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This study aims to examine the effect of the COVID-19 pandemic in comparison to the global financial crisis (GFC) on the gross domestic product (GDP) growth rate of China.

Empirical analyses are conducted through alternative methods such as ordinary least squares, Markov regime switching (MRS) and mixed data sampling (MIDAS) regressions. The flexibility of MIDAS regression enables us to use different variables with quarterly (GDP), monthly (export sales and foreign-exchange reserves) and daily frequencies (foreign exchange rates and Brent oil price).

The results indicate that the COVID-19 pandemic has had a considerable negative effect on China’s GDP growth, while the dummy variables used for the GFC are found to be insignificant. Further, the forecast accuracy test statistics exhibited a superior performance from MIDAS regression compared to the alternative models, such as MRS regression analysis. According to the forecast results, the authors expect a recovery in China’s economic growth in the second quarter of 2020.

This is one of the earliest studies to examine the effect of the COVID-19 pandemic on the Chinese economy, and to compare the impact of COVID-19 with the GFC. The authors provide further evidence regarding the performance of MIDAS regression analysis vs alternative methods. Findings obtained shed light on policymakers, corporations and households to update their consumption, saving and investment decisions in the chaotic environment of this pandemic.

The purpose of this paper is to compare the predictive capability of three methods of truck cycle time estimation in open‐pit mining: computer simulation, artificial neural networks (NNs), and multiple regressions (MRs). The aim is to determine the best method. The most common method currently used is computer simulation.

Truck cycle times at a large open pit mine are estimated using computer simulation, artificial NNs, and MRs. The estimated cycle times by each method are in turn compared to the actual cycle times recorded by a computerized mine monitoring system at the same mine. The errors associated with each method relative to the actual cycle times are documented and form the basis for comparing the three methods.

The paper clearly indicates that computer simulation methods used in predicting truck cycle times in open‐pit mining underestimate and overestimate the results for short and long hauls, respectively. It appears that both NN and regression models are superior in their predictive abilities compared to computer simulations.

The cycle time prediction models developed apply to a specific mine site and one has to be careful not to directly apply these models to other operations.

The paper describes the implementation of regression and NN modelling. An opportunity exists for mines to utilise the large volumes of data generated to predict truck haulage cycle times more accurately and hence, improve the quality of mine planning.

The paper addresses an area of need in the mining industry. Accurate prediction of cycle times is critical to mine planners as it impacts on production targets and hence, the budgets.

The study aims to understand the role of different streams of oil shocks (demand, supply and risk shocks) on the oil-importing and exporting countries' stock returns. The study also examines the impact of crude oil shocks across the economic regimes and market states. Besides, the role of the Global Financial Crisis (GFC) of 2008 in shaping the oil–stock relationship is also investigated.

The authors revisit the impact of oil shocks on emerging equity markets by using the novel shock decomposition algorithm proposed by Ready (2018). The authors consider 24 emerging equity markets for the period spanning over July 15, 2002, to June 18, 2018, and bifurcate them based on oil dependence. The authors use rolling and dynamic conditional correlation analysis to understand the time-varying co-movements between oil prices and stock returns. The regime and state-specific dependence of stock returns on the structural oil shocks are captured by the Markov regime switching and quantile regression models.

The authors find that the demand shocks are positively associated with stock markets, whereas the supply shocks are negatively related, except in some of the oil-exporting countries. The risk-based shocks also appear to have a negative association with stocks. The authors do not find evidence of strong regime dependence and the direction of relationship across the high and low regimes is somewhat stable. Further, the authors observe an intense oil–stock relationship in the bearish market conditions. Besides, the authors also report evidences of changes in oil–stock relationship onset the GFC.

This is among the first studies to use the oil shock decomposition algorithm of Ready (2018) in the context of emerging equity markets. Additionally, oil shocks' role on the stock market movements across the regimes and market states is studied comprehensively. Thus, the nature of oil shock and the extent to which the emerging markets are exposed is observed in this study.

This paper investigates the influence of three different sentiment indicators on the time-varying stock–bond correlation of 15 countries during the global crisis period of the coronavirus disease 2019 (COVID-19) pandemic.

The author uses the time-varying correlation estimated using the autoregressive moving average -dynamic conditional correlation - generalised autoregressive conditional heteroskedasticity (ARMA-DCC-GARCH) model to achieve this aim. The impact of investor sentiment on the stock–bond correlation was analysed using the Markov regime-switching regression.

The study results show that the sentiment indicators of fear, uncertainty and distress have a pronounced negative impact on the stock–bond correlation. They further provide evidence of a strong regime effect on the stock–bond correlation with sentiment indicators.

The paper has a relevant impact on policymakers and fund managers. First, the policymakers now have more insightful evidence of how the stock and bond markets react during crises. Second, the fund managers need to focus on behavioural variables as they may be driving factors in crisis periods that may impair portfolio management.

To the best of my knowledge, the paper is the first to throw light on the behaviour of the stock–bond correlation for 15 countries during the COVID-19 period.

This paper aims to investigate the relationship between oil price shocks and world food prices between 1974 and 2018.

The authors use the SVAR model to disentangle the oil price into supply, aggregate demand and oil-specific demand shocks and apply the detrended cross-correlations analysis to measure the association between oil price shocks and food returns/volatility and analyze contagion effects between oil and food markets.

The results show that the correlations between oil and food prices depend on whether oil prices changes are driven by supply or demand shocks. Particularly, food returns (volatility) are positively (negatively) more dependent on the oil price changes driven by aggregate demand (oil specific demand) shocks. Further analysis dealing with contagion analysis between oil and food markets shows a contagion effect during the food crisis of 2006–2008. Oil-specific demand shocks are the main source of this phenomenon.

