This paper examines long-run linkages and possible instabilities in the gold–silver price relationship using daily futures prices over the period from 4 January 2010 to 28 November 2025. The empirical analysis includes unit-root and cointegration tests as well as endogenous structural break...

We use small-area estimation methods that combine information from a household budget survey, a much larger survey of local demographics and employment, and area-level information on bank account outflows and energy consumption to estimate average equivalised consumption measures across 367...

This document introduces the R library estimateW to estimate spatial weight matrices for Bayesian spatial econometric panel models. The approach focuses on spatial weights that are binary prior to row-standardization. However, unlike recent literature our approach requires no strong a priori...

This study examines the role of volatility mean reversion in option pricing and evaluates the performance of commonly used volatility estimators within a broad market context. Using a comprehensive dataset of end-of-day option chains for the 100 most actively traded U.S. equities from 2018 to...

Extreme movements in financial time series pose challenges for risk management and forecasting, particularly when their timing is irregular and difficult to anticipate. This study aims to develop a probabilistic framework for detecting and predicting such events using daily Bitcoin returns as a...

We propose a new model in which relationship-specific e!ects or shocks are identified in a bipartite network under mild covariance restrictions, generalizing the influential Abowd et al. (1999) framework. For example, separate demand shocks are identified for each bank from which a firm borrows....

Total factor productivity (TFP) is crucial for evaluating technological innovation, yet its accurate measurement remains challenging due to diverse methodologies. Notably, the selection of proxy variables in control function approach (CFA) significantly influences TFP estimation, yet existing...

This paper presents a novel method for estimating treatment effects in cases where prior knowledge of the exogeneity of the treatment variable is limited. We employ a machine learning technique, double selection via Lasso, to identify a robust set of control variables without requiring prior...

Applied researchers commonly interpret coefficient movements across OLS and difference-in-differences specifications with varying controls as evidence of bias reduction or improved identification. This note shows that such interpretations generally fail under heterogeneous treatment effects. In...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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 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...