Given data on a scalar random variable 𝑌, a prediction set for 𝑌 with miscoverage level 𝛼 is a set of values for 𝑌 that contains a randomly drawn 𝑌 with probability 1 - 𝛼, where 𝛼 ∈ (0, 1). Among all prediction sets that satisfy this coverage property, the oracle...

We propose the double robust Lagrange multiplier (DRLM) statistic for testing hypotheses specified on the minimizer of the population continuous updating objective function. The (bounding) χ2 limiting distribution of the DRLM statistic is robust to both misspecification and weak identification,...

Given data on a scalar random variable 𝑌, a prediction set for 𝑌 with miscoverage level 𝛼 is a set of values for 𝑌 that contains a randomly drawn 𝑌 with probability 1 − 𝛼, where 𝛼 ∈ (0, 1). Among all prediction sets that satisfy this coverage property, the oracle...

Star variables, such as potential output and the neutral real interest rate, are fundamental to economic policymaking but challenging to identify due to their latent nature. Buncic, Pagan, and Robinson (2023) highlight the difficulty of identifying star variables within short macroeconomic...

This paper proposes a subsampling inference method for extreme conditional quantiles based on a self-normalized version of a local estimator for conditional quantiles, such as the local linear quantile regression estimator. The proposed method circumvents difficulty of estimating nuisance...

A unified theory of estimation and inference is developed for an autoregressive process with root in (-∞, ∞) that includes the stationary, local-to-unity, explosive and all intermediate regions. The discontinuity of the limit distribution of the t-statistic outside the stationary region and...

This paper introduces Parameter Estimation by Raw Moments (PERM), a flexible method for evaluating a policy's impact on the parameters of an outcome distribution. Such parameters include the variance (E[Y 2 ]−E[Y] 2 ), skewness and covariance of two outcomes. PERM simplifies distributional...

We use a rigorous three-stage many-analysts design to assess how different researcher decisions—specifically data cleaning, research design, and the interpretation of a policy question—affect the variation in estimated treatment effects. A total of 146 research teams each completed the same...

We study the estimation of causal treatment effects on demand when treatment is randomly assigned but prices adjust in response to treatment. We show that regressions of demand on treatment or on treatment and price lead to biased estimates of the direct treatment effect. The bias in both cases...

Building on the sequential identification result of Aguirregabiria and Mira (2019), this paper develops estimation and inference procedures for static games of incomplete information with payoff‐relevant unobserved heterogeneity and multiple equilibria. With payoff‐relevant unobserved...

This paper considers the problem of deriving heteroskedasticity and autocorrelation robust (HAR) inference about a scalar parameter of interest. The main assumption is that there is a known upper bound on the degree of persistence in data. I derive finite‐sample optimal tests in the Gaussian...

Forest degradation by fires is growing in a fast pace in the Brazilian portion of the Amazonian rainforest, damaging the regional environment and economy. Seeking to measure the impact of such process on rainfall, the paper analyses a wide diversity of fine-grained satellite measurements with...

We study the application of approximate mean field variational inference algorithms to Bayesian panel VAR models in which an exchangeable prior is placed on the dynamic parameters and the residuals follow either a Gaussian or a Student-t distribution. This reduces the estimation time of possibly...

This paper introduces conformal inference, a powerful and flexible framework for constructing prediction intervals with guaranteed coverage in finite samples. Unlike conventional methods, conformal inference makes no assumptions about the underlying data distribution other than exchangeability....

Most research questions in agricultural and applied economics are of a causal nature, i.e., how one or more variables (e.g., policies, prices, the weather) affect one or more other variables (e.g., the welfare of individuals or the society, the demanded or produced quantity, pollution). Only a...

We consider estimation and inference for a regression coefficient in panels with interactive fixed effects (i.e., with a factor structure). We demonstrate that existing estimators and confidence intervals (CIs) can be heavily biased and size-distorted when some of the factors are weak. We...

We consider a linear panel data model with nonseparable two-way unobserved heterogeneity corresponding to a linear version of the model studied in Bonhomme et al. (2022). We show that inference is possible in this setting using a straightforward two-step estimation procedure inspired by existing...

This paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate structural parameters. We introduce two new stacking approaches for DDML: short-stacking exploits the cross-fitting step of DDML to...

In this short paper we review the intellectual history of indirect inference as a methodology in its progress from an informal method for evaluating early models of representative agents to formally testing DSGE models of the economy; and we have considered the issues that can arise in carrying...

A statistical decision rule is a mapping from data to actions induced by statistical inference on the data. We characterize these rules for data that are chosen strategically in persuasion environments. A designer wishes to persuade a decision maker (DM) to take a particular action and decides...

We analyze over 44,000 economics working papers from 1980–2023 using a custom language model to construct knowledge graphs mapping economic concepts and their relationships, distinguishing between general claims and those supported by causal inference methods. The share of causal claims within...

We analyze over 44,000 economics working papers from 1980-2023 using a custom language model to construct knowledge graphs mapping economic concepts and their relationships, distinguishing between general claims and those supported by causal inference methods. The share of causal claims within...

Determining a plausible number of components in a factor model is a nontrivial issue in case of weak data, sparse model restrictions and diffuse prior information. We discuss the issue of structural parametric identification in a static factor model and introduce orthogonal restrictions which...

When estimating treatment effects, the gold standard is to conduct a randomized experiment and then contrast outcomes associated with the treatment group and the control group. However, in many cases, randomized experiments are either conducted with a much smaller scale compared to the size of...

This study's main purpose is to use Bayesian structural time-series models to investigate the causal effect of an earthquake on the Borsa Istanbul Stock Index. The results reveal a significant negative impact on stock market value during the post-treatment period. The results indicate rapid...

This paper proposes a score-driven model for filtering time-varying causal parameters through the use of instrumental variables. In the presence of suitable instruments, we show that we can uncover dynamic causal relations between variables, even in the presence of regressor endogeneity which...