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Regression discontinuity (RD) design is a sophisticated quasi-experimental approach used for inferring causal relationships and estimating treatment effects. This paper aims to educate human resource development (HRD) researchers and practitioners on the implementation of RD design as an ethical alternative for making causal claims about training interventions.

To demonstrate the key features of RD designs, a simulated data set was generated from actual pre-test and post-test diversity training scores of 276 participants from three organizations in the USA. Parametric and non-parametric analyses were conducted, and graphical presentations were produced.

This study found that RD design can be used for evaluating training interventions. The results of the simulated data set yielded statistically significant results for the treatment effects, showing a positive causal effect of the training intervention. The analyses found support for the use of RD models with retrospective training intervention data, eliminating ethical concerns from random group assignment. The results of the non-parametric model provided evidence of the plausibility of finding the right balance between precision of estimates and generalizable results, making it an alternative to experimental designs.

This study contributes to the HRD field by explicating the implementation of a sophisticated, statistical tool to strengthen causal claims, contributing to an evidence-based HRD approach to practice and providing the R syntax for replicating the analyses contained herein.

Despite the growing number of scholarly articles being published in HRD journals, very few have used experimental or quasi-experimental design approaches. Therefore, a very limited amount of research has been devoted to uncovering causal relationships.

The purpose of this paper is to assess price linkages and patterns of transmission among producer and consumer markets for apple in Slovenia.

Non-linear error correction models are applied. Non-linearities are allowed by means of threshold and multivariate local linear regression estimation techniques. Monthly prices over the period 2000-2011 are used in the empirical application.

Both techniques provide evidence of non-linearities in price adjustments. Findings suggest that producer and consumer prices tend to increase rather than decrease. Results also indicate that parametric threshold approaches may have difficulties in adequately representing price behavior dynamics.

The main contribution of this work to the literature relies on the fact that this is the first attempt to assess vertical price transmission in the apple sector in Central and Eastern European Country markets. Further, it is the first attempt to use multivariate local linear regression techniques in this context.

This paper aims to investigate the contemporaneous link between price volatility and trading volume in the futures markets of energy.

Non-parametric (local linear) regression models and formal statistical tests are used to assess monotonicity, linearity and symmetry. The data are daily price and volumes from five futures markets (West Texas Intermediate, Brent, gasoline, heating oil and natural gas) in the USA.

Trading volume and price volatility have, in all markets, a strong nonlinear relation to each other. There are violations of monotonicity locally but not globally. The qualitative nature of the price shocks may have implications for the trading activity locally.

To the authors’ best knowledge, this is the first manuscript that investigates simultaneously and formally all the three important issues (i.e. monotonicity, linearity and asymmetry) for the price volatility–volume relationship using a highly flexible nonparametric approach.

Though many studies in the past dealt with the survival and growth of enterprises both in the local- and export-markets, less attention was given to the analysis of the duration of enterprises entry into the export-markets. The aim of this paper is, therefore, to analyze the duration of Ethiopian enterprises entry into the export-markets.

This paper used data collected from 848 enterprises through a cross-sectional survey method conducted by the World Bank in 2015. In order to estimate the average duration – the time that enterprises need to wait before entering into the export-markets and the associated factors – the authors used the mixture of non-parametric (Kaplan-Meier) and parametric (Weibull) models.

The non-parametric results show that enterprises are required to wait for an average of about eight years before entering into the export-markets after their establishment. In addition, foreign-owned enterprises were found to be faster in entering into the export-markets than their domestically owned counterparts. The parametric results revealed that enterprises’ product innovation, enterprises’ size and age, and custom and trade regulations are factors that curtail the durations of enterprises entry into the export-markets. On the other hand, enterprises’ operational costs, the size of enterprises’ locality, and enterprises’ location are factors that slow the durations of enterprises’ entry into the export-markets.

This study is the first to offer pioneering evidences on the duration (time in years) that Ethiopian enterprises need to wait before entering into the export-markets and the factors that affect the length of their waiting time.

The purpose of this paper is to investigate the dynamic relationship between inflation, interest rate differential, the exchange trade and exchange rate parities, i.e. (USD/TND, EUR/TND and JPY/TND).

Given the existing non-linear form between the different time series in this study, the empirical analysis is based on the using of non-parametric method such as the artificial neural networks. In order to detect the causality relationship between the variables, the authors use an NARX model.

Mixed results were found; there is a bidirectional relationship between inflation and exchange rate among others. Results also show that there is a strong correlation between the terms of trade and inflation, which says that trade openness increases the demand for imported goods and, therefore, causes more inflation for Tunisia.

After these results, it is important for policymakers to know which factors influence exchange rate stability, especially in developing countries like Tunisia.

