Empirical Application Research Articles

Estimating the productivity of US agriculture: The Fisher total factor productivity index for time series data with unknown prices

AbstractIn this paper, we propose a straightforward way to estimate the Fisher ideal total factor productivity (TFP) index (FI) in cases where price information is unavailable, using ‘shadow prices’ derived from data envelopment analysis (DEA). A Monte Carlo experiment shows that the shadow price Fisher ideal TFP index (SPFI) can effectively estimate the ‘true’ FI with relatively small (and stable) errors. The empirical application to the US agriculture sector (1948–2017) further suggests that the SPFI is a (superior) alternative to the traditional Malmquist DEA, especially in dealing with unbalanced panel or time series data when price data are unknown.

Improved estimators in bell regression model with application

In this paper, we propose the application of shrinkage strategies to estimate coefficients in the Bell regression models when prior information about the coefficients is available. The Bell regression models are well-suited for modelling count data with multiple covariates. Furthermore, we provide a detailed explanation of the asymptotic properties of the proposed estimators, including asymptotic biases and mean squared errors. To assess the performance of the estimators, we conduct numerical studies using Monte Carlo simulations and evaluate their simulated relative efficiency. The results demonstrate that the suggested estimators outperform the unrestricted estimator when prior information is taken into account. Additionally, we present an empirical application to demonstrate the practical utility of the suggested estimators.

Online learning of order flow and market impact with Bayesian change-point detection methods

Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can be identified through suitable time series models applied to market data. In this paper, we propose the use of Bayesian online change-point detection (BOCPD) methods to identify regime shifts in real-time and enable online predictions of order flow and market impact. To enhance the effectiveness of our approach, we have developed a novel BOCPD method using a score-driven approach. This method accommodates temporal correlations and time-varying parameters within each regime. Through empirical application to NASDAQ data, we have found that: (i) Our newly proposed model demonstrates superior out-of-sample predictive performance compared to existing models that assume i.i.d. behavior within each regime; (ii) When examining the residuals, our model demonstrates good specification in terms of both distributional assumptions and temporal correlations; (iii) Within a given regime, the price dynamics exhibit a concave relationship with respect to time and volume, mirroring the characteristics of actual large orders; (iv) By incorporating regime information, our model produces more accurate online predictions of order flow and market impact compared to models that do not consider regimes.

Geographic disaggregation of household surveys. Modified estimators for correcting misclassification bias

ABSTRACT We present a two-stage method to estimate spatial conditional means at a higher spatial resolution than the data actually have. In the first stage, we increase the spatial resolution of the data using classification tools and ancillary data. In the second stage, we estimate spatial conditional means (conditioning on the new spatial resolution). The estimation procedure in the second stage is not straightforward because the new finer spatial areas are subject to misclassification (measurement error). We prove that the least square (LS) estimators are biased under this framework and propose a consistent and asymptotically normal estimator under non-differential measurement errors. Given that the proposed estimator depends on unobservable terms, we also present its feasible version. Unlike most of the spatial downscaling methods, our proposal is non-model-based, and does not require area homogeneity assumptions. We assess analytical results by some Monte Carlo simulations, showing that our proposals work properly and outperform the spatial microsimulation approach. Finally, we conduct an empirical application where we analyse poverty and unemployment in one of the main urban agglomerates of Argentina known as Gran Rosario, we spatially disaggregate the original database to make inferences at a finer geographic scale.

Model-assisted sensitivity analysis for treatment effects under unmeasured confounding via regularized calibrated estimation

Abstract Consider sensitivity analysis for estimating average treatment effects under unmeasured confounding, assumed to satisfy a marginal sensitivity model. At the population level, we provide new representations for the sharp population bounds and doubly robust estimating functions. We also derive new, relaxed population bounds, depending on weighted linear outcome quantile regression. At the sample level, we develop new methods and theory for obtaining not only doubly robust point estimators for the relaxed population bounds with respect to misspecification of a propensity score model or an outcome mean regression model, but also model-assisted confidence intervals which are valid if the propensity score model is correctly specified, but the outcome quantile and mean regression models may be misspecified. The relaxed population bounds reduce to the sharp bounds if outcome quantile regression is correctly specified. For a linear outcome mean regression model, the confidence intervals are also doubly robust. Our methods involve regularized calibrated estimation, with Lasso penalties but carefully chosen loss functions, for fitting propensity score and outcome mean and quantile regression models. We present a simulation study and an empirical application to an observational study on the effects of right-heart catheterization. The proposed method is implemented in the R package RCALsa.

