Empirical Application Research Articles

Bubbles and crashes: A tale of quantiles

Periodically collapsing bubbles, if they exist, induce asymmetric dynamics in asset prices. In this article, I show that unit root quantile autoregressive models can approximate such dynamics by allowing the largest autoregressive root to take values below unity at low quantiles, which correspond to price crashes, and above unity at upper quantiles, that correspond to bubble expansions. On this basis, I employ two unit root tests based on quantile autoregressions to detect bubbles. Monte Carlo simulations suggest that the two tests have good size and power properties, and can outperform recursive least‐squares‐based tests. The merits of the two tests are further illustrated in three empirical applications that examine Bitcoin, US equity and US housing markets. In the empirical applications, special attention is given to the issue of controlling for economic fundamentals. The estimation results indicate the presence of asymmetric dynamics that closely match those of the simulated bubble processes.

LASSO-type instrumental variable selection methods with an application to Mendelian randomization.

Valid instrumental variables (IVs) must not directly impact the outcome variable and must also be uncorrelated with nonmeasured variables. However, in practice, IVs are likely to be invalid. The existing methods can lead to large bias relative to standard errors in situations with many weak and invalid instruments. In this paper, we derive a LASSO procedure for the k-class IV estimation methods in the linear IV model. In addition, we propose the jackknife IV method by using LASSO to address the problem of many weak invalid instruments in the case of heteroscedastic data. The proposed methods are robust for estimating causal effects in the presence of many invalid and valid instruments, with theoretical assurances of their execution. In addition, two-step numerical algorithms are developed for the estimation of causal effects. The performance of the proposed estimators is demonstrated via Monte Carlo simulations as well as an empirical application. We use Mendelian randomization as an application, wherein we estimate the causal effect of body mass index on the health-related quality of life index using single nucleotide polymorphisms as instruments for body mass index.

Network analysis: An overview for mental health research.

Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time-varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross-sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research.

Evaluating directional scale elasticity in the RAM model with undesirable outputs: an application in the horticultural industry

This paper advances the concept of scale elasticity by examining the impact of directional input changes on both desirable and undesirable outputs. We integrate two disposability concepts into a unified measure within the Range Adjusted Measure (RAM) model, enabling the computation of both directional scale elasticity and directional scale damage. This approach allows for a comprehensive analysis of scale effects in the presence of undesirable outputs. Grounded in Data Envelopment Analysis (DEA), our research enhances the understanding of managing undesirable outputs while accounting for directional scale changes, offering significant implications for both performance improvement and environmental sustainability. The paper includes theoretical foundations, numerical examples to illustrate the methodologies, and an empirical application in the Iranian horticulture industry, demonstrating the effectiveness and adaptability of our approach.

On periodic $$\log GARCH$$ model with empirical application

On periodic $$\log GARCH$$ model with empirical application

A Markov regime-switching event response model: beef price spread response to processing capacity shocks

AbstractThis research introduces a new method for event studies in time-series analysis named the Markov regime-switching event response model (MS-ERM). The MS-ERM is a comprehensive approach that integrates two different event study approaches: 1) measuring the impact of an event through structural shift and 2) measuring the impact via additional distributional components. As an empirical application, the study measures the impact of beef-packing plant closures on the weekly live-to-cutout beef price spread. The results indicate that the MS-ERM is a promising tool for event studies, particularly when an empirical dataset has both groups of events that cause and do not cause structural changes.

Random Forest estimation of the ordered choice model

AbstractIn this paper we develop a new machine learning estimator for ordered choice models based on the Random Forest. The proposed Ordered Forest flexibly estimates the conditional choice probabilities while taking the ordering information explicitly into account. In addition to common machine learning estimators, it enables the estimation of marginal effects as well as conducting inference and thus provides the same output as classical econometric estimators. An extensive simulation study reveals a good predictive performance, particularly in settings with nonlinearities and high correlation among covariates. An empirical application contrasts the estimation of marginal effects and their standard errors with an Ordered Logit model. A software implementation of the Ordered Forest is provided both in and in the package available on and , respectively.

