ESTIMATION, INFERENCE, AND SPECIFICATION TESTING FOR POSSIBLY MISSPECIFIED QUANTILE REGRESSION

Impact of government governance and environmental taxes on sustainable energy transition in China: fresh evidence using a novel QARDL approach

Although economies have experienced immense growth in recent times, however, it also comes with environmental and social consequences which question the current practices and threaten the well-being of current as well as the future generation. This realization, thus, pushes institutions to bring change in existing energy-related policies in order to incorporate social and environmental concerns. Clean energy transition, in this regard, is gaining attraction all over the world as it shifts away economies from non-renewable resources. The study, thereby, intends to explore the role of governance and environmental taxes in the energy transition in China economy over the period 1999-2019. The roles of industrialization and economic growth in the transition of energy are taken into consideration. The recently introduced legit quantile autoregressive distributed lag (QARDL) model and Granger causality in quantiles are applied to quarterly data spanning 1999Q1 to 2019Q4 for empirical quantile analysis. Results echoed that governance has a positive impact and environmental resources have a negative impact on energy transition across all quantiles. However, economic growth influences clean energy transition only at extremely higher quantiles (0.60-0.95), and industrialization does not have any effect on energy transition over the entire quantile range. The findings of the Granger causality analysis reveal the presence of a bidirectional causal association between clean energy transition and all the variables. Worthy policies are recommended on the basis of the findings.

Did weekly economic index and volatility index impact US food sales during the first year of the pandemic?

We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United States from January 2020 to January 2021. We use the US weekly economic index (WEI) to measure economic dislocations and the Chicago Board Options Exchange volatility index (VIX) to capture the broader stock market dislocations. We validate the NARDL model by testing a battery of models using the autoregressive distributed lags (ARDL) methodology (ARDL, NARDL, and QARDL specifications). Our study postulates that an increase in WEI has a significant negative long-term effect on food sales, whereas a decrease in WEI has no statistically significant (long-run) effect. Thus, policy responses that ignore asymmetric effects and hidden cointegration may fail to promote food security during pandemics.

Investigating the existence of asymmetric environmental Kuznets curve and pollution haven hypothesis in China: Fresh evidence from QARDL and quantile Granger causality

This study aims to analyze the asymmetric long-run relationship between economic growth (EG), foreign direct investment (FDI), and carbon emissions (CO2) within the context of the environmental Kuznets curve (EKC) and the pollution haven hypothesis (PHH) in China. Employing the quarterly data from 1982Q1 to 2018Q4, we have used novel techniques to meet the stated objectives of our study, named quantile ARDL and quantile Granger causality. The study provides novel outcomes using the advanced quantile ARDL and quantile Granger causality tests. The significant implication of this method is that it provides