Economic decisions often depend on many noisy estimates of neighborhood effects, school quality, and hospital performance. Shrinkage estimation can reduce this noise by pooling information across related units. When units are related through geography, adjacency, or shared characteristics, the main challenge is not only how much to shrink, but which relationships should guide pooling. We use Stein's Unbiased Risk Estimate (SURE) to select among and average over flexible shrinkage estimators, allowing researchers to compare candidate definitions of relatedness without treating any one prior, covariance model, or adjacency rule as the true model for the latent parameters. Under regularity conditions stated directly on the estimator maps, SURE selection performs nearly as well as the best rule in a candidate class. The SURE-chosen weighted average likewise performs nearly as well as the best fixed weighted average of trained candidates, including nonlinear shrinkage rules whose fitted values use the full vector of noisy estimates. In an application to Opportunity Atlas economic mobility data from 20 commuting zones, the best individual spatial specification varies across zones, and the SURE-chosen average reduces reported SURE-estimated mean squared error by about 27% relative to the best-performing non-spatial empirical Bayes benchmark.

The package \texttt{rbreak} implements methods for detecting structural breaks and estimating break locations for linear multiple regression models under general linear restrictions on the coefficient vector. Restrictions can be within regimes, across regimes, or both, and are supported in two forms: an affine parameterization (Form A: \texttt{delta = S*theta + s}) and explicit linear constraints (Form B: \texttt{R*delta = r}). It provides break date estimation with confidence interval, a restricted sup-F test for the null of no structural change, simulation of critical values by Monte Carlo, and a bootstrap restart procedure to reduce the risk of convergence to spurious local optima. It also implements a generalized regression tree (linear model tree) procedure where each leaf contains a linear regression rather than a local average. This note explains the methods and illustrates them with applications.

We develop new inference procedures for a linear IV model that are robust to identification strength and heteroskedasticity of unknown form, and nonparametric with respect to the first-stage equation. Our first test is tailored for inference on parameters of endogenous explanatory variables. Our new statistic modifies that of Antoine and Lavergne (2003) to directly account for heteroskedasticity of unknown form. As a result, it is asymptotically pivotal, so that inference is greatly facilitated in practice. We also develop (i) an identification-robust subvector inference procedure that does not rely on the knowledge of identification strength for the remaining parameters, and (ii) a pure specification test. In both cases, the tests are conservative but powerful. We show that our procedures are computationally friendly and competitive with existing ones in simulations and an application.

This paper develops a new toolbox for estimation and inference in panel data threshold regression models with interactive fixed effects and a fixed number of time periods, T. The toolbox is designed to be simple, accurate and computationally efficient. It is based on a simple least squares style estimator of the model parameters, and includes a number of inferential procedures for testing hypotheses regarding not only the threshold but also other parameters. The new toolbox is applied to study the impact of inflation on economic growth.

We describe Chronos LTV, a system to measure the long-term impact of delays and other service defects on key business metrics. We use Markov decision processes to model customer interactions over time, and formalize our target estimand as the marginal policy effect with respect to moving the average delay rate. Given this setup, we show that we can identify long-term effects under a sequential unconfoundedness assumption where delays are as good as random given observed order characteristics; and can estimate these effects using a simple covariate-balancing algorithm.

This paper proposes estimation and inference procedures for quantiles of the heterogeneous individual-specific coefficients in panel data. Unlike conventional panel quantile regression, which focuses on outcome heterogeneity, our approach targets the $\tau$-quantile of the cross-sectional distribution of individual-specific slopes. We establish the asymptotic theory under both stochastic and deterministic designs, with convergence rates $\sqrt{N}$ and $\sqrt{N\sqrt{T}}$, respectively. We also develop two corresponding bootstrap procedures for practical inference, and formally establish their validity. The suggested methods are of practical interest since they require weaker sample size growth conditions than standard fixed-effect quantile regression, and accommodate large $N$ settings. Numerical simulations and an empirical application illustrate the empirical effectiveness of the methods under both designs.

We study causal inference in experiments and quasi-experiments, where the economic outcome is imperfectly measured by a remotely sensed variable. The remotely sensed variable is low-cost, scalable, and predictive of the economic outcome in observational data; examples include satellite imagery and mobile phone activity. We model the remotely sensed variable as post-outcome: variation in the economic outcome causes variation in the remotely sensed variable. For example, changes in environmental quality cause changes in satellite imagery, not vice versa. Under this assumption, we propose a formula to nonparametrically identify the causal parameter by combining experimental and observational data. We develop a method for n^{-1/2} inference that is robust to misspecification and that does not restrict the algorithms used to process remotely sensed variables.

This paper proposes methods for producing compound selection decisions in a Gaussian sequence model. Given unknown, fixed parameters $\mu_ {1:n}$ and known $\sigma_{1:n}$ with observations $Y_i \sim \textsf{N}(\mu_i, \sigma_i^2)$, the decision maker would like to select a subset of indices $S$ so as to maximize utility $\frac{1}{n}\sum_{i\in S} (\mu_i - K_i)$, for known costs $K_i$. Inspired by Stein's unbiased risk estimate (SURE), we introduce an almost unbiased estimator, called ASSURE, for the expected utility of a proposed decision rule. ASSURE allows a user to choose a welfare-maximizing rule from a pre-specified class by optimizing the estimated welfare, thereby producing selection decisions that borrow strength across noisy estimates. We show that ASSURE produces decision rules that are asymptotically no worse than the optimal but infeasible decision rule in the pre-specified class. We apply ASSURE to the selection of Census tracts for economic opportunity, the identification of discriminating firms, and the analysis of $p$-value decision procedures in A/B testing.

Discrete games are central tools for empirical analysis of strategic interaction, but equilibrium multiplicity and partial identification often make them computationally difficult to estimate. This paper develops tractable methods for estimation and inference in complete-information discrete games. The key idea is to construct an outer set by comparing observed frequencies of action profiles with singleton-class generalized likelihoods: model-implied probabilities that those profiles can arise as equilibria. The resulting conditional moment inequalities avoid computationally expensive equilibrium enumeration, numerical simulation, and grid search. Under standard empirical assumptions used in discrete-game models, including logit payoff shocks, these restrictions have closed-form expressions and are convex in a subvector of structural parameters. I develop the approach for both unordered and ordered action spaces. Monte Carlo experiments and empirical applications show that the methods deliver informative outer sets and can reduce computation time by several orders of magnitude relative to existing approaches.

Network interference occurs when the treatment status of one unit affects the potential outcomes of other units, giving rise to spillover effects that are difficult to test for. We propose treating the network as a random variable rather than a fixed quantity to address this challenge. This overcomes a key challenge of non-imputability of potential outcomes under the null and avoids the computational intractability of existing conditional randomization tests. Our quasi-randomization test builds the null distribution of no spillover effects using random graph null models, is exactly valid in finite samples under mild assumptions on the network-generating process, and offers substantially improved power over existing methods, particularly in cluster-randomized trials. We validate our approach via simulation and illustrate it by testing for interference in a weather insurance adoption experiment in rural China.

Several key states in various regions of the U.S. have experienced recent sawtimber as well as pulp and paper mill closures, which raises an important policy question: how have and will key macroeconomic and industry specific indicators within the U.S. forest sector likely to change over time? This study provides empirical evidence to support forest-sector policy design by using a vector error correction (VEC) model to forecast economic trends in three major industries - forestry and logging, wood manufacturing, and paper manufacturing - across six of the most forest-dependent states found by the location quotient (LQ) measure: Alabama, Arkansas, Maine, Mississippi, Oregon, and Wisconsin. Overall, the results suggest a general decline in employment and the number of firms in the forestry and logging industry as well as the paper manufacturing industry, while wood manufacturing is projected to see modest employment gains. These results also offer key insights for regional policymakers, industry leaders, and local economic development officials: communities dependent on timber-based manufacturing may be more resilient than other forestry-based industries in the face of economic disruptions. Our findings can help prioritize targeted policy interventions and inform regional economic resilience strategies. We show distinct differences across forest-dependent industries and/or state sectors and geographies, highlighting that policies may have to be specific to each sector and/or geographical area. Finally, our VEC modeling framework is adaptable to other resource-dependent industries that serve as regional economic pillars such as mining, agriculture, and energy production offering a transferable tool for policy analysis in regions with similar economic structures.

This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers. We provide a framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global effect. We show that optimal clustering solves a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes simple conditions to choose between any two cluster designs, including choosing between a cluster or individual-level randomization. We illustrate the method's properties using unique network data from the universe of Facebook's users and existing data from a field experiment.