Technical and environmental inefficiency measurement in agriculture using a flexible by‐production stochastic frontier model

AbstractIn light of the urgent need for farms to mitigate greenhouse gas emissions while maintaining economic viability, this paper analyses technical and environmental inefficiencies and their determinants based on a flexible multi‐equation by‐production stochastic frontier model, which accounts for the stochastic dependence between good and bad outputs via a copula function. An empirical application to Dutch dairy farms illustrates the distortions in the inefficiency scores and in the estimates of their determinants that occur when the dependence between good and bad outputs is ignored. The empirical results indicate a strong positive dependence between the good output (milk) and the bad output (methane emissions), which is particularly pronounced in the upper tail of the distribution. Notably, farms exhibit high efficiency in maximising their good output and minimising their bad output. Subsidies are negatively related to good output inefficiency but positively related to bad output inefficiency, while stock density exhibits a negative association with inefficiencies in both outputs. Disregarding output dependence leads to distortions in inefficiency estimates, particularly affecting the estimates for their determinants.

Bonferroni‐Type Tests for Return Predictability With Possibly Trending Predictors

ABSTRACTThe Bonferroni test is widely used in empirical studies investigating predictability in asset returns by strongly persistent and endogenous predictors. Its formulation, however, only allows for a constant mean in the predictor, seemingly at odds with many of the predictors used in practice. We establish the asymptotic size and local power properties of the test, and the corresponding Bonferroni ‐test, under a local‐to‐zero specification for a linear trend in the predictor, revealing that size and power depend on the magnitude of the trend for both. To rectify this, we develop with‐trend variants of the operational Bonferroni and tests. However, where a trend is not present in the predictor, we show that these tests lose (both finite sample and asymptotic local) power relative to the extant constant‐only versions of the tests. In practice, uncertainty will necessarily exist over whether a linear trend is genuinely present in the predictor or not. To deal with this, we also develop hybrid tests based on union‐of‐rejections and switching mechanisms to capitalise on the relative power advantages of the constant‐only tests when a trend is absent (or very weak) and the with‐trend tests otherwise. A further extension allows the use of a conventional ‐test where the predictor appears to be weakly persistent. We show that, overall, our recommended hybrid test can offer excellent size and power properties regardless of whether or not a linear trend is present in the predictor, or the predictor's degrees of persistence and endogeneity. An empirical application illustrates the practical relevance of our new approach.JEL Classifications: C22, C12, G14

When Diplomats Become Leaders

Abstract Challenging assumptions about diplomacy as a self-effacing practice of collective governance, this article searches for leadership in a system that conventionally leaves no room for it. Drawing on interviews and a Leadership Trait Analysis (lta), it advances an original conceptual framework for identifying and analysing diplomatic leadership in crisis. The cases of Ukraine’s current Ambassador to Germany and his predecessor illustrate the empirical application. The study finds that, under conditions of disrupted routine and increasing public visibility, not only political figures, but also diplomats posted abroad are able to practice a type of leadership. It concludes that eventually personality traits decide whether or not leadership is exercised. Expanding the sparse knowledge about psychological variables in diplomacy, my work finally calls for a more thorough exploration of the synthesis between research on leadership and diplomacy.

Estimating average treatment effects with only discrete covariates

AbstractWe propose a kernel smoothing method to estimate average treatment effects (ATE) when covariates are all discrete variables. This approach is based on Ouyang et al. (2009), who developed a Econometric Theory, 2009;25: 1–42 who developed least squares cross‐validation (CV) kernel smoothing method dealing with discrete covariates. This method automatically smoothes the irrelevant variables out of the regression model with a high probability, and avoids the problem of loss of efficiency related to the traditional nonparametric frequency‐based method. We derive the asymptotic distribution of our proposed ATE estimator. Monte Carlo simulations and empirical applications showcase the usefulness of the proposed method.

Analytic Moments of TGARCH(1,1) Models with Polynomially Adjusted Densities

Abstract This article extends He, Silvennoinen, and Teräsvirta (2008, J Finan Econ, 6, 208–230) and Francq and Zakoïan (2010, GARCH Models) by providing analytical expressions for the moments of the unconditional distribution of the TGARCH(1,1) under alternative specifications for the conditional mean and different skewed distributions for the innovations. We consider polynomially adjusted (PA) densities, such as the PA Logistic, PA hyperbolic secant, and the PA Gaussian, along with the skewed Student-t. Our results show that (i) the main driver of the skewness of the TGARCH(1,1) is the skewness of the innovations, while the excess kurtosis has a comparatively lesser impact. However, both skewness and kurtosis of the innovations significantly affect the TGARCH(1,1) kurtosis; (ii) if the conditional mean is not constant, returns can be asymmetric even if innovations are symmetric; (iii) skewed innovations can generate cross-correlations different from zero, indicating leverage effect, even when the volatility model is symmetric. Finally, we illustrate our theoretical results with an empirical application to stock indices.