The Raise Regression: Justification, Properties and Application

SummaryMulticollinearity results in inflation in the variance of the ordinary least squares estimators due to the correlation between two or more independent variables (including the constant term). A widely applied solution is to estimate with penalised estimators such as the ridge estimator, which trade off some bias in the estimators to gain a reduction in the variance of these estimators. Although the variance diminishes with these procedures, all seem to indicate that the inference and goodness of fit are controversial. Alternatively, the raise regression allows mitigation of the problems associated with multicollinearity without the loss of inference or the coefficient of determination. This paper completely formalises the raise estimator. For the first time, the norm of the estimator, the behaviour of the individual and joint significance, the behaviour of the mean squared error and the coefficient of variation are analysed. We also present the generalisation of the estimation and the relation between the raise and the residualisation estimators. To have a better understanding of raise regression, previous contributions are also summarised: its mean squared error, the variance inflation factor, the condition number, adequate selection of the variable to be raised, the successive raising, and the relation between the raise and the ridge estimator. The usefulness of the raise regression as an alternative to mitigate multicollinearity is illustrated with two empirical applications.

Smoothed gradient least squares estimator for linear threshold models

In the presence of fixed threshold effects, the least squares (LS) estimator of the threshold parameter poses challenges for statistical inference due to its nonstandard limiting distribution, which also presents challenges for bootstrap methods. To address this issue, we propose a novel estimator: a two-step smoothed gradient least squares (SGLS) estimator. Our proposed method achieves a normal limiting distribution for the threshold parameter with minimal efficiency loss compared to the LS estimator. Furthermore, our modified bootstrap method significantly enhances computational efficiency, leading to improved bootstrap confidence intervals (CIs) for the threshold parameter compared to asymptotic CIs. Our method is validated through a small Monte Carlo study and demonstrated with an empirical application.

Discrete forecast reconciliation

This paper presents a formal framework and proposes algorithms to extend forecast reconciliation to discrete-valued data to extend forecast reconciliation to discrete-valued data, including low counts. A novel method is introduced based on recasting the optimisation of scoring rules as an assignment problem, which is solved using quadratic programming. The proposed framework produces coherent joint probabilistic forecasts for count hierarchical time series. Two discrete reconciliation algorithms are also proposed and compared against generalisations of the top-down and bottom-up approaches for count data. Two simulation experiments and two empirical examples are conducted to validate that the proposed reconciliation algorithms improve forecast accuracy. The empirical applications are forecasting criminal offences in Washington D.C. and product unit sales in the M5 dataset. Compared to benchmarks, the proposed framework shows superior performance in both simulations and empirical studies.

Efficiency and Incidence of Taxation with Free Entry and Love-of-Variety Preferences

We develop a theory of commodity taxation featuring imperfect competition along with love-of-variety preferences and endogenous firm entry and exit. We derive new formulas for the efficiency and pass-through of specific and ad valorem taxes. These formulas unify existing canonical ones and feature a new term capturing the effect of variety on consumer surplus. As a proof of concept, we use theoretical formulas to identify love-of-variety preferences in an empirical application. Our welfare analysis shows that the marginal excess burden is sensitive to the estimated love of variety, which overturns classical results on the desirability of ad valorem versus specific taxation. (JEL D11, D21, D91, H25)

An Intelligent Airflow Perception Model for Metal Mines Based on CNN-LSTM Architecture

