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Using data on the NBER working paper series, we show that the dissemination of economics research suffers from a congestion problem: An increase in the number of weekly released working papers on average reduces downloads, abstract views, and media attention for each paper. Subsequent publishing and citation outcomes are harmed as well. Papers written by prominent authors are not immune to this congestion effect. Finally, suggestive evidence on viewership and downloads implies that working papers substitute for the dissemination function of publication. Our results highlight how readers face time and cognitive constraints, with increased congestion in working papers leading to real impacts on how research is consumed.

The escalating consumption of LPG in Indonesia has prompted the country's National Oil & Gas Company to embark on constructing a new LPG Facility. This paper addresses the imperative to streamline the Front-End Loading (FEL) stage to prevent cost and schedule overruns due to scope creep. The primary objective is to formulate a comprehensive Work Breakdown Structure (WBS) and Cost Breakdown Structure (CBS) for the LPG Facility Construction project, aimed at facilitating accurate cost estimation and scheduling processes for diverse stakeholders. Using the unfolded tesseract theory to analyze stakeholder needs and elucidate the object-oriented WBS, a Multi-Attribute Decision Making (MADM) analysis was conducted. The study concludes that a multidimensional WBS, particularly employing OmniClass, is the optimal choice for addressing the complexities of the LPG Facility Project and meeting stakeholder needs within BIM software. [ABSTRACT FROM AUTHOR]

This document is an editorial introduction to a special issue of Studies in Nonlinear Dynamics & Econometrics that honors the career of Herman van Dijk in Bayesian econometrics. The special issue includes 11 contributed papers on various topics in Bayesian econometrics, all of which are influenced by Herman's research themes of Bayesian computation, identification, model combination, nonlinearity, and non-stationarity. The papers cover a range of methodological advancements and empirical insights in the field. The editorial also expresses gratitude to Bruce Mizrach, the former editor, and Jeremy Piger, the new editor, for their contributions to the journal. [Extracted from the article]

This paper examines the little-known connection between econometrics and accounting invoked by Paweł Ciompa, who first introduced the term econometrics in 1910. Since then, research in accounting and in statistical (econometric) analysis has developed in parallel. It is argued that contemporary accounting research is methodologically closer to econometrics than ever before. This paper concentrates on the accounting origins of econometrics and on the econometric methodologies currently in use in accounting research, beginning with Paweł Ciompa's introduction of the term econometrics in accounting. The major contribution of this paper is a review of the occurrence of econometric methods in five leading journals in accounting research. The author identified 246 papers, and these were examined regarding the use of econometric methods. Two-thirds of the papers used methodologies that belong to econometrics—specifically, to financial microeconometrics. The most common methods were panel data models, qualitative variables models, and causality models. [ABSTRACT FROM AUTHOR]

This paper shows a systematic literature review using HistCiteTM in the context of factor market misallocation in China. Four key research streams are identified: government intervention; misallocation explanation; market distortion and regional factor distortion. Further, the paper identifies three emerging research streams: endogeneity; legislation relating to factors of production; and knowledge management. Factor markets misallocation has been a lasting problem since the market‐oriented reform implemented by the Chinese Government. The academic literature review mainly focuses on the misallocation under Chinese national situation. [ABSTRACT FROM AUTHOR]

As reiterated for a long, mainstream economics mainly relies upon a set of unrealistic axioms and deductive assumptions, which, nevertheless, are what underpins its core postulates. They are unnecessarily unrealistic assumptions, mainly because the empirical evidence for contrasting them is as overwhelming as old. Even the claim for (returning to) realism in economics is already old within part of the profession. However, most mainstream academics tend to consider that their research works have in fact empirical contents insofar as they usually start by considering simplified cases, situations, or descriptions that allude to some real economic facet; this, in the way of specificities to be introduced into the standard General Equilibrium model. This seeming paradox is discussed in this paper, and as a result, it is argued that there do is a predominance of a research programme in mainstream orthodox economics (modern neoclassical "economic theory," plus "econometrics") that is mainly characterized, more than by micro-empirical precisions, by a sort of abstract micro empiricism. It is also argued that the outcomes brought about by such a "mode of research production" (contributions) do not appear significantly relevant in terms of the quantum of knowledge provided about the functioning and problems of our economies. [ABSTRACT FROM AUTHOR]

