Research

Google Scholar • arXiv • ORCiD • NBER • SSRN • RePEc/IDEAS • ResearchGate

Working Papers

• Leveraging Uniformization and Sparsity for Estimation and Computation of Continuous-Time Dynamic Discrete Choice Games. Updated November 7, 2025. Under review at Journal of Econometrics.
• PyELW: Exact Local Whittle Estimation for Long Memory Time Series in Python. Updated December 6, 2025. Reject and resubmit at Journal of Statistical Software.
• Semiparametric Estimation of Fractional Integration: An Evaluation of Local Whittle Methods. Updated December 16, 2025. Under review at Journal of Applied Econometrics.
• Nested Pseudo-Likelihood Estimation of Dynamic Discrete Choice Games: A Replication and Retrospective, with Minhae Kim. Updated November 12, 2019.
• Partial Identification and Inference in Binary Choice and Duration Panel Data Models. Updated March 28, 2011.

Work in Progress

• Identification and Estimation of Spatial Weights in Matrix Exponential Panel Data Models.
• Multiplicity and Uniqueness of Equilibria in Continuous-Time Dynamic Discrete Choice Games, with Youngjae Jeong.
• Exact Local Whittle Estimation with Unknown Deterministic Components.

Publications

• Identification and Estimation of Continuous-Time Dynamic Discrete Choice Games. Forthcoming at Quantitative Economics.
• Dearing, A. and J.R. Blevins (2025). Efficient and Convergent Sequential Pseudo-Likelihood Estimation of Dynamic Discrete Games. Review of Economic Studies 92, 981–1021.
• Blevins, J. R. and Minhae Kim (2024). Nested Pseudo Likelihood Estimation of Continuous-Time Dynamic Discrete Games. Journal of Econometrics 238, 105576.
• Blevins, J. R., W. Shi., D. R. Haurin, and S. Moulton (2020). A Dynamic Discrete Choice Model of Reverse Mortgage Borrower Behavior. International Economic Review 61, 1437–1477.
• Blevins, J. R. and G. T. Senney (2019). Dynamic Selection and Distributional Bounds on Search Costs in Dynamic Unit-Demand Models. Quantitative Economics 10, 891–929.
• Blevins, J. R., A. Khwaja, and N. Yang (2018). Firm Expansion, Size Spillovers and Market Dominance in Retail Chain Dynamics. Management Science 64, 3971–4470.
• Blevins, J. R. (2017). Identifying Restrictions for Finite Parameter Continuous Time Models with Discrete Time Data. Econometric Theory 33, 739–754.
• Blevins, J. R. (2016). Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models. Journal of Applied Econometrics 31, 773–804.
• Arcidiacono, P., P. Bayer, J. R. Blevins, and P. B. Ellickson (2016). Estimation of Dynamic Discrete Choice Models in Continuous Time with an Application to Retail Competition. Review of Economic Studies 83, 889–931.
• Blevins, J. R. (2015). Non-Standard Rates of Convergence of Criterion-Function-Based Set Estimators for Binary Response Models. Econometrics Journal 18, 172–199.
• Blevins, J. R. (2015). Structural Estimation of Sequential Games of Complete Information. Economic Inquiry 53, 791–811.
• Blevins, J. R. (2014). Nonparametric Identification of Dynamic Decision Processes with Discrete and Continuous Choices. Quantitative Economics 5, 531–554.
• Blevins, J. R. and S. Khan (2013). Distribution-Free Estimation of Heteroskedastic Binary Response Models in Stata. Stata Journal 13, 588–602.
• Blevins, J. R. and S. Khan (2013). Local NLLS Estimation of Semiparametric Binary Choice Models. Econometrics Journal 16, 135–160.
• Blevins, J. R. (2009). A Generic Linked List Implementation in Fortran 95. ACM Fortran Forum 28(3), 2–7.

Scientific Software

• PyELW, A Python package for exact local Whittle estimation for long memory time series.
• Annotated MATLAB Translation of the Aguirregabiria and Mira (2007) Replication Code, with Minhae Kim (2019).
• DFBR, a Stata program for distribution-free estimation of heteroskedastic binary response models, with Shakeeb Khan (2012).
• PDESolutionTester, a Mathematica program for the symbolic verification of exact solutions of nonlinear partial differential equations, with Jeff Heath and Willy Hereman (2002).

Unpublished Manuscripts

• Updating the Centroid Decomposition with Applications in LSI, with Moody Chu, 2004.
• The Effects of Ties on Convergence in K-Modes Variants for Clustering Categorical Data, with Nick Orlowski, David Schlorff, Daniel Cañas, Moody Chu, and Robert Funderlic, 2004.