This study differentiates itself from the previous literature by simultaneously disentangling oil price into supply, aggregate demand and oil-specific demand-driven shocks and evaluating the cross-correlations between each shock type and food returns/volatility. Specifically, this study has the originality of detecting the main source of contagion effects between oil and food markets over the food crisis of 2006–2008.

The results of this study are important for policymakers and investors. They should account for the oil price fluctuations differently depending on whether the oil price shocks are driven by the demand or supply side. Moreover, they should anticipate an increase (decrease) in food prices due to a positive (negative) oil shock. In addition, special attention should be accorded to the world oil demand. Finally, when a food crisis occurs, markets operators should focus more on the specific oil-demand shocks, as it is the most contributor to possible contagion effects between oil and food markets.

This study differentiates itself from the previous literature by simultaneously disentangling oil price into supply, aggregate demand and oil-specific demand-driven shocks and evaluating the cross-correlations between each shock type and food returns/volatility. Specifically, this study has the originality of detecting the main source of contagion effects between oil and food markets over the food crisis of 2006–2008.

This chapter estimates a regime switching Taylor Rule for the European Central Bank (ECB) in order to investigate some potential nonlinearities in the forward-looking policy reaction function within a real-time framework. In order to compare observed and predicted policy behavior, the chapter estimates Actual and Perceived regime switching Taylor Rules for the ECB. The former is based on the refi rate set by the Governing Council while the latter relies on the professional point forecasts of the refi rate performed by a large investment bank before the upcoming policy rate decision. The empirical evidence shows that the Central Bank’s main policy rate has switched between two regimes: in the first one the Taylor Principle is satisfied and the ECB stabilizes the economic outlook, while in the second regime the Central Bank cuts rates more aggressively and puts a higher emphasis on stabilizing real output growth expectations. Second, the results point out that the professional forecasters have broadly well predicted the actual policy regimes. The estimation results are also robust to using consensus forecasts of inflation and real output growth. The empirical evidence from the augmented Taylor Rules shows that the Central Bank has most likely not responded to the growth rates of M3 and the nominal effective exchange rate and the estimated regimes are robust to including these additional variables in the regressions. Finally, after the bankruptcy of Lehman Brothers the policy rate has switched to a crisis regime as the ECB has focused on preventing a further decline in economic activity and on securing the stability of the financial system.

In this paper we investigate the joint conditional distribution of health (life expectancy) and income growth, and its evolution over time. The conditional distributions of these two variables are obtained by applying non-parametric methods to a bivariate non-parametric regression system of equations. Analyzing the distributions of the non-parametric fitted values from these models we find strong evidence of movement over time and strong evidence of first-order stochastic dominance of the earlier years over the later ones. We also find strong evidence of second-order stochastic dominance by non-OECD countries over OECD countries in each period. Our results complement the findings of Wu, Savvides and Stengos (2008) who explored the unconditional behaviour of these joint distributions over time.

Early turnover is a worldwide problem that occurs frequently during the first three years of employment. From a multidisciplinary perspective, this study attempts to find the economic, organizational and psychological factors that account for turnover at the early stage of employment.

The authors used turnover records provided by the human resources division of a US pharmaceutical company operating in China of 222 Medical Representatives (MR). The method of Firth's logistic regression for analyzing was employed.

As an economic factor, the favorable labor conditions (i.e. high ratio of job vacancies) at the time of recruitment were inversely associated with MR subsequent retention. For organizational factors, unsatisfactory supervision and disappointment of intra-organizational career were the major predictors, and job ranks showed a U-shaped relationship to early resignation. Moreover, working pressure was a psychological factor of early exit.

This study provides organizations with empirical implications to devise retention plans for newcomers at risk of attrition, which prevent them from early turnover in the industry facing a talent shortage. Studies based on the company exit records have little been done in turnover literature.

The purpose of this paper is to examine reported literature on the influence of medical representatives (MRs) and other promotional tools on drug prescribing behaviour, and to assess whether this effect is different in developed and developing countries.

A survey of the literature was conducted across online databases from 2000 to 2016. Eligible studies addressed MRs and other promotion tools used to influence drug prescribing in developed and developing countries.

A total of 40 reviewed studies met the inclusion requirements. In total, 22 of the studies were conducted in developed countries and 18 in developing countries. Out of ten studies that examined the influence of MRs on drug prescribing in developed countries, eight found a positive influence, one found only moderate and one finds no influence. Analogous results were found in developing countries. Six out of ten studies on the influence of MRs conducted in developing countries found a positive effect, three found only moderate effects, while one finds no influence. The influence of promotion tools on prescribing varied in developed countries, five found positive influence, four reported a small effect and one found negative influence. In developing countries, the size of effect also varied, five studies found positive influence of promotion tools on drug prescribing behaviour, five found a negligible or small effect, and one found no association. However, marked differences were observed between two sectors. In the developed countries, MRs are valued as a source of information and can have an effect on prescribing, while it is unreliable in developing countries. Sample drugs are more generally seen as an important promotional tool for prescribing in developed countries than developing countries.

The results derived from this review are based on studies with varying methodological consistency. The review provides the crucial information that will be valuable to researchers working on comparative analysis of marketing efforts in developing and developed countries.

This paper is one of the few systematic reviews on the influence of MRs and other promotional tools on prescribing. It compares the influence of MRs and promotional efforts in both developed and developing countries.