Purpose: While much of the literature testing for shirking by professional athletes have used performance metrics, some works have quantified shirking in dollar terms by comparing salary to estimated marginal revenue product (MRP). However, Ordinary Least Squares (OLS) approaches to measuring shirking by comparing salary to MRP have an endogeneity problem, as salary and contract length are determined simultaneously. We test for shirking in Major League Baseball (MLB) using an MRP approach, addressing this potential endogeneity. Design/methodology/approach: This paper uses instrumental variables regression to address potential endogeneity using MLB season-level player and team data from 2010 to 2017. Findings: Using OLS regression, the impact of an additional year of guaranteed contract on shirking is estimated at approximately $1m in 2010 US dollars, and the impact of having a long-term contract is estimated at $5m, estimates comparable to those in the literature. Using instrumental variables regression, these impacts increase to $1.6m and over $9m in 2010 dollars. Practical implications: Given large, causal shirking estimates, profit maximizing sports organizations should take caution when negotiating long-term contracts. These findings also have important implications for other labor market settings where workers feel job security. Originality/value: To our knowledge, this is the first work testing for shirking in sports using an MRP approach which uses instrumental variables regression to address potential endogeneity. [ABSTRACT FROM AUTHOR]

This paper aims to elaborate a treated data set and apply the boosting methodology to monthly Brazilian macroeconomic variables to check its predictability. The forecasting performed here consists in using linear and nonlinear base-learners, as well as a third type of model that has both linear and nonlinear components in the estimation of the variables using the history itself with lag up to 12 periods. We want to investigate which models and for which forecast horizons we have the strongest performance. The results obtained here through different evaluation approaches point out that, on average, the performance of boosting models using P-Splines as base-learner are the ones that have the best results, especially the methodology with two components: two-stage boosting. In addition, we conducted an analysis on a subgroup of variables with data available until 2022 to verify the validity of our conclusions. We also compared the performance of boosted trees with other models and evaluated model parameters using both cross-validation and Akaike Information Criteria in order to check the robustness of the results. [ABSTRACT FROM AUTHOR]

This paper conducts a brief survey of spatial unit roots within the context of spatial econometrics. We summarize important concepts and assumptions in this area and study the parameter space of the spatial autoregressive coefficient, which leads to the idea of spatial unit roots. Like the case in time series, the spatial unit roots lead to spurious regression because the system cannot achieve equilibrium. This phenomenon undermines the power of the usual Ordinary Least Squares (OLS) method, so various estimation methods such as Quasi-maximum Likelihood Estimate (QMLE), Two Stage Least Squares (2SLS), and Generalized Spatial Two Stage Least Squares (GS2SLS) are explored. This paper considers the assumptions needed to guarantee the identification and asymptotic properties of these methods. Because of the potential damage of spatial unit roots, we study some test procedures to detect them. Lastly, we offer insights into how to relax the compactness assumption to avoid spatial unit roots, as well as the relationship between spatial unit roots and other models, such as the Spatial Dynamic Panel Data (SDPD) model and Lévy–Brownian motion. [ABSTRACT FROM AUTHOR]

