Publications
- Review of Economics and Statistics, Forthcoming.This paper proposes a robust method for semiparametric identification and estimation in panel multinomial choice models, where we allow for infinite-dimensional fixed effects that enter into consumer utilities in an additively nonseparable way, thus incorporating rich forms of unobserved heterogeneity. Our identification strategy exploits multivariate monotonicity in parametric indexes, and uses the logical contraposition of an intertemporal inequality on choice probabilities to obtain identifying restrictions. We provide a consistent estimation procedure, and demonstrate the practical advantages of our method with Monte Carlo simulations and an empirical illustration on popcorn sales with the Nielsen data.
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- Quantitative Economics, 16(3), 2025, 823--858.This paper considers nonparametric estimation and inference in first-order autoregressive (AR(1)) models with deterministically time-varying parameters. A key feature of the proposed approach is to allow for time-varying stationarity in some time periods, time-varying nonstationarity (i.e., unit root or local-to-unit root behavior) in other periods, and smooth transitions between the two. The estimation of the AR parameter at any time point is based on a local least squares regression method, where the relevant initial condition is endogenous. We obtain limit distributions for the AR parameter estimator and t-statistic at a given point in time when the parameter exhibits unit root, local-to-unity, or stationary/stationary-like behavior at time. These results are used to construct confidence intervals and median-unbiased interval estimators for the AR parameter at any specified point in time. The confidence intervals have correct asymptotic coverage probabilities with the coverage holding uniformly over stationary and nonstationary behavior of the observations.
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- Journal of Econometrics, 235, 2023, 302--324.→ Award: Zellner Award, Journal of Econometrics, 2025.This paper considers a semiparametric model of dyadic network formation under nontransferable utilities (NTU). NTU arises frequently in real-world social interactions that require bilateral consent, but by its nature induces additive non-separability. We show how unobserved individual heterogeneity in our model can be canceled out without additive separability, using a novel method we call logical differencing. The key idea is to construct events involving the intersection of two mutually exclusive restrictions on the unobserved heterogeneity, based on multivariate monotonicity. We provide a consistent estimator and analyze its performance via simulation, and apply our method to the Nyakatoke risk-sharing networks.
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Working Papers
- Resubmitted, Journal of Econometrics.This paper proposes a correlated random coefficient linear panel data model, where regressors can be correlated with time-varying and individual-specific random coefficients through both a fixed effect and a time-varying random shock. I develop a new panel data-based method to identify the average partial effect and the local average response function. The identification strategy employs a sufficient statistic to control for the fixed effect and a control variable for the random shock. Conditional on these two controls, the residual variation in the regressors is driven solely by the exogenous instrumental variables, and thus can be exploited to identify the parameters of interest. The constructive identification analysis leads to three-step series estimators, for which I establish rates of convergence and asymptotic normality. To illustrate the method, I estimate a heterogeneous Cobb-Douglas production function for manufacturing firms in China, finding substantial variations in output elasticities across firms that can be related to various firm characteristics.
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- This paper considers confidence intervals (CIs) for the autoregressive (AR) parameter in an AR model with an AR parameter that may be close or equal to one. Existing CIs rely on the assumption of a stationary or fixed initial condition to obtain correct asymptotic coverage and good finite sample coverage. When this assumption fails, their coverage can be quite poor. In this paper, we introduce a new CI for the AR parameter whose coverage probability is completely robust to the initial condition, both asymptotically and in finite samples. This CI pays only a small price in terms of its length when the initial condition is stationary or fixed. The new CI also is robust to conditional heteroskedasticity of the errors.
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- This paper studies parametric estimation and inference in a dyadic network formation model with nontransferable utilities, incorporating observed covariates and unobservable individual fixed effects. We address both theoretical and computational challenges of maximum likelihood estimation in this complex network model by proposing a new bootstrap aggregating (bagging) estimator, which is asymptotically normal, unbiased, and efficient. We extend the approach to estimating average partial effects and analyzing link function misspecification. Simulations demonstrate strong finite-sample performance. Two empirical applications to Nyakatoke risk-sharing networks and Indian microfinance data find insignificant roles of wealth differences in link formation and the strong influence of caste in Indian villages, respectively.
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- We develop a tractable identification approach for strategic network formation models that accommodate both strategic link interdependence and individual unobserved heterogeneity (fixed effects). The key challenge in this setting is that endogenous network statistics (such as the number of common friends) enter the link formation equation, while the mapping from model primitives to equilibrium network structure is generally intractable to characterize and compute. Using a "bounding-by-c" technique, our approach circumvents this difficulty by treating endogenous covariates as random variables and exploiting monotonicity restrictions to extract identifying information without requiring characterization of the equilibrium. We derive a system of identifying restrictions based on subnetwork configurations: leading tetrad-based restrictions achieve complete elimination of all individual fixed effects, triad-based restrictions that only partially difference out fixed effects, and general weighted cycle-based restrictions. We show via numerical simulation that our approach provides informative bounds on the model parameter in a network formation model with strategic complementarity.
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