Investigating heterogeneity in IRTree models for multiple response processes with score-based partitioning.

Item response tree (IRTree) models form a family of psychometric models that allow researchers to control for multiple response processes, such as different sorts of response styles, in the measurement of latent traits. While IRTree models can capture quantitative individual differences in both the latent traits of interest and the use of response categories, they maintain the basic assumption that the nature and weighting of latent response processes are homogeneous across the entire population of respondents. In the present research, we therefore propose a novel approach for detecting heterogeneity in the parameters of IRTree models across subgroups that engage in different response behavior. The approach uses score-based tests to reveal violations of parameter heterogeneity along extraneous person covariates, and it can be employed as a model-based partitioning algorithm to identify sources of differences in the strength of trait-based responding or other response processes. Simulation studies demonstrate generally accurate Type I error rates and sufficient power for metric, ordinal, and categorical person covariates and for different types of test statistics, with the potential to differentiate between different types of parameter heterogeneity. An empirical application illustrates the use of score-based partitioning in the analysis of latent response processes with real data.

Securing the Future of the Humanities

Abstract A decade ago, the American Academy of Arts and Sciences published The Heart of the Matter report to much acclaim. But what has been the impact of such a high-minded report? In the decade since its publication, we have seen a prevailing anti-humanities rhetoric with significant consequences to the security and persistence of humanistic principles. This article focuses on what many consider to be the most crucial problem of our time, climate change and its consequences, in thinking about how this overwhelming problem offers a rallying point for the insertion of the humanities into practical solutions which require an upending of discrete disciplinary perspectives as well as a bridging of the academic and public divide so that any space between the practice of the humanities and advocacy for social and environmental justice is vastly diminished. It argues for a thorough review of academic reward systems, for a broadening of scholarly definitions, and for a pedagogical focus that demands theory commit to empirical application. Finally, it suggests that we reengage our storytelling prowess with an emphasis on the power of metaphor in order to bolster imaginative response and methodological flexibility that is both cogent and compelling.

Some Similarity Measures on Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert Sets and Their Applications in Medical Diagnosis

In recent years, the application of fuzzy sets has gained significant attraction in various fields, including medical diagnosis, due to their ability to manage uncertainties and imprecise information. This paper focuses on the comparative analysis of similarity measures within the realm of Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert Sets (GIVIFSESs) and explores their application in the domain of medical diagnosis. Most of the important topics in fuzzy set theory are the similarity measures between the generalizations of fuzzy set theory. Similarity measures are a crucial tool which was used in data science. In this process, we measure how much the data sets are related and comparable. Measures of similarity give a numerical value that reveals the strength of associations between sets or sets of variables. In this paper, we initiate a new concept of generalized interval-valued intuitionistic fuzzy soft expert sets and their fundamental operations. This new concept is more flexible than existing concepts based on their algebraic definition. Unlike fuzzy sets, the concept of generalized interval-valued intuitionistic fuzzy soft expert sets is characterized by a degree of membership and degree of non-membership along with fuzzy set theory. The proposed methodology is validated through an empirical application in medical diagnosis, where (GIV-IFSESs) are employed to model the uncertainty and imprecision inherent in expert assessments. The selected similarity measures are then applied to quantify the degree of resemblance between different medical cases, facilitating a more informed decision-making process. We introduce several types of similarity measures on generalized interval-valued intuitionistic fuzzy soft expert sets. We also discuss a similarity measure of Type-I, Type-II, and Type-III for two (GIVIFSESs) and its application in medical diagnosis problems.

Multivariate probabilistic forecasting of electricity prices with trading applications

This study extends recently introduced neural networks approach, based on a regularized distributional multilayer perceptron (DMLP) technique for a multivariate case electricity price forecasting. The performance of a fully connected architecture and a LSTM architecture of neural networks are tested. Different from previous studies we incorporate dependence between multiple exchanges (EPEX and Nord Pool). The empirical data application analyzes two auctions in the day-ahead electricity market for the United Kingdom market. Along with statistical evaluation of probabilistic forecasts we develop a flexible bidding strategy based on risk-adjusted investor utility function. The trading application leverages the differences of the two exchanges by having long/short positions in both. Our findings demonstrate while DMLP shows similar performance compared to the benchmarks, the algorithm is considerably less computationally costly. LASSO Quantile Regression is better in terms if statistical evaluation of distributional fit, while DMLP outperforms in terms of Sharpe ration (by 18%) in the trading application.