In view of the harsh underground working environment and difficulties of airflow monitoring sensors placement, implementing an intelligent ventilation strategy is crucial for ensuring ventilation safety during mining process. The key issue lies in obtaining global real-time airflow parameters for ventilation safety and intelligent control. Thus, we propose an intelligent perception approach based on artificial intelligence (AI) method for acquiring airflow parameters, which operates by leveraging partial airflow data from specific monitoring points to predict airflow at undisclosed locations. Initially, this approach is facilitated by the training of an AI database through numerical simulations of ventilation networks. Subsequently, an intelligent airflow perception model is constructed, incorporating convolution neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM architectures. Through iterative updates and enhancements, these models demonstrate average deviations between predicted and actual airflow parameters of less than 5% in both simulated scenarios and empirical applications. Furthermore, in the case study, the CNN-LSTM architecture model exhibits superior performance for intelligent airflow perception. This architecture combing with airflow monitoring system, and utilizing partial real-time data inputs to obtain perception point outputs, can effectively enhance employee productivity, reduce energy consumption, and prevent resource wastage.

Reprint of: Out-of-sample tests for conditional quantile coverage: An application to Growth-at-Risk

This paper proposes tests for out-of-sample comparisons of interval forecasts based on parametric conditional quantile models. The tests rank the distance between actual and nominal conditional coverage with respect to the set of conditioning variables from all models, for a given loss function. We propose a pairwise test to compare two models for a single predictive interval. The set-up is then extended to a comparison across multiple models and/or intervals. The limiting distribution varies depending on whether models are strictly non-nested or overlapping. In the latter case, degeneracy may occur. We establish the asymptotic validity of wild bootstrap based critical values across all cases. An empirical application to Growth-at-Risk (GaR) uncovers situations in which a richer set of financial indicators are found to outperform a commonly-used benchmark model when predicting downside risk to economic activity.

Consumers’ Trust in Sharing Economy Peer-To-Peer Platforms: A Case Study of Online Food Delivery in Hanoi, Vietnam

Purpose: The purpose of this study is to the influence of trust on consumers’ transactional intention within a P2P sharing platform focusing on consumers who used OFD in Hanoi, Vietnam.   Design/methodology/approach: A structured questionnaire is developed in a Google Form and distributed mainly through web-based and telephone-based platforms. In total, 240 valid responses were collected from Hanoi. To ensure the study's reliability and representativeness while minimizing bias, a convenience sampling method is employed, considering the demographic distribution of consumers across different age groups, genders, occupations, income levels, and various online food delivery platforms, income, frequency of consumption, and average expense. Analytical techniques included descriptive statistics, Cronbach’s Alpha reliability testing, and multiple regression analysis.   Results and discussions: Findings revealed that four of the seven factors examined exerted a significant positive impact on Consumers’ Intention to Transact: "Consumer’s Trust in supplying Peer’s Integrity”, "Consumer's Trust in Products Ability”, "Consumer’s Trust in supplying Peer’s Benevolence”, and "Consumer’s Trust in Platform’s Integrity”. Conversely, while “Consumer’s Trust in supplying Peer’s Ability”, “Consumer’s Trust in Platforms Ability”, and “Consumer’s Trust in Platform’s Benevolence” do influence ‘Customers’ Intention to Transact’, their effect lacks statistical significance.   Research Implications: This research sheds light on Vietnam's Sharing Economy, showcasing its transformative impact and governmental support. It focuses on the developing OFD sector, highlighting its rapid growth and significance. By adopting a trust-based research model, the study offers insights into consumer behavior within OFD platforms, contributing to the understanding of trust dynamics.   Originality/Value: The research provides a theoretical foundation for understanding trust's role in shaping consumer behavior and transactional intentions, laying the groundwork for empirical research and practical applications. Practically, the study offers actionable insights for policymakers and industry players. It emphasizes the importance of integrity, reliability, and effective communication in building consumer trust.