This paper contributes to the social science literature when analyzing survey or time series data social scientists use spurious regression without due consideration of its assumptions and the data structure. This results in misinterpretation and misleading conclusions about the population. The paper reviews basic statistical and econometrics literature which led to the development of modern time series analysis in the presence of spurious regression. It concludes that the term 'Spurious' was well known before the Granger and Yule's work in time series context rather than cross-sectional data. The same reasons can produce spurious regression today and surely the solution doesn't exist in the cointegration analysis. Social scientists and applied econometrician investigators need more serious thinking and care to avoid spurious regression, if it is necessary even if data is stationary or cross-sectional. In this study, we extended the Ghouse experiment which is based on simulated data by employing real-world data to assess the effectiveness of the newly proposed Ghouse Equation in comparison to conventional approaches. The findings demonstrate that the Ghouse Equation produces the lowest probability of spurious regression as compared to its counterparts. Moreover, in forecasting performance, Ghouse Equation outperformed its counterparts. These results highlight the Ghouse Equation as a valuable and better tool for econometric analysis for nonstationary time series. [ABSTRACT FROM AUTHOR]

In this paper, we utilize the Chicago Board Option Exchange (CBOE) implied volatility indices to estimate the time-frequency information transmission among financial markets from 01.08.2008 to 31.10.2019. In doing so, we utilize the rolling window wavelet correlation (RWWC), Diebold & Yilmaz (The Economic Journal 119: 158–171, 2012), and Barunik & Krehlik (Journal of Financial Econometrics 16: 271–296, 2018). Our empirical findings suggest short-term and long-term dynamic connectedness between implied volatility indices of alternative assets. The long-term analysis findings suggest potential hedging and diversification opportunities that can be exploited by taking offsetting positions across volatility indices. The findings confirm heterogeneity between short-term and long-term connectedness results. Our findings also show superior out of sample hedging effectiveness of GVZ. The implications of the findings are further discussed in the paper. [ABSTRACT FROM AUTHOR]

This paper investigates how labour market regulations alter the adverse impact of rising import competition from China in European local labour markets between 1997 and 2006. The paper constructs measures of regional exposure to Chinese imports based on previous literature and on regional labour market frictions exploiting involuntary labour reallocations. Taking into account the endogeneity of import competition and its interaction with labour market regulations, the paper finds that regions more exposed to the rise of China have suffered from a reduction in manufacturing employment shares. This shock grows larger with regional labour market frictions; hence, it exacerbates the impact of trade shock on employment. Moreover, the paper finds that employment in public services, and not in construction or private services sector, absorbed the negative shock to the manufacturing sector. The unemployment rate, the labour force participation rate and wages in all sectors are unresponsive to import competition from China. [ABSTRACT FROM AUTHOR]

This paper compares two estimators—the Border Approach and an Instrumental Variable (IV) estimator—using a unified framework where identifying variation arises from "preference externalities," following the intuition in Waldfogel (2003). We highlight two dimensions in favor of the IV approach. First, an econometric model of the data-generating process reveals that the border approach requires a set of identification assumptions that are not easily satisfied in practice: the ignorance of some payoff-relevant information and conflicting spatial correlation assumptions. The IV approach, in contrast, exhibits greater internal validity because it is derived from the model that generates the data. Second, the border approach suffers from representative issues when the true effect sizes are different between border and off-border regions. We use a common political advertising example to evaluate these estimators and suggest ways to evaluate or limit the above concerns, such as excluding localities that are a large share of the policy making region and evaluating spatial correlations of observables. We find the border approach's representative issue to be substantial when the ignorance assumption is most plausible and observe that spatial correlations do not reflect those needed in the unobservables for consistency of the estimator. The IV, in contrast, does not exhibit concerns related to local average treatment effects. We also derive the specific conditions when the border approach can reduce bias relative to OLS. This paper was accepted by Raphael Thomadsen, marketing. Funding: This work was supported by National Natural Science Foundation of China (NSFC) [72072004, 72131001]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4977. [ABSTRACT FROM AUTHOR]

