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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 reestablish 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.

This article develops a dynamic model of retail competition and uses it to study the impact of the expansion of a new national competitor on the structure of urban markets. In order to accommodate substantial heterogeneity (both observed and unobserved) across agents and markets, the article first develops a general framework for estimating and solving dynamic discrete choice models in continuous time that is computationally light and readily applicable to dynamic games. In the proposed framework, players face a standard dynamic discrete choice problem at decision times that occur stochastically. The resulting stochastic-sequential structure naturally admits the use of conditional choice probability methods for estimation and makes it possible to compute counterfactual simulations for relatively high-dimensional games. The model and method are applied to the retail grocery industry, into which Walmart began rapidly expanding in the early 1990s, eventually attaining a dominant position. We find that Walmart's expansion into groceries came mostly at the expense of the large incumbent supermarket chains, rather than the single-store outlets that bore the brunt of its earlier conquest of the broader general merchandise sector. Instead, we find that independent grocers actually thrive when Walmart enters, leading to an overall reduction in market concentration. These competitive effects are strongest in larger markets and those into which Walmart expanded most rapidly, suggesting a diminishing role of scale and a greater emphasis on differentiation in this previously mature industry.

This paper develops estimators for dynamic microeconomic models with serially correlated unobserved state variables using sequential Monte Carlo methods to estimate the parameters and the distribution of the unobservables. If persistent unobservables are ignored, the estimates can be subject to a dynamic form of sample selection bias. We focus on single-agent dynamic discrete-choice models and dynamic games of incomplete information. We propose a full-solution maximum likelihood procedure and a two-step method and use them to estimate an extended version of the capital replacement model of Rust with the original data and in a Monte Carlo study.

We develop and estimate a dynamic game of strategic firm expansion and contraction decisions to study the role of firm size in future profitability and market dominance. Modeling firm size is important because retail chain dynamics are more richly driven by expansion and contraction than de novo entry or permanent exit. Additionally, anticipated size spillovers may influence the strategies of forward-looking firms, making it difficult to analyze the effects of size without explicitly accounting for these in the expectations and, hence, decisions of firms. Expansion may also be profitable for some firms while detrimental for others. Thus, we explicitly model and allow for heterogeneity in the dynamic link between firm size and profits as well as potential for persistent brand effects through firm-specific unobservable factors. As a methodological contribution, we surmount the hurdle of estimating the model by extending a two-step procedure that circumvents solving the game. The first stage combines semiparametric conditional choice probability estimation with a particle filter to eliminate the serially correlated unobservable components. The second stage uses a forward simulation approach to estimate the payoff parameters. Data on Canadian hamburger chains from their inception in 1970 to 2005 provide evidence of firm-specific heterogeneity in brand effects, size spillovers, and persistence in profitability. This heterogeneous dynamic linkage shows how McDonald’s becomes dominant and other chains falter as they evolve, thus affecting market structure and industry concentration. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2814 . This paper was accepted by J. Miguel Villas-Boas, marketing.

This paper revisits the question of parameter identification when a linear continuous time model is sampled only at equispaced points in time. Following the framework and assumptions of Phillips (1973), we consider models characterized by first-order, linear systems of stochastic differential equations and use a priori restrictions on the model parameters as identifying restrictions. A practical rank condition is derived to test whether any particular collection of at least $\\left\\lfloor {n/2} \\right\\rfloor$ general linear restrictions on the parameter matrix is sufficient for identification. We then consider extensions to incorporate prior restrictions on the covariance matrix of the disturbances, to identify the covariance matrix itself, and to address identification in models with cointegration.

Using unique data on reverse mortgage borrowers in the Home Equity Conversion Mortgage (HECM) program, we semiparametrically estimate a dynamic discrete choice model of borrower behavior. Our estimator is based on a new identification result we develop for models with multiple terminating actions. We show that the per-period utility functions and discount factor are identified without restrictive, ad hoc identifying restrictions that lead to incorrect counterfactual implications. Our estimates provide insights about factors that influence HECM refinance, default, and termination decisions and allow us to quantify the trade-offs involved for proposed program modifications, such as income and credit requirements.

This paper establishes conditions for nonparametric identification of dynamic optimization models in which agents make both discrete and continuous choices. We consider identification of both the payoff function and the distribution of unobservables. Models of this kind are prevalent in applied microeconomics and many of the required conditions are standard assumptions currently used in empirical work. We focus on conditions on the model that can be implied by economic theory and assumptions about the data generating process that are likely to be satisfied in a typical application. Our analysis is intended to highlight the identifying power of each assumption individually, where possible, and our proofs are constructive in nature.