This paper aims to focus on a better model to capture the trait of varying volatility in various financial time series, as well as to obtain reliable estimate of value at risk (VaR).

The typical methods in spectral analysis are used to obtain the sample of conditional mean, conditional variance and residual term. The generalized regression neural network is used to establish a time-varying non-linear model, and the non-parametric kernel density estimation method is applied for the estimation of VaR.

The proposed model is able to follow the heteroscedastic characteristic which is common in financial time series, and the estimated VaR is satisfactory.

The analysis method in this study allows the hedgers, bankers, financial analysts as well as economists to draw a better inference from financial time series. Also, relatively more precise estimate of the VaR value for a certain kind of financial asset is available. The model with its derived estimates would definitely help in developing other models.

Up-to-date, the study in literature which models financial time serial from the viewpoint of spectral analysis is rare to see. Thus, the proposed model, along with a comprehensive empirical study which reveals desirable result for the estimation of VaR would enrich the related researches at present.

Evidence suggests that a growing number of consumers across the world are becoming more environmentally responsible in terms of their personal habits and lifestyles. In this paper, the authors aim to use both parametric and non-parametric econometric models to estimate Kuwaiti consumers’ willingness to pay (WTP) for environmental protection in Failaka island.

Contingent valuation methods based on log-logistic and log-normal regression models revealed that consumers in Kuwait are willing to pay a price premium of approximately 40 Kuwaiti dinars for environmental protection in Failaka island based on the double-bound dichotomous choice model.

Socio-economic variables have no significant influence on the respondent’s WTP. As expected income has a positive relationship with WTP and bid price has negative relationship with WTP to protect the environment in Failaka island.

This study highlight the fact that understanding consumers’ environmental-friendly behaviors may play an important role in formulating environmental policy changes to face complex problems as diverse as environmental pollution or environmental degradation.

This paper aims to review prior studies and presents a synthesis of the takeover prediction literature spanning the period 1968–2018.

The paper adopts a narrative review approach. It explores prior studies on takeover target prediction from a historical perspective, focusing on the evolution and development of the literature over the 50-year period.

From a historical development perspective, prior studies in the area can be partitioned into four distinct eras. Studies in the first era (1968–1985) mainly established that takeover targets share common characteristics which can be captured with financial ratios. Studies in the second era (1986–2002) developed and extended formal target prediction hypotheses. These studies concluded that it was impossible to build a successful investment strategy around takeover target prediction. Studies in the third era (2003–2009) explored similar questions using alternative modelling techniques but arrive at similar results – targets can be predicted with limited accuracy and target prediction is unlikely to lead to abnormal returns. Studies in the fourth era (2010–2018) explore implications of M&A predictability on share valuation, governance and bond prices (amongst others), but most importantly, provide some evidence that takeover prediction can lead to abnormal returns when combined with appropriate screening strategies.

This presents the first in-depth review of the literature on takeover target prediction. It highlights the development of the literature over four distinct eras and identifies several limitations, research gaps and opportunities for future research. Given the recent decline in the literature (i.e. fourth era), this study may stimulate new research in the area.

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.

As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.

The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.

This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

This study examines the survival probability of the firms during the COVID-19 pandemic and identifies the effects of pandemic-era business strategies on firm survival across sectors and sizes.

This study combines World Bank Enterprise Survey data with three consecutive follow-up COVID-19 survey data. The COVID-19 surveys are the follow-up surveys of WBES, and they are done at different points of time during the pandemic. Both WBES and COVID-19 surveys follow the same sampling methods, and the data are merged based on the unique id number of the firms. The data covers 12,551 firms from 21 countries in different regions such as Africa, Latin America, Central Asia and the Middle East. The study applies Kaplan–Meier estimate to analyze the survival probability of the firms across sectors and sizes. The study then uses Cox non-parametric regression model to identify the effect of business strategies on the survival of the firms during the pandemic. The robustness of the Cox model is checked using the multilevel parametric regression model.

The study's findings suggest that a firm's survival probability decreases during the pandemic era. Manufacturing firms have a higher survival probability than service firms, whereas SMEs have a higher survival probability than large firms. During the pandemic period, business strategies significantly boost the probability of firm survival, and their impacts differ among firm sectors and sizes. Several firm-specific factors affect firm survival in different magnitudes and signs. Except in a few cases, the findings also indicate that one strategy positively moderates the influence of another strategy on firm survival during a pandemic.

COVID-19 pandemic has drastically affected the business across the globe. Firms adopted new business processes and strategies to face the challenges created by the pandemic. The critical research question is whether these pandemic-era business strategies ensure firms' survival. This study attempts to identify the effects of these business strategies on firms' survival, focusing on a comprehensive firm-level data set that includes firms from different sectors and sizes of countries from various regions.