A maximum-likelihood-based approach to estimate the long memory parameter using fractional spline wavelets

Fractional spline wavelets act as fractional differentiators for essentially low-pass signals, which is a useful property to analyze time series with fractal behavior. Moreover, under suitable scaling of wavelet coefficients, one can whiten a fractional Gaussian noise inputted in the wavelet channels. In this article, we demonstrate the two former statements and, by employing them, propose a method based on maximum likelihood to estimate the long-memory parameter using fractional spline wavelets. The proposed estimator exhibits competitive behavior in terms of mean squared error in the simulation studies we conducted, dominating all other methods as the sample size increases. In empirical applications, the proposed method outperformed other approaches in 10 of 12 evaluations for three different time series. Moreover, the proposed method allows for estimation in a continuous search grid, even when the Hurst coefficient lies outside of stationarity boundaries, which is a clear advantage over many algorithms: in such cases, the proposed method displays a stable behavior and an increasing outperformance margin. We also propose an identification procedure for the persistence or antipersistence of a given signal, based on the energy concentration of the coefficients of wavelet decomposition and its theoretical properties.

Semiparametric spatial autoregressive models with nonlinear endogeneity

. This article constructs nonparametric two-step least squares (2SLS) and generalized method of moments (GMM) sieve estimators to estimate a functional-coefficient spatial autoregressive model with an endogenous environment variable. We derive the consistency and asymptotic normality results for our proposed sieve estimators. A small Monte Carlo study shows that our proposed estimators exhibit good finite-sample performance. An empirical application is used to illustrate the usefulness of our methods.

Examining the Use and Application of the WHO Integrated People-Centred Health Services Framework in Research Globally - a Systematic Scoping Review.

The World Health Organisation (WHO) accepted the Integrated People-centred Health Services (IPCHS) framework in 2016 as an essential component for achieving universal health coverage in fragmented health systems. We aimed to examine the empirical applications of the WHO IPCHS framework to guide its use in strengthening health-service research. Academic databases and the IPCHS website were searched for relevant articles published between 2016 and July 2023. Two reviewers independently screened and extracted data on the study design, setting, IPCHS framework components, and facilitators and barriers to implementing the IPCHS strategies. Descriptive and content analyses were conducted. Six studies were identified using the IPCHS framework. Studies have examined a combination of the five IPCHS strategies. All studies reported building strong primary care-based systems and coordinating care for individuals. Continued relationships and trust, co-production of health programmes, diversity of health care team, and technology were major facilitators, while low health literacy, lack of primary setting capacity and healthcare workforce were principal barriers to IPCHS implementation. This scoping review offers an overview of IPCHS strategies employed in healthcare research. Generally, the IPCHS framework remains underutilised in primary research. These results offer guidance for future research to support effective healthcare delivery.

A holistic framework for the analysis of predictive rhetoric in digital visualizations

In digital culture, visualizations are a prevalent and ubiquitous form of communication. A veteran journalistic tool, and an increasingly popular one in digital politics, visualizations offer informative value, attract readership, and increase engagement. Visualizations’ multimodality enables them to convey rhetoric through informative, narrative and visual strategies, making them particularly well-suited for future-oriented discourse. Despite the rise of visualization-focused scholarly work over the past decade, several analytical lacunas remain, due to visualizations’ multimodal nature and their rich array of actors, contexts and usages in the digital world. Specifically, no scholarly approach examines forward-looking visualizations comprehensively, addressing the ways in which their rhetorical layers coalesce to broker knowledge in multimodal predictive discourse. To fill this gap, our paper proposes a holistic framework for their analysis, addressing knowledge-brokering functions, predictive components, and rhetorical strategies. Thus, we ask, ‘How are predictive visualizations rhetorically constructed to mediate the future?’ and answer through conceptualization complemented by qualitative analysis of predictive pandemic visualizations from journalistic and social media. We begin by creating a theoretically informed framework, based on existing perspectives from data-journalism studies, projection studies, and visualization scholarship, which we then refine through analytical workshops and empirical application. Our final analytical framework encapsulates each visualization’s rhetorical strategies, its knowledge-brokering functions, predictive structure, and their interrelations, highlighting the division of rhetorical and predictive labor across each visualization’s components. We conclude with an analytical epilogue in which we demonstrate the usefulness of this framework in holistically analyzing predictive multimodal rhetoric by revisiting the elusive concept of rhetorical complexity in predictive visualizations.

