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...

This paper provides a critical examination of the empirical basis of the output convergence debate in the light of recent developments in the analysis of dynamic heterogeneous panels with interactive effects. It shows that popular tools such as Barro's cross-country regressions and two-way fixed...

Sequence-space models are becoming increasingly popular in macroeconomics, especially in the heterogeneous-agent literature. However, the econometric toolkit for users of these models remains less developed than that available for traditional state-space methods. This note introduces an...

It is well known that standard frequentist inference breaks down in IV regressions with weak instruments. Bayesian inference with diffuse priors suffers from the same problem. We show that the issue arises because flat priors on the first-stage coefficients overstate instrument strength. In...

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...

We present the R package MSTest, which implements hypothesis testing procedures to determine the number of regimes in Markov switching models. These models have wide ranging applications in economics, finance, and many other fields. MSTest provides several testing frameworks, including Monte...

Statistical inference-both estimation and testing-for stochastic volatility (SV) models is known to be challenging and computationally demanding. We propose simple and efficient estimators for SV models with conditionally heavy-tailed error distributions, particularly the Student's t and...

We derive an admissible parameter space for vector Multiplicative Error Models (vMEMs), explicitly formulating it in terms of the model's matrix parameters through a set of matrix inequalities. Another key contribution is the adoption of constrained maximum likelihood estimation for the...

Purpose - We propose a method to build confidence sets for the solutions of stochastic discrete optimization problems solved through the sample average approximation method. Design/methodology/approach - By combining the concept of Model Confidence Set (MCS) with shrinkage estimation of large...

The key assumption of normally distributed error terms is usually not tested in empirical practice when using ordered probit models. Therefore, an artificial regression version of the LM test against the class of Pearson distributions is derived that can be implemented more easily than the...

The rare access to exact official geocoordinates opens new methodological possibilities for analyzing highly sensitive tax data. We explore their visualization potential and systematically evaluate aggregation as an anonymization strategy, with particular attention to its methodological and...

The availability of geocoordinates offers valuable insights into spatial patterns of economic, demographic and health outcomes. However, disclosing the exact geolocation of statistical units to secondary analysts contravenes the responsible use of data. To protect privacy, anonymisation methods...

The problem with FDI flows is that zero or negative flows are being ignored or handled differently. This paper modifies the heterogeneous firm model proposed by Helpman et al. (2008) and examines the determinants of the bilateral outward foreign direct investment (FDI) flows. Given the...

The paper derives and proves results of Gaussian maximum likelihood estimation of constant unknowns (coefficients, covariances) and time-varying unknowns (factors, disturbances) of static and dynamic factor models and, thereby, extends the statistics and econometrics literatures on estimation...

We propose a novel dynamic panel estimator. Different from the commonly used difference and system GMM, our proposed estimator requires only one of the crosssectional dimension (N) or the time dimension (T) to grow large to be asymptotically unbiased. This improves reliability in panels with...

In structural vector autoregressive analysis it has become quite popular to identify some structural shocks of interest by external instruments or proxies. This study points out a range of areas where such proxies have been used and sketches the way the proxies have been constructed. It reviews...

This paper provides an introduction to Double/Debiased Machine Learning (DML). DML is a general approach to performing inference about a target parameter in the presence of nuisance functions: objects that are needed to identify the target parameter but are not of primary interest. Nuisance...

We propose a demand estimation approach that leverages unstructured data to infer substitution patterns. Using pre-trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a mixed logit demand model. This approach enables demand...

Most empirical estimates of the trade elasticity exploit tariff variation generated by Preferential Trade Agreements (PTAs). Because firms only partly utilize preferential tariffs, standard regressions identify an eligible-tariff elasticity rather than the structural elasticity with respect to...