The Logic of Term Limits Pledges

This article offers a rationale for candidates who voluntarily pledge to term limits. My analysis is built on a standard political agency model to which I add an election campaign where candidates can commit not to seek a second term. Pledging to term limits allows candidates to signal their private type and insulate themselves from career concerns. By doing so, candidates leverage the fact that the representative voter endogenously prefers to elect a candidate who does not seek reelection because she either has on average more desirable attributes, distorts her decisions to a lesser extent, or both. As a result, candidates who pledge to term limits have a higher probability of being elected in the first place. I characterize the equilibria of a model specification in which politicians differ with respect to their policy preferences and uncover circumstances in which term limits pledges are informative and improve the voter's welfare.

Forecasting the Severity of COVID-19 Pandemic Amidst the Emerging SARS-CoV-2 Variants: Adoption of ARIMA Model

Currently, the global report of COVID-19 cases is around 110 million, and more than 2.43 million related death cases as of February 18, 2021. Viruses continuously change through mutation; hence, different virus of SARS-CoV-2 has been reported globally. The United Kingdom (UK), South Africa, Brazil, and Nigeria are the countries from which these emerged variants have been notified and now spreading globally. Therefore, these countries have been selected as a research sample for the present study. The datasets analyzed in this study spanned from March 1, 2020, to January 31, 2021, and were obtained from the World Health Organization website. The study used the Autoregressive Integrated Moving Average (ARIMA) model to forecast coronavirus incidence in the UK, South Africa, Brazil, and Nigeria. ARIMA models with minimum Akaike Information Criterion Correction (AICc) and statistically significant parameters were chosen as the best models in this research. Accordingly, for the new confirmed cases, ARIMA (3,1,14), ARIMA (0,1,11), ARIMA (1,0,10), and ARIMA (1,1,14) models were chosen for the UK, South Africa, Brazil, and Nigeria, respectively. Also, the model specification for the confirmed death cases was ARIMA (3,0,4), ARIMA (0,1,4), ARIMA (1,0,7), and ARIMA (Brown); models were selected for the UK, South Africa, Brazil, and Nigeria, respectively. The results of the ARIMA model forecasting showed that if the required measures are not taken by the respective governments and health practitioners in the days to come, the magnitude of the coronavirus pandemic is expected to increase in the study’s selected countries.

Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review

Background While many studies have consistently found incomplete reporting of regression-based prediction model studies, evidence is lacking for machine learning-based prediction model studies. We aim to systematically review the adherence of Machine Learning (ML)-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Methods We included articles reporting on development or external validation of a multivariable prediction model (either diagnostic or prognostic) developed using supervised ML for individualized predictions across all medical fields. We searched PubMed from 1 January 2018 to 31 December 2019. Data extraction was performed using the 22-item checklist for reporting of prediction model studies (www.TRIPOD-statement.org). We measured the overall adherence per article and per TRIPOD item. Results Our search identified 24,814 articles, of which 152 articles were included: 94 (61.8%) prognostic and 58 (38.2%) diagnostic prediction model studies. Overall, articles adhered to a median of 38.7% (IQR 31.0–46.4%) of TRIPOD items. No article fully adhered to complete reporting of the abstract and very few reported the flow of participants (3.9%, 95% CI 1.8 to 8.3), appropriate title (4.6%, 95% CI 2.2 to 9.2), blinding of predictors (4.6%, 95% CI 2.2 to 9.2), model specification (5.2%, 95% CI 2.4 to 10.8), and model’s predictive performance (5.9%, 95% CI 3.1 to 10.9). There was often complete reporting of source of data (98.0%, 95% CI 94.4 to 99.3) and interpretation of the results (94.7%, 95% CI 90.0 to 97.3). Conclusion Similar to prediction model studies developed using conventional regression-based techniques, the completeness of reporting is poor. Essential information to decide to use the model (i.e. model specification and its performance) is rarely reported. However, some items and sub-items of TRIPOD might be less suitable for ML-based prediction model studies and thus, TRIPOD requires extensions. Overall, there is an urgent need to improve the reporting quality and usability of research to avoid research waste. Systematic review registration PROSPERO, CRD42019161764.

Exploring Different Assumptions about Outcome-Related Risk Perceptions in Discrete Choice Experiments

Environmental outcomes are often affected by the stochastic nature of the environment and ecosystem, as well as the effectiveness of governmental policy in combination with human activities. Incorporating information about risk in discrete choice experiments has been suggested to enhance survey credibility. Although some studies have incorporated risk in the design and treated it as either the weights of the corresponding environmental outcomes or as a stand-alone factor, little research has discussed the implications of those behavioural assumptions under risk and explored individuals’ outcome-related risk perceptions in a context where environmental outcomes can be either described as improvement or deterioration. This paper investigates outcome-related risk perceptions for environmental outcomes in the gain and loss domains together and examines differences in choices about air quality changes in China using a discrete choice experiment. Results suggest that respondents consider the information of risk in both domains, and their elicited behavioural patterns are best described by direct risk aversion, which states that individuals obtain disutility directly from the increasing risk regardless of the associated environmental outcomes. We discuss the implication of our results and provide recommendations on the choice of model specification when incorporating risk.