Bayesian estimation of large-scale simulation models with Gaussian process regression surrogates

Large scale, computationally expensive simulation models pose a particular challenge when it comes to estimating their parameters from empirical data. Most simulation models do not possess closed-form expressions for their likelihood function, requiring the use of simulation-based inference, such as simulated method of moments, indirect inference, likelihood-free inference or approximate Bayesian computation. However, given the high computational requirements of large-scale models, it is often difficult to run these estimation methods, as they require more simulated runs that can feasibly be carried out. The aim is to address the problem by providing a full Bayesian estimation framework where the true but intractable likelihood function of the simulation model is replaced by one generated by a surrogate model trained on the limited simulated data. This is provided by a Linear Model of Coregionalization, where each latent variable is a sparse variational Gaussian process, chosen for its desirable convergence and consistency properties. The effectiveness of the approach is tested using both a simulated Bayesian computing analysis on a known data generating process, and an empirical application in which the free parameters of a computationally demanding agent-based model are estimated on US macroeconomic data.

Advancing group efficiency evaluation in DEA with negative data: An empirical application in the banking industry

Data Envelopment Analysis (DEA) plays a pivotal role in assessing production unit efficiency. This study extends group efficiency assessment within the banking sector by utilizing the Modified Semi-Oriented Radial Measure (MSORM) model, specifically designed to handle negative data. It introduces two distinct efficiency definitions and develops models for their evaluation within these groups. Focusing on banks as decision-making units, the MSORM model delves into the intricacies of group efficiency. By effectively addressing negative data complexities, it enables a comprehensive evaluation of bank efficiency across various group frameworks. The study further examines the efficacy of efficiency definitions based on average and weakest performances within the MSORM framework. Empirical findings reveal significant variations in group efficiency assessment under different paradigms, highlighting the impact of the evaluation approach. This research contributes valuable insights into performance variations within the banking industry and aids in enhancing efficiency evaluations in banking systems.

Concrete freedom at work: Hegel’s normative conception of freedom and its relevance to organization studies

In organization studies, the prevailing understanding of freedom has revolved around a specific concept of autonomy. However, we propose an alternative and normative concept of freedom, drawing inspiration from the philosophy of Hegel and a reinterpretation of his ideas by Robert Brandom. We begin by diagnosing the liberal conceptions of freedom as opportunity and autonomy that shape the predominant understanding of freedom at work within organization studies. We then follow Hegel and conceive freedom as the expansive realization of oneself through the commitment to meaningful constraints. In contrast to the dominant liberal notions of freedom, Hegel allows us to understand professional commitments and communal obligations at work as the medium of freedom. We argue that concrete freedom at work requires that workers a) see their work as intrinsically valuable and b) that neither they nor their employers are unconditionally free to define norms for how work is to be organized and pursued. We then demonstrate the theoretical implications of shifting from the liberal conceptions of freedom prevalent in organization studies to a Hegelian conception of freedom. The Hegelian theory leads to a more nuanced understanding of worker decision-discretion, motivation, and the meaningfulness of work. Additionally, it has implications for research on bureaucracy and institutional logics. We end by considering the limits of the empirical applicability of our Hegelian theory, and by highlighting its normative value for practitioners and critical management scholars.

Optimal smoothing parameter selection in single-index model derivative estimation

. Single-index model is one of the most popular semiparametric models in applied econometrics. Estimation of the derivative function is often of crucial importance, as studying “marginal effects” serves as a cornerstone of microeconomics. A prerequisite for the successful application of nonparametric/semiparametric kernel estimation methods is to select smoothing parameters properly to balance the estimation squared bias and variance. Henderson et al. (2015) propose a novel method for selecting the smoothing parameters optimally for derivative function estimation. However, their method suffers from the “curse of diemnsionality” problem in a multivariate nonparametric regression model. In this article, we extend the work of Henderson et al. (2015) to estimation of the derivative function of a single-index model. Specifically, we propose a data-driven method to select smoothing parameters optimally for single-index model derivative function estimation. We also derive the asymptotic distribution of the resulting local linear estimator of the derivative function. Both simulations and empirical applications show that the proposed method works well in practice.