We introduce a new instrumental-variables (IV) approach for spatial dynamic panel-data models with interactive effects under large N and T asymptotics. Alongside the methodology, we present the spxtivdfreg package, which implements the proposed approach in Stata. Most existing approaches in this literature rely on quasi–maximum likelihood estimation. Our IV approach is appealing from both theoretical and practical standpoints for several reasons. First, it is linear in the parameters of interest and computationally inexpensive. Second, the IV estimator avoids the asymptotic bias that typically arises from the incidental parameters problem. Third, the approach accommodates endogenous regressors, provided suitable external instruments are available.

For the homogeneous-slope case, we develop a pooled two-stage IV (2SIV) estimator, which is consistent and asymptotically normal as N and T grow large. For the heterogeneous-slope case, we propose an N-consistent mean group IV (MGIV) estimator based on averaging individual-specific estimated slopes. To our knowledge, no existing method in the literature allows for this level of heterogeneity in dynamic spatial models with interactive effects.

We also provide practical guidance on how best to run these methods in Stata.

This paper studies identification, estimation, and inference of general unconditional quantile partial effects (UQPE) under endogeneity. When a valid instrument is available, we show that using a control-function approach, the UQPE can be identified through the conditional average of the conditional quantile partial effects, given the unconditional quantile of the dependent variable of interest. Based on this identification result, we propose a semiparametric two-step estimator. The first step is based on a control-function quantile regression method, and the second step uses a nonparametric estimator to compute the conditional average. This general formulation includes nonparametric regressions and sieve estimators. The asymptotic properties of the estimator are derived, namely, consistency and asymptotic normality. We also develop practical statistical inference procedures and establish the validity of a bootstrap approach. Monte Carlo simulations show that the proposed methods have good finite-sample properties. Finally, we apply the proposed methods to estimate unconditional quantile effects of class size on educational performance.

Empirical studies using regression discontinuity (RD) designs often explore heterogeneous treatment effects based on pretreatment covariates, even though no formal statistical methods exist for such analyses. This has led to the widespread use of ad hoc approaches in applications. Motivated by common empirical practice, we develop a unified, theoretically grounded framework for RD heterogeneity analysis. We show that a fully interacted local linear (in functional parameters) model effectively captures heterogeneity while still being tractable and interpretable in applications. The model structure holds without loss of generality for discrete covariates. Although our proposed model is potentially restrictive for continuous covariates, it naturally aligns with standard empirical practice and offers a causal interpretation for RD applications. We establish principled bandwidth selection and robust bias-corrected inference methods to analyze heterogeneous treatment effects and test group differences. We provide companion software to facilitate implementation of our results. An empirical application illustrates the practical relevance of our methods.

Young adults in the United States face critical decisions after high school, often defined by employment, enrollment, or enlistment. Military service provides educational benefits, but the attractiveness of this pathway wanes with perceived college affordability. We use the rollout of tuition-free community college in Tennessee to study the effects of Promise scholarships on enlistment. We find a four percent decline in military enlistment driven by Army and Navy and concentrated in low-income counties and the most populous counties. In addition, the composition of successful enlistees shifted toward those with more mechanical and automotive aptitudes.

Committees for the management and redistribution of public resources are common in a variety of settings, ranging from homeowners’ associations (HOAs), to parent–teacher organizations to government councils. Why do some individuals join these committees, and what predicts their behaviors once they become committee members? Joining is costly but necessary for the provision of public goods. Prosocial, intrinsically motivated individuals may therefore be more likely to self-select into committees. However, because there is little oversight and transparency over committee expenditures, making it relatively easy to embezzle funds, committees could also attract the most dishonest individuals. We employ a laboratory experiment to test whether and to what extent individuals’ decision to join a committee in charge of public funds depends on their type (honest versus dishonest, and prosocial versus self-interested) and their subjective beliefs of how (dis)honest the existing committee members are. We also test whether mechanisms that resemble town hall meetings and require committee members to communicate their decisions to the public affect both the decision to engage in corruption and the decision to join committees.

Full abstract forthcoming.