Counterpoint

Studying and modeling theoretical concepts is a cornerstone activity in information systems (IS) research. Researchers have been familiar with one type of theoretical concept, namely behavioral concepts, which are assumed to exist in nature and measured by a set of observable variables. In this paper, we present a second type of theoretical concept, namely forged concepts, which are designed and assumed to emerge within their environment. While behavioral concepts are classically operationalized as latent variables, forged concepts are better specified as emergent variables. Additionally, we propose composite-based structural equation modeling (SEM) as a subtype of SEM that is eminently suitable to analyze models containing emergent variables. We shed light on the composite-based SEM steps: model specification, model identification, model estimation, and model assessment. Then, we present an illustrative example from the domain of IS research to demonstrate these four steps and show how modeling with emergent variables proceeds.

G-computation and doubly robust standardisation for continuous-time data: A comparison with inverse probability weighting

In time-to-event settings, g-computation and doubly robust estimators are based on discrete-time data. However, many biological processes are evolving continuously over time. In this paper, we extend the g-computation and the doubly robust standardisation procedures to a continuous-time context. We compare their performance to the well-known inverse-probability-weighting estimator for the estimation of the hazard ratio and restricted mean survival times difference, using a simulation study. Under a correct model specification, all methods are unbiased, but g-computation and the doubly robust standardisation are more efficient than inverse-probability-weighting. We also analyse two real-world datasets to illustrate the practical implementation of these approaches. We have updated the R package RISCA to facilitate the use of these methods and their dissemination.

Bankruptcy Prediction with a Doubly Stochastic Poisson Forward Intensity Model and Low-Quality Data

With the record high leverage across all segments of the (global) economy, default prediction has never been more important. The excess cash illusion created in the context of COVID-19 may disappear just as quickly as the pandemic entered our world in 2020. In this paper, instead of using any scoring device to discriminate between healthy companies and potential defaulters, we model default probability using a doubly stochastic Poisson process. Our paper is unique in that it uses a large dataset of non-public companies with low-quality reporting standards and very patchy data. We believe this is the first attempt to apply the Duffie–Duan formulation to emerging markets at such a scale. Our results are comparable, if not more robust, than those obtained for public companies in developed countries. The out-of-sample accuracy ratios range from 85% to 76%, one and three years prior to default, respectively. What we lose in (data) quality, we regain in (data) quantity; the power of our tests benefits from the size of the sample: 15,122 non-financial companies from 2007 to 2017, unique in this research area. Our results are also robust to model specification (with different macro and company-specific covariates used) and statistically significant at the 1% level.

DRAWBACKS IN THE 3-FACTOR APPROACH OF FAMA AND FRENCH (2018)

This paper features a statistical analysis of the monthly three factor Fama/French return series. Rolling OLS regressions explore the relationship between the 3 factors, using data from July 1926 to June 2018, available on French’s website. The results suggest there are significant and time-varying relationships between the factors. A sub-sample from July 1990 to July 2018 is used to analyze the three series using two-stage least squares and the Hausman test to check for issues related to endogeneity. The empirical results suggest that the factors, when combined in OLS regression analysis, as suggested by Fama and French (2018), are likely to suffer from endogeneity. Ramsey’s RESET tests suggest a nonlinear relationship exists between the three series. We use two instruments to estimate the market betas, and compare them to betas estimated not using instruments. Non-parametric tests of the two sets of betas suggest significant differences. The results suggest that using these factors in linear regression analysis, as recommended by Fama and French [(2018). Choosing factors. Journal of Financial Economics, 128(2), 234–252] is problematic in that the estimated coefficients are highly sensitive to the correct model specification.

Geotechnical Properties of Bauxite: A Case Study in Bukit Goh, Kuantan, Malaysia

The research focuses on the basic and morphological characteristics to ensure bauxite ore reached the International Maritime Solid Bulk Cargoes Code (IMSBC Code) standard before being exported to other countries. The testing procedure, referred to as Geo-spec 3: Model Specification for Soil Testing, was performed to discover the basic parameters of the soil, including pore size distribution, water content, particle density, and morphology qualities. At Bukit Goh, Kuantan, about four (4) samples were chosen, whereas two (2) samples were from the stockpile and two (2) samples were from the Bukit Goh mine. The results illustrated that the mean water content of the soil is 20.64% which is above 10% of the recommended value. The value of Bulk Density is not in the range of 1190 kg/m3 to 1389 kg/m3, which is 2836.25 kg/m3 and the particle size distribution for fine material is greater than 30%, and coarse material is less than 70%. The SEM examination revealed a high concentration of tiny particles in bauxite samples. Bukit Goh bauxite cannot be classified as group C under the IMSBC Code. As a result, the bauxite does not meet the criteria and cannot be shipped.

Identifying Model Complexity: A Machine Learning Framework

`All models are wrong, but some are useful' is an often used mantra, particularly when a model's ability to capture the full complexities of social life is questioned. However, an appropriate functional form is key to valid statistical inference, and under-estimating model complexity can lead to biased results. Unfortunately, it is unclear a-priori what the appropriate complexity of a functional form should be. I propose to use methods from machine learning to generate an estimate of the fit potential in a dataset. By comparing this fit potential with that from a functional form originally hypothesized by a researcher, a lack of model complexity in the latter can be identified. These flexible models can then be unpacked to generate understanding into the type of complexity missing. I illustrate the approach using simulations, and real-world case studies, and show how the framework is easy to implement, and leads to improved model specification.