IntroductionIn the acute phase of COVID-19, elevated d-dimer levels indicate a hypercoagulable state putting the patients at increased risk for venous thromboembolic disease (VTE). It is unclear, if prior COVID-19 disease increases the risk for VTE after total joint arthroplasty (TJA) and if d-dimer levels can be used to identify patients at risk.Materials and methodsd-Dimer levels of 313 consecutive SARS-CoV-2 IgG-positive and 2,053 -negative patients undergoing TJA between 05/20 and 12/20 were evaluated. d-Dimer levels were divided into three groups: < 200 ng/ml, 200–400 ng/ml, and > 400 ng/ml d-dimer units (DDU). 277 SARS-CoV-2 IgG-positive patients underwent a Doppler ultrasound to rule out deep-vein thrombosis (DVT) 4–6 weeks after TJA.Resultsd-Dimer levels did not differ significantly between SARS-CoV-2 IgG-positive and -negative patients (p value 0.53). Among SARS-CoV-2 IgG-negative patients, 1687 (82.17%) had d-dimer levels < 200 ng/ml, 256 (12.47%) between 200 and 400 ng/ml, and 110 (5.36%) > 400 ng/ml. Of the SARS-CoV-2 IgG-positive patients, 257 (83.71%) had d-dimer levels < 200 ng/ml, 34 (11.07%) between 200 and 400 ng/ml, and 16 (5.21%) > 400 ng/ml. A postoperative DVT was detected in nine patients (2.9%) in the SARS-CoV-2 IgG-positive group and a PE in one patient (0.3%). 7/229 patients with < 200 ng/ml (3.1%), 1/28 patients (3.6%) with 200–400 ng/ml and 1/9 patients (11.1%) with d-dimer levels > 400 ng/ml had a DVT or PE (p = 0.43).ConclusionsThe findings of this investigation suggest there is no difference in d-dimer levels between SARS-CoV-2 IgG-positive and -negative patients undergoing TJA. Although there is a trend for increased VTE rates with increased d-dimer levels, routine d-dimer testing is not recommended based on the current data. SARS-CoV-2 IgG-positive patients have a low risk of VTE in the current study.

Get With The Guidelines (GWTG)-Stroke is a national stroke registry and quality improvement program. We examined the accuracy and reliability of data entered in GWTG-Stroke. Data entered by sites in the GWTG-Stroke database were compared with that abstracted from de-identified medical records by trained auditors. Accuracy for each individual data element and a composite accuracy measure were calculated. Reliability was assessed using kappa (κ) statistics for categorical variables and intraclass correlation (ICC) for continuous variables. A random selection of 438 medical records from 147 GWTG-Stroke hospitals was obtained. Overall accuracy was above 90% for all variables abstracted except for weight (84.9%), serum creatinine (88.1%), deep venous thrombosis prophylaxis (79.0%), and date/time last known well (85.3%). Intermediate to good (κ or ICC 0.40-0.75) or excellent agreement (κ or ICC ≥0.75) was observed for nearly all audited variables, including time-related performance measures such as arrival within 2 hours of symptom onset (κ = 0.90) and door-to-needle time ≤60 minutes (κ = 0.72). The overall composite accuracy rate was 96.1%. The composite measure varied slightly by region and hospital academic status, but there were no significant differences in composite accuracy by bed size, ischemic stroke volume, primary stroke center certification, or Coverdell Registry participation. This audit establishes the reliability of GWTG-Stroke registry data. Individual data elements with suboptimal accuracy should be targeted for further data quality improvement.

This paper establishes consistency and non-standard rates of convergence for set estimators based on contour sets of criterion functions for a semi-parametric binary response model under a conditional median restriction. The model can be partially identified due to potentially limited-support regressors and an unknown distribution of errors. A set estimator analogous to the maximum score estimator is essentially cube-root consistent for the identified set when a continuous but possibly bounded regressor is present. Arbitrarily fast convergence occurs when all regressors are discrete. We also establish the validity of a subsampling procedure for constructing confidence sets for the identified set. As a technical contribution, we provide more convenient sufficient conditions on the underlying empirical processes for cube-root convergence and a sufficient condition for arbitrarily fast convergence, both of which can be applied to other models. Finally, we carry out a series of Monte Carlo experiments, which verify our theoretical findings and shed light on the finite-sample performance of the proposed procedures.