This paper introduces a panel threshold model with covariate-dependent and time-varying thresholds (PTCT), which extends the classical panel threshold model of Hansen, B. E. 1999. "Threshold Effects in Non-dynamic Panels: Estimation, Testing, and Inference." Journal of Econometrics 93: 345–68 to a framework with multiple covariate-dependent and time-varying thresholds. Based on the within-group transformation and Markov chain Monte Carlo (MCMC) technique, we develop methods for estimation and inference for threshold parameters in the proposed panel threshold model. We also suggest test statistics for threshold effect, threshold constancy, and for determining the number of thresholds. Monte Carlo simulations indicate that the estimation, inference and testing procedures work well in finite samples. Empirically, using the same data as in Hansen, B. E. 1999. "Threshold Effects in Non-dynamic Panels: Estimation, Testing, and Inference." Journal of Econometrics 93: 345–68 we revisit the cash flow/investment relationship and find quite different results. [ABSTRACT FROM AUTHOR]

Investments in the architectural, engineering, and construction (AEC) sector can be used by governments to introduce desired changes in the economy, which gives an indication of the future trends of the economy. In addition, the stock market—as reflected by the Standard and Poor 500 (S&P 500)—creates a sense of confidence about the direction of the US economy. Many studies have shown that stock prices in multiple international industries could be considered as leading economic indicators. Nevertheless, this was not researched within the AEC sector in the United States. This paper addresses this knowledge gap by investigating whether the stock prices of AEC companies in the United States could be used to inform strategic, proactive decision making. First, time series data were collected for the stock prices of 16 large AEC companies as well as for the S&P 500 index. Second, multivariate time series analysis was conducted to examine the type of relationship between the response variable—S&P 500—and all other input variables. Two econometrics models were developed: a linear, statistical vector autoregression model and a computational, deep learning model based on long–short-term memory (LSTM) networks. Third, the two developed econometrics models were tested on unseen data. Fourth, the best econometrics model was verified using another leading economic indicator which is the Purchasing Managers' Index. The results reflected that the computational LSTM model outperformed the linear statistical model. The findings showed that the stock prices of AEC companies could be considered as an additional and complementing leading economic indicator to the American Institute of Architects' Architecture Billings Index to provide a broader perspective of the business ecosystem. This paper adds to the body of knowledge by providing an econometrics model for helping in optimal investment opportunities, taking informed strategic management and business judgments, and predicting changes in the economy. [ABSTRACT FROM AUTHOR]

In this paper, we propose to analyze multiple cross-correlation between six stock market indexes (Dow Jones Industrial Average, Nasdaq Composite, DAX Performance, FTSE 100, S&P/TSX Composite and Nikkei 225) based on the multiple detrended cross-correlation coefficient, DMC x 2 . Due to its importance and long existence, we will have as the base index, acting as dependent variable, the Dow Jones Industrial Average, which will be compared with the other five indexes in all possible combinations. In addition to calculating the DMC x 2 for all combinations with Dow Jones, we will also infer the relative strength of each index in relation to the whole, and this will be done here through an unprecedented map of relative strength. With this map it will be possible to verify that the Nikkei 225 index has less weight for all time-scales and also all combinations, and that there is better distribution in the Nasdaq and DAX combination. Finally, we present more possibilities for this application throughout the paper. [ABSTRACT FROM AUTHOR]

This document is an introduction to a special issue of the journal SERIEs, which is dedicated to honoring the influential economist Manuel Arellano. The special issue follows a conference held in Madrid in 2022, where presentations were given by past and present co-authors of Arellano. Arellano, a renowned econometrician, has made significant contributions to panel data econometrics and empirical microeconomics. The special issue includes a collection of research articles that showcase the breadth and impact of Arellano's work. The document also expresses gratitude to those involved in organizing the conference and the special issue. [Extracted from the article]

A mature developed urban area like London has various policy tools to trigger economic development and leverage poverty. The scale of economic inequality in London has called for more effective policy tools to leverage poverty and close the gaps of economic inequality. Opportunity Areas is one of the tools in urban regeneration to alleviate poverty and develop communities. After over a decade of the policy rollout we have little knowledge how effective this type of policy tool is. The project investigates how effective opportunity areas for economic growth and societal development in London. The key economic indicators including unemployment rate and house price were assessed at MSOA/ward levels in London to understand their spatial variation through GIS mapping. Subsequently, socio-economic factors of commercial land use, culture infrastructure and public transport accessibility were discussed and selected to explore whether they have impacts on the difference of economic performance within opportunity areas. The spatial impacts of these factors among wards/MSOA were evaluated by Geographically Weighted Regression. The research discussed these factors' variations in Lee Valley, Park Royal and Croydon. Finally, this paper argued that local geographical context has limitations for small area growth corresponding to general policy and strategic management plan. [ABSTRACT FROM AUTHOR]

Two linear algebra problems implore a solution to them, creating the themes pursued in this paper. The first problem interfaces with graph theory via binary 0-1 adjacency matrices and their Laplacian counterparts. More contemporary spatial statistics/econometrics applications motivate the second problem, which embodies approximating the eigenvalues of massively large versions of these two aforementioned matrices. The proposed solutions outlined in this paper essentially are a reformulated multiple linear regression analysis for the first problem and a matrix inertia refinement adapted to existing work for the second problem. [ABSTRACT FROM AUTHOR]

The paper has two main objectives: one, to investigate if inter-bank horizontal dispersion in the remuneration of executives has any significant influence on bank performance. And two, to explore various situations with respect to board quality, agency cost, and government ownership under which the dispersion-performance nexus is moderated. The paper employs a two-step system GMM of (Blundell and Bond, Journal of Econometrics 8:115–143, 1998) to account for the possible endogeneity issues, and Driscoll–Kraay's non-parametric matrix estimator to account for cross-sectional dependence. The findings show that horizontal pay dispersion has a negative influence on bank performance. It produces evidence in favor of the "equity fairness theory" that higher pay dispersion would incentivize them to engage in excessive risk-taking for maximizing their personal interests, which emerges to be detrimental to the bank's performance. Further evidence of moderating effect shows that in the presence of effective board oversight, pay dispersion exerts a favorable influence on the performance of Indian banking firms. The research has significant policy ramifications for regulators to apprehend the undesirable implications of pay dispersion on bank performance and initiate quick action to scrutinize the governance practices of higher-paying banks. [ABSTRACT FROM AUTHOR]

This paper forecasts the energy efficiency coefficients at the Polish province level (NUT-2), based on imperfect and contradictory knowledge. On the one hand, we have information on the aggregated national energy efficiency coefficients in the industrial, transport, household, and service sectors. On the other hand, we also have information on the energy intensity at the Polish province level. Since the two samples are of different natures and known with uncertainty, we are obviously dealing with an inverse stochastic problem whose solution requires particular statistical devices. The applied technique of non-extensive cross-entropy econometrics generalizes the Shannon-Kullback-Leibler approach based on the Gaussian assumptions. Its justification is explained throughout this paper from both methodological and empirical points of view. The model forecasts lead to the high-value energy efficiency estimates from quasi-unstructured sets of information. This constitutes the main contribution of this research. These outputs should provide energy policy units with valuable new devices for the optimization of the energy management processes on a disaggregated local level where, by contrast, different agents and households act decisively. On a global level, the proposed technique can be applied in different EU countries and elsewhere, in the context of experimental official statistics. [ABSTRACT FROM AUTHOR]

Researchers in the field of HRM and Strategic HRM have for several decades engaged in research endeavours to identify the link between HR investments and firm performance. Despite this, the field still has some way to go to definitively demonstrate this causal relationship; partly due to the cross-sectional nature of much of this research. In the current paper, we give a brief overview of the SHRM field and the benefits that the new subfield of People Analytics (PA) might bring to the area. We also conduct a brief survey of research in Insider Econometrics, an approach used in Personnel Economics to produce empirical estimates of the value of HR practices, to highlight longitudinal intra-firm research in economics that could provide insights to research exploring the HR investment-performance link (important in both SHRM and PA fields). We discuss the value of combining the business impact perspective of People Analytics that has a unique 'inside' (intra-firm) position, with the longitudinal intra-firm analysis approaches utilized in Insider Econometrics. We suggest that People Analytics researchers, who are able to draw on the theoretical developments in HRM field, would benefit from exploring existing research insights and methodological techniques used within the field of Insider Econometrics. [ABSTRACT FROM AUTHOR]

AbstractThe goal of this paper is to recover François Divisia’s economic thought and role in the foundation of the Econometric Society. This paper relies on historiographical material and on the correspondence between Divisia and Ragnar Frisch. The paper shows that Divisia was one of the masterminds behind the idea to form a group of thinkers interested in the quantitative progress of economics. Later, as the society’s first vice-president, Divisia also took part in its consolidation. Furthermore, Divisia championed an initial standalone scientific meeting for the society, and was central to the debate regarding its membership, advocating a diplomatic, welcoming ethos. [ABSTRACT FROM AUTHOR]

This paper investigates the nexus between financial development (FD), institutions, and economic growth using a spatial autoregressive model on a panel dataset of 82 countries from 1990 to 2019. We measure the dependence between countries through geographical and institutional proximity. The latter has not been widely explored in the finance‐growth literature. The concept of institutional proximity proposes that countries with similar institutions will have similar levels of economic growth and larger spillovers once spatial effects of FD and institutional quality are controlled. Overall, our findings support this proposition, as FD and political institutions are shown to have significant positive effects on growth. However, the growth‐effect of FD becomes negative beyond a certain threshold. We also find significant positive spatial lag growth in the model, indicating the presence of indirect spillover effects of FD and institutions on the growth of neighbouring countries in both geographical and institutional spheres. Furthermore, the spatial growth model with the institutional proximity matrix exhibits a higher rate of convergence and larger size of spillover than the model with geographical proximity. These findings are robust to various model specifications, and the paper concludes with some policy recommendations. [ABSTRACT FROM AUTHOR]

Quality control charts aim at raising an alarm as soon as sequentially obtained observations of an underlying random process no longer seem to be within stochastic fluctuations prescribed by an in-control scenario. Such random processes can often be modelled using the concept of stationarity, or even independence as in most classical works. An important out-of-control scenario is the changepoint alternative, for which the distribution of the process changes at an unknown point in time. In his seminal 1954 Biometrika paper, E. S. Page introduced the famous cumulative sum control charts for changepoint monitoring. Innovatively, decision rules based on cumulative sum procedures took the full history of the process into account, whereas previous procedures were based only on a fixed and typically small number of the most recent observations. The extreme case of using only the most recent observation, often referred to as the Shewhart chart, is more akin to serial outlier than changepoint detection. Page's cumulative sum approach, introduced seven decades ago, is ubiquitous in modern changepoint analysis, and his original paper has led to a multitude of follow-up papers in different research communities. This review is focused on a particular subfield of this research, namely nonparametric sequential, or online, changepoint tests that are constructed to maintain a desired Type-1 error as opposed to the more traditional approach seeking to minimize the average run length of the procedures. Such tests have originated at the intersection of econometrics and statistics. We trace the development of these tests and highlight their properties, mostly using a simple location model for clarity of exposition, but we also review more complex situations such as regression and time series models. [ABSTRACT FROM AUTHOR]

This paper describes various selected properties and features of negative binomial (NB) random variables, with special reference to NB2 (i.e., p = 2), and some generalizations to NBp (i.e., p ≥ 2), specifications. It presents new results (e.g., the NBp moment-generating function) with regard to the relationship between a sample mean and its accompanying variance, as well as spatial statistical/econometric numerical and empirical examples, whose parameter estimators are maximum likelihood or method of moment ones. Finally, it highlights the Moran eigenvector spatial filtering methodology within the context of generalized linear modeling, demonstrating it in terms of spatial negative binomial regression. Its overall conclusion is a bolstering of important findings the literature already reports with a newly recognized empirical example of an NB3 phenomenon. [ABSTRACT FROM AUTHOR]