Resilience compass navigation through manufacturing organization uncertainty – A dynamic capabilities approach using mixed methods

In uncertain manufacturing environments, tools that help companies and supply chains navigate unexpected events and promote sustainability are crucial. However, the application of resilience in manufacturing organizations is limited, often focusing on supply chain resilience. This study used mixed methods to minimize subjectivity in manufacturing resilience. A quantitative Content Validity Index (CVI) helped develop the 'resilience compass' assessment tool, while empirical applications in six manufacturing organizations demonstrated its usefulness. The study identifies relationships between 54 resilience practices, 11 dynamic capabilities, and three stages: anticipation, coping, and adaptation, providing decision-makers with actionable insights to enhance manufacturing resilience.

Enhanced Cross-Entropy Technique for Triangular Fuzzy Neutrosophic Multiple-Attribute Decision-Making and Applications to Quality Evaluation of Urban-Park Environmental Design

The evaluation of urban park environmental design quality is essential for ensuring that parks meet community needs and environmental sustainability. By comprehensively considering indicators such as functionality, aesthetics, sustainability, and user experience, this evaluation helps designers optimize spatial layouts and enhance the efficiency and aesthetic value of parks. Additionally, good environmental design promotes ecological protection, improves the quality of life for residents, and adds more green spaces to the city, making parks an integral part of daily urban life. The evaluation of urban park environmental design quality is framed as a multiple-attribute decision-making (MADM) problem. This study introduces the triangular fuzzy neutrosophic number cross-entropy (TFNN-CE) method, which integrates traditional cross-entropy with triangular fuzzy neutrosophic sets (TFNSs). Subsequently, the TFNN-CE technique for MADM is established. A numerical example illustrating the evaluation of urban park environmental design quality is provided, alongside several comparative analyses to highlight the advantages of the proposed approach. The key contributions of this research are as follows: (1) the TFNN-CE technique is introduced through the integration of cross-entropy and TFNSs; (2) the TFNN-CE technique is formulated to address MADM problems using TFNSs; (3) an empirical application for assessing urban park environmental design quality is presented to validate the proposed methodology; (4) comparative studies are conducted to substantiate the rationality of the TFNN-CE technique.

Identification of Average Marginal Effects in Fixed Effects Dynamic Discrete Choice Models

Abstract In nonlinear panel data models, fixed-effects methods are often criticized because they cannot identify average marginal effects (AMEs) in short panels. In contrast with that criticism, we prove the point identification of different AMEs, including causal effects of changes in the lagged dependent variable or the last choice's duration, in a panel dynamic logit model for T as small as three. Our proofs are constructive and provide simple closed-form expressions for the AMEs in terms of probabilities of choice histories. We illustrate our results using Monte Carlo experiments and with an empirical application of a dynamic model of consumer brand choice.

EXPRESS: Addressing Endogeneity Using a Two-stage Copula Generated Regressor Approach

The ubiquitous presence of endogenous regressors presents a significant challenge when drawing causal inference using observational data. The classical econometric method used to handle regressor endogeneity requires IVs that must satisfy the stringent condition of exclusion restriction, rendering it unfeasible in many settings. Herein, we propose a new IV-free method that uses copulas to address the endogeneity problem. Existing copula correction methods require nonnormal endogenous regressors: normally or nearly normally distributed endogenous regressors cause model non-identification or significant finite-sample bias. Furthermore, existing copula control function methods presume the independence of exogenous regressors and the copula control function. While maintaining the Gaussian copula regressor-error dependence structure, our generalized two-stage copula endogeneity correction (2sCOPE) method simultaneously relaxes the two key identification requirements. Under the Gaussian copula dependence structure, we prove that 2sCOPE yields consistent causal-effect estimates with correlated endogenous and exogenous regressors as well as normally distributed endogenous regressors. In addition to relaxing the identification requirements, 2sCOPE has superior finite-sample performance and addresses the significant finite-sample bias problem due to insufficient regressor nonnormality. Moreover, 2sCOPE employs generated regressors derived from existing regressors to control for endogeneity, and can thus considerably increase the ease and broaden the applicability of IV-free methods for handling regressor endogeneity. We further demonstrate 2sCOPE’s performance using simulation studies and illustrate its use in an empirical application.