates. We re-establish conditions for existence of a Markov perfect equilibrium in the generalized model and consider identification of the model primitives with only discrete time data sampled at fixed intervals. Three foundational example models are considered: a single agent renewal model, a dynamic entry and exit model, and a quality ladder model. Through these examples we examine the computational and statistical properties of estimators via Monte Carlo experiments and an empirical example using data from Rust (1987). The experiments show how parameter estimates behave when moving from continuous time data to discrete time data of decreasing frequency and the computational feasibility as the number of firms grows. The empirical example highlights the impact of allowing decision rates to vary.

econ.EM↗

arXiv2024

Leveraging Uniformization and Sparsity for Estimation and Computation of Continuous Time Dynamic Discrete Choice Games

Continuous-time empirical dynamic discrete choice games offer notable computational advantages over discrete-time models. This paper addresses remaining computational and econometric challenges to further improve both model solution and estimation. We establish convergence rates for value iteration and policy evaluation with fixed beliefs, and develop Newton-Kantorovich methods that exploit analytical Jacobians and sparse matrix structure. We apply uniformization both to derive a new representation of the value function that draws direct analogies to discrete-time models and to enable stable computation of the matrix exponential and its parameter derivatives for estimation with discrete-time snapshot data, a common but challenging data scenario. These methods provide a complete chain of analytical derivatives from the value function for a given equilibrium through the log-likelihood function, eliminating the need for numerical differentiation and improving finite-sample estimation accuracy and computational efficiency. Monte Carlo experiments demonstrate substantial gains in both statistical performance and computational efficiency, enabling researchers to estimate richer models of strategic interaction. While we focus on games, our methods extend to single-agent dynamic discrete choice and continuous-time Markov jump processes.

econ.EM↗

arXiv2021

Nested Pseudo Likelihood Estimation of Continuous-Time Dynamic Discrete Games

We introduce a sequential estimator for continuous time dynamic discrete choice models (single-agent models and games) by adapting the nested pseudo likelihood (NPL) estimator of Aguirregabiria and Mira (2002, 2007), developed for discrete time models with discrete time data, to the continuous time case with data sampled either discretely (i.e., uniformly-spaced snapshot data) or continuously. We establish conditions for consistency and asymptotic normality of the estimator, a local convergence condition, and, for single agent models, a zero Jacobian property assuring local convergence. We carry out a series of Monte Carlo experiments using an entry-exit game with five heterogeneous firms to confirm the large-sample properties and demonstrate finite-sample bias reduction via iteration. In our simulations we show that the convergence issues documented for the NPL estimator in discrete time models are less likely to affect comparable continuous-time models. We also show that there can be large bias in economically-relevant parameters, such as the competitive effect and entry cost, from estimating a misspecified discrete time model when in fact the data generating process is a continuous time model.

econ.EM↗

arXiv2019

Efficient and Convergent Sequential Pseudo-Likelihood Estimation of Dynamic Discrete Games

We propose a new sequential Efficient Pseudo-Likelihood (k-EPL) estimator for dynamic discrete choice games of incomplete information. k-EPL considers the joint behavior of multiple players simultaneously, as opposed to individual responses to other agents' equilibrium play. This, in addition to reframing the problem from conditional choice probability (CCP) space to value function space, yields a computationally tractable, stable, and efficient estimator. We show that each iteration in the k-EPL sequence is consistent and asymptotically efficient, so the first-order asymptotic properties do not vary across iterations. Furthermore, we show the sequence achieves higher-order equivalence to the finite-sample maximum likelihood estimator with iteration and that the sequence of estimators converges almost surely to the maximum likelihood estimator at a nearly-superlinear rate when the data are generated by any regular Markov perfect equilibrium, including equilibria that lead to inconsistency of other sequential estimators. When utility is linear in parameters, k-EPL iterations are computationally simple, only requiring that the researcher solve linear systems of equations to generate pseudo-regressors which are used in a static logit/probit regression. Monte Carlo simulations demonstrate the theoretical results and show k-EPL's good performance in finite samples in both small- and large-scale games, even when the game admits spurious equilibria in addition to one that generated the data. We apply the estimator to study the role of competition in the U.S. wholesale club industry.

econ.EM↗