We approximate the finite-sample distribution of impulse response function (IRF) estimators that are just-identified with a weak instrument using the conventional local-to-zero asymptotic framework. Since the distribution lacks a mean, we assess bias using the mode and conclude that researchers...

We propose a nonparametric framework for estimating the extremal index that captures the persistence of extreme observations. The framework provides unified and simple procedures for verifying the well-known local dependence condition D(ᵈ) (un), which characterizes the extremal index yet is...

This paper by Blueprint Director Joshua Angrist, Alberto Abadie, Brigham Frandsen, and Jörn-Steffen Pischke surveys econometric innovations related to differences-in-differences estimators and to event-study models with time-varying treatment effects. The researchers discussion highlights...

Many instrumental variables applications specify a single Bernoulli treatment. But instruments may change outcomes through multiple pathways or by varying treatment intensity. Lottery instruments that boost charter school enrollment, for instance, may affect outcomes by lengthening time enrolled...

This study proposes a regression-based estimation method in difference-in-differences settings in the presence of time-varying covariates - a scenario commonly encountered in applications. We impose only a conditional parallel trends assumption with time-varying covariates and plausible...

Difference-in-differences (DiD) is one of the most popular approaches for empirical research in economics, political science, and beyond. Identification in these models is based on the conditional parallel trends assumption: In the absence of treatment, the average outcome of the treated and...

A central question in class action litigation is all or nearly all proposed class members were injured by the alleged conduct, that is, does "common impact" exist. In this paper, we demonstrate that a two-step econometric methodology can be used to estimate customer-level overcharges, providing...

The Pareto distribution has been widely used to model income distribution and inequality. The tail index and the Gini index are typically computed by iteration using Maximum Likelihood and are usually interpreted in terms of the Lorenz curve. We derive an alternative method by considering a...

Agent-based models (ABMs) are gaining increasing traction in several domains, due to their ability to represent complex systems that are not easily expressible with classical mathematical models. This expressivity and richness come at a cost: ABMs can typically be analyzed only through...

Forecasting natural gas demand is critical for enhancing energy efficiency, optimizing infrastructure planning, and supporting Tunisia's transition toward sustainable energy. As the fastest-growing fossil fuel and one of the cleanest among non-renewable energy sources, natural gas plays a...

This paper explores classical and Bayesian approaches to the estimation of unknown parameters and reliability functions for the power Rayleigh distribution. The maximum likelihood estimator (MLE) method is considered in classical estimation. The Bayesian estimation, on the other hand uses...

In statistical analysis, evaluating the normality of large datasets is crucial for validating parametric tests, particularly in areas such as Global Navigation Satellite System (GNSS) measurements, where data often exhibit non-normal characteristics resulting from their variability and errors....

A class of estimators of finite population variance (S_y^2) using auxiliary information has been proposed under simple random sampling without replacement (SRSWOR) scheme. An attempt has been made to derive the minimum variance unbiased estimator of finite population variance from the proposed...