Sustained efforts and investments in different sectors are essential for the overall development of a region. Various studies around the globe underscore the importance of investment in road transport infrastructure in many developing countries to achieve their development targets. The...

This paper focuses on an empirical analysis of the relationship between investing and saving, taking into account various economic regions. The economic aggregates are selected following the International Monetary Fund (IMF) standard classification. The investigation is developed within the...

In emerging markets, stock markets play a crucial role in supporting long-term growth. This study explores the causal relationship between stock market development and economic growth in the Fragile Five countries-Brazil, India, Indonesia, South Africa, and Turkey-covering the period from 2001...

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...

Banks often assume that higher credit limits increase customer default risk because greater exposure appears to imply greater vulnerability. This reasoning, however, conflates correlation with causation. Whether increasing a customer's credit limit truly raises the likelihood of default remains...

Researchers typically collect experimental data sequentially, allowing early outcome observations and adaptive treatment assignment to reduce exposure to inferior treatments. This article reviews multi-armed-bandit adaptive experimental designs that balance exploration and exploitation. Because...

Researchers typically collect experimental data sequentially, allowing early outcome observations and adaptive treatment assignment to reduce exposure to inferior treatments. This article reviews multiarmed-bandit adaptive experimental designs that balance exploration and exploitation. Because...

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 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 study firms' financial and real decisions after an exogenous change in their interest rate beliefs induced by a survey experiment with an information treatment. Firms revise their expectations downward after learning about the European Central Bank's policy rate. Moreover, we find a reduction...

We investigate the long-run relationship between organized crime and corruption using data from 20 Italian regions over 30 years. As Rose-Ackerman and Palifka (2018) argue, corruption and organized crime often go together. Our study contributes to the literature in several ways in terms of...

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 paper proposes a new resilience index, CLARE (Causal machine Learning Approach to Resilience Estimation), which is rooted in an impact evaluation framework and causal machine learning algorithms applied to longitudinal household survey data. The indicator is model-agnostic, data-driven,...

Global warming remains one of the greatest threats to the sustainability of the planet. The literature is replete with studies investigating the economic effects of global warming with very little attention being paid to the financial ramifications. To this end, the present study attempts to...

We estimate monetary wage returns to academic-track education, Germany’s elite secondary school type granting university entrance. Because academic-track attendance and subsequent university education are institutionally linked, we disentangle their contributions using a causal mediation...

This study addresses a fundamental challenge in the empirical application of the Capability Approach: the measurement of the "capability set" as an opportunity set. Unlike standard utility-based measures that focus solely on achieved outcomes, measuring capability requires assessing the welfare...

Design-based econometrics, centred on average treatment effects in the potential-outcomes framework, treats causal effects as population-level expectations under hypothetical interventions and thereby offers an expectation-based, mechanism-agnostic conception of causality. This conception is...

This paper investigates the nexus between medium- and high-technology exports and electric- ity consumption from 2016M1 to 2025M05 in Turkey. Nonlinear features in the data lead the use of a Smooth Transition Regression model that shows entrepreneurial expansion which intensifies electricity...

Building on the testable implications for IV validity underlying local average treatment effect (LATE) estimation, we (i) propose a simple testing procedure that may accommodate high-dimensional covariates and (ii) demonstrate that it can also detect biases arising from misspecified IV...

When multiple instruments are available, conventional instrumental variable estimators aggregate across potentially heterogeneous margins of compliance and may yield effects that lack a clear economic interpretation. The problem is compounded when some instruments violate the exclusion...

We study the estimation of causal effects on group-level parameters identified from microdata (e.g., child penalties). We demonstrate that standard one-step methods (such as pooled OLS and IV regressions) are generally inconsistent due to an endogenous weighting bias, where the policy affects...

Instrumental variables (IV) methods are central to applied microeconomics. While classical approaches assume linear models with constant effects, recent literature has shifted toward the local average treatment effect (LATE) framework to accommodate heterogeneous treatment effects. This paper...

We apply rolling-window econometric methods, including GARCH(1,1) estimation, Bai-Perron structural break detection, CUSUM stability testing, and Granger causality analysis in bivariate VAR frameworks, to analyze the temporal dynamics of market integration in cryptocurrency perpetual futures,...

This paper examines the interaction between nominal wage dynamics and consumer price inflation in Uzbekistan, with the aim of assessing the risk of wage-price spirals in an emerging market context. Using quarterly data covering the period from 2007 to 2025, the analysis investigates whether wage...

Intensive care units (ICUs) operate with fixed capacities and face uncertainty such as demand variability, leading to demand-driven, early discharges to free up beds. These discharges can increase ICU readmission rates, negatively impacting patient outcomes and aggravating ICU bottleneck...