ALT We study estimation of the average partial effect (APE) in an irregular correlated random coefficients panel data model in which the time dimension equals the number of random coefficients, T=p, and the density function is degenerate. We extend Graham and Powell (2012) in two directions. First, we allow the density of dᵢ=det(Xᵢ) to vanish or diverge at the origin at the polynomial rate |d|ᵃ with a>-1, and show that the aggregate time effects and the APE remain identified for every such a, extending earlier work studying the case a = 0. Second, we propose a three-step estimator: an mth-order local polynomial regression over the stayers for the time effects; a local polynomial fit of the conditional mean coefficient just outside the trimming threshold; and an imputation step that extrapolates the latter into the stayer region rather than discarding stayers. We establish joint asymptotic normality in three regimes determined by the sign of a, and obtain a convergence rate approaching n⁻⁽ᵐ

ALT I analyze decision problems where a planner must choose, based on data from an experiment in a pilot population, how to simultaneously distribute a limited number of treatments that generate network spillovers across individuals in a target population. A global treatment rule maps the target population's observed covariates and network features into a jointly feasible allocation. Under local-response, stability, support, and policy-relevant overlap conditions, I identify the resulting welfare using a transported inverse propensity weighted criterion and construct an empirical welfare maximizer. I derive a finite-sample regret bound under restrictions on the complexity of network spillovers and the complexity of the global rule class, measured by its Natarajan dimension. The upper bound uses independent node-level variation, dyadic network formation without imposing a bounded maximum degree. A minimax lower bound establishes a corresponding rate comparison under additional welfare-relev

ALT We propose an instrument-based test for peer effects in short-T panels when the underlying network is unknown. The test avoids estimating the network by aggregating pairwise comparisons between covariate-projected outcomes of potential peers and residualized own outcomes. The statistic is designed for fixed-T, large-N settings with individual fixed effects, heterogeneous slopes, heteroskedasticity, and serial dependence within units. We also develop a weighted extension that accounts for additive common time effects. The limiting distribution is standard Gaussian when the norm of the projected component of the outcomes is bounded away from zero, but can be nonstandard when it is local to zero. We derive a conditional Gaussian limit experiment and a feasible conditional Monte Carlo procedure to obtain uniformly valid critical values. Monte Carlo evidence shows that the conditional procedure leads to accurate size control while retaining power against latent peer-effect alternatives.

ALT This paper examines whether inflation compensation around Brazilian monetary policy decisions is associated differently with policy forecast errors when sovereign spreads are relatively elevated. Public Tesouro Direto quotations, meeting-specific Focus forecasts and a lagged EMBI indicator yield 68 three-year and 60 five-year event observations. The outcome is a matched-horizon retail yield proxy; the policy variable is the announced Selic target minus a preceding-week survey mean. At three years, a 10-basis-point forecast error is associated with 4.14 basis points less compensation repricing in the high-spread state than in the low-spread state, but the restricted wild cluster test gives a two-sided p-value of 0.1018. The estimated total high-state response is -0.42 basis points, with a pointwise 95 percent interval of [-2.25, 1.41]. The five-year interaction is sensitive to pandemic meetings and changes sign when 2020 is excluded. A common sample of 43 events does not establish a mat

ALT We develop a rational-inattention BLP (RI-BLP) model for demand estimation from aggregate market shares, allowing for endogenous prices and unobserved preference heterogeneity. We establish conditions for uniqueness of market-share inversion and construct a simulated RI-BLP GMM estimator with instrumental-variable moments and market-share constraints. Under regularity and simulation-accuracy conditions, the estimator is consistent and asymptotically normal as the number of markets grows, with the number of products fixed. Monte Carlo experiments show accurate recovery of mean utility coefficients, with less precise estimates of dispersion parameters. In a canned-tuna application, RI-BLP and logit yield similar average own-price elasticities. Unlike logit, the fitted RI-BLP model implies that a price increase can reduce demand even for similar products and shift demand toward the outside option.

ALT Many policies determine social arrangements whose effects depend on the relationships formed afterwards. We study how to learn such policies when a constrained joint action changes network formation and outcomes depend on exposures to the resulting network. Policy value integrates an outcome response over the distribution of post-assignment networks. The large number of feasible arrangements makes it difficult to evaluate policies directly from historical data. We therefore adopt a model-based approach that pools information across assignments to evaluate alternative arrangements. We characterize identification of policy values and construct plug-in estimators. Our regret bounds account for sampling variation, first-stage estimation error, and computational errors. Our applications to classroom seating and business groups illustrate predicted welfare gains from learned rules relative to random assignment.

ALT When including covariates in Difference-in-Differences, researchers must consider not only which covariates to include, but also how to model them. The Common Causality of Covariates (CCC) assumption requires that covariate coefficients in the untreated potential-outcome model remain constant across regions and periods. Violations can bias DID estimators even when the relevant covariates are correctly included. I develop a CCC test based on Wald tests from an additive model that uses untreated observations in pre-treatment periods to assess whether covariate coefficients vary across regions, periods, or both. Motivated by this specification problem, I show theoretically that well-specified Integrated DID estimators are consistent, whereas overly restrictive models can introduce bias and, under conditional homoskedasticity, unnecessarily flexible models can reduce efficiency. Monte Carlo simulations show that the CCC test has good size and power. An application to private-sector Ban-the

ALT We compare the relation between sentiment noise in the Twitter feed and losses for stocks of domestic and foreign firms. The mean quarterly domestic-foreign difference in the slope of next-quarter losses on measured noise is -1.40 (Newey-West t = -2.99). This slope is negative for 78 and 48 percent of the quarters for domestic and foreign stocks, respectively (block-bootstrap p = 0.016). Thus, this relationship is temporally more stable for domestic stocks. We use a noisy rational expectations model to explain these patterns through the composition of the contributor pool. The feeds for stocks of foreign firms have higher repeat-author concentration and a larger model-implied nonfundamental component than those for domestic firms. We interpret these differences through the producer-knowledge mismatch hypothesis. For foreign firms, the English-language contributor pool underrepresents the home-market investors who tend to possess informational advantages about them. Thus, the foreign si

ALT In this paper, we propose a simple score-based specification test for the Tobit regression model, implemented through a computationally efficient multiplier bootstrap. We test correct specification through the conditional moment restriction that the generalized Tobit residual has zero conditional mean, and construct a feasible score-based empirical process using the integrated conditional moments approach, conditioning on the estimated index so that no smoothing or tuning parameter is required. We derive a first-order uniform representation of the feasible process, showing how its limiting behavior is affected by maximum-likelihood estimation of the structural parameter, and obtain the asymptotic properties of the corresponding Kolmogorov–Smirnov statistic under the null, fixed alternatives, and a sequence of local alternatives. Since the limiting null distribution is non-pivotal, p-values are obtained from a multiplier bootstrap that mimics the first-order representation and therefore

ALT This paper studies data-driven macroeconomic policy choice in a structural VAR framework. We formulate the policymaker's problem of optimizing the future path of a policy instrument as a statistical decision problem under two alternative policy regimes: a "contemporaneous" regime, where the chosen path is implemented through unexpected policy shocks each period, and a "forward guidance" regime, where the expected path is announced in advance and enforced through anticipated policy shocks. We derive oracle, Bayes-optimal, and robust Bayes decision rules, the last for the case where structural impulse responses are only set-identified, and show that the decision problem depends on the structural rotation matrix only through the span of the columns associated with the policy shocks. We argue that the plug-in rule is uniformly dominated by a James-Stein-type shrinkage rule in a local asymptotic Gaussian limit experiment, and is therefore inadmissible therein. Numerical analysis calibrated

ALT This paper studies social interactions in discrete choice models in which individuals can respond differently to different network members, conforming to some while distinguishing from others. In this generalized framework, I show how to use within-network variation in individual choices to draw inference about differences in endogenous interaction effects, while accounting for unobserved contextual effects. I also show how to construct internal instruments to correct for measurement error bias, a key source of endogeneity in network models with incomplete information. I apply these results to data from two empirical settings: classroom peer effects in Tennessee primary schools and spillovers in deworming treatment uptake in Kenya. In both applications, I find that differences in social interaction effects, with individuals conforming more strongly to similar peers, play a key role in shaping people's choices, with implications for the targeting of policy interventions.

ALT This paper investigates the ability of random feature models to learn more complex patterns than the simple negative autocorrelation of financial returns. Theoretical insights highlight that the learning capacity of random features ridge regression is determined by the interplay between signal-to-noise ratio (SNR) level and regularization. We characterize the different learning regimes through the derivation of a particular threshold: the SNR frontier. Practically, our results permit to determine the minimum penalty level to be introduced into the estimation process in order to guard against a drop in the SNR during the implementation phase. We empirically apply this methodology to equity premium forecasting and use the output to build mean-variance efficient portfolios. The resulting portfolios yield substantial economic value over a portfolio construction scheme based on a short-term reversal strategy.

ALT Researchers often observe group quantiles but not group means, raising the question of whether these summaries alone support conventional regression analysis. We develop estimation and inference methods for linear models of group means under quantile-only and mixed disclosure. We establish conditions on disclosure design for consistent estimation and asymptotic normality at the parametric rate. Under fixed disclosure, we characterize sharp identified sets and construct simultaneous confidence regions, clarifying how informativeness depends on the number and placement of disclosed quantiles. Applications to education, social care, and medical expenditure illustrate the methods. The R package qonlyreg implements the proposed procedures.

ALT The synthetic control method matches a treated unit to a convex combination of donor units, with the relative importance of each predictor determined by a weight matrix V. In standard practice, V is selected through a bi-level optimization problem that is nonconvex and lacks convergence guarantees. This article derives an exact geometric solution for synthetic control estimators. For any positive definite V, a synthetic control is given by the projection of the treated unit's predictors onto the convex hull of the donor predictors under a V-weighted norm. As V varies over its candidate class, these projections trace out the set of attainable synthetic controls. We characterize this set. When the predictor vector of the treated unit lies outside the convex hull of the donor predictors, the attainable set under general positive definite V is the union of the facets of the donor hull that are visible from the treated unit. For diagonal V, a sign-compatibility test identifies the attainabl

ALT Panel local projections are widely used to estimate impulse responses in policy analysis. This paper examines the properties of the fixed-effect estimator and various bias-correction methods in panel local projections, focusing on settings with autoregressive control variables. We analyze the bias and variance of these estimators while allowing for persistence in both the target and control variables. Our theoretical findings indicate that the half-panel jackknife has desirable bias-reducing properties beyond the stationary setup. For empirical researchers, we recommend using bias-correction-adjusted standard errors to account for the sampling uncertainty associated with the half-panel jackknife estimator, regardless of the time-series dimension of the data.

ALT Most empirical models of trend inflation treat it as a latent random walk driven by unobserved shocks, so they cannot say which economic conditions move it. Deep-π* makes the innovations to the trend a possibly nonlinear function of a large number of macroeconomic indicators, estimated with neural networks, so that Shapley values can be used to attribute every movement of the trend to the indicators behind it. Two complementary loss functions define a current trend, which fits inflation today, and a forward-looking trend, which best predicts inflation over the next year. In US data, employment-related variables were the largest contributors to the post-pandemic rise of the current trend. However, the forward-looking trend rose earlier, with real activity, employment, and money and credit as the main drivers, and peaked eight to twelve months before the current trend. Both trends looked through most of the movements in energy and commodity prices. The contributions of the indicators are

ALT This paper develops tuning-free tests for treatment-effect heterogeneity. By characterizing the null hypothesis as constancy of the expectile treatment-effect curve, we construct test statistics as continuous functionals of the mean-centered sample expectile process. We establish a uniform Bahadur representation, derive the asymptotic properties of the test statistics under the null and fixed alternatives, and show nontrivial power against local alternatives approaching the null at the n⁻¹/² rate. For feasible inference, we develop a fast multiplier bootstrap that consistently approximates the nonpivotal null distribution while capturing the first-order effect of estimating the unknown common shift. The procedure is tuning-free, requiring no density or score-function estimation, bandwidth selection, or repeated refitting. The framework further incorporates a pointwise-studentized refinement and a covariate adjustment that reduces asymptotic variation without altering the population dis

ALT We study the design of multi-arm experiments that identify local average treatment effects under imperfect compliance. With three or more arms, conventional randomization fails to identify margin-specific effects even under strong extensions of monotone compliance. A choice-theoretic analysis unifies these extensions and motivates a two-stage design: subjects report a preferred arm and are randomized conditional on it. If treatment is confined to the assigned or reported arm, one-sided noncompliance is restored conditional on the report, identifying the effect between the two for their compliers. We discuss the design's applicability and feasibility in relation to the choice model.

ALT We study inference on common slope parameters in linear panel models with two-way unobserved heterogeneity. In linear regression, bias arises from unobserved heterogeneity that is shared between the residual and the covariates. Our common unobserved heterogeneity (CUH) estimator targets this common variation directly: it searches over functionals of the residualised outcome for variation that also resides in the covariates, and seeks to further project out only bias-inducing variation. This makes the estimator less sensitive to weak outcome factors while avoiding the complete removal of exogenous variation in covariates that is unrelated to the confounding heterogeneity. We develop a weak-factor theory based on a singular-value strength index and establish √NT-asymptotic normality of the cross-fitted CUH estimator. Under homoskedastic errors, its asymptotic variance is no larger than that of a common correlated effects factor estimator. Simulations and empirical applications illustrate

ALT This paper studies inference on the value of a linear program (LP) when the objective and constraints are estimated. Using LP duality, we construct one- and two-sided confidence intervals by inverting tests of the feasibility of systems of linear inequalities. Existing tests yield asymptotically valid confidence intervals but can have poor power. We propose a modification that augments these systems with estimated “box constraints.” The resulting procedure has power against N⁻¹/²-local alternatives and yields confidence intervals of order N⁻¹/². These results require only that the LP value be finite, and do not require compactness of the feasible region nor standard constraint qualifications. We obtain stronger guarantees under various constraint qualifications and establish uniform validity over large classes of DGPs when known deterministic constraints bound the primal and/or dual variables. Simulations demonstrate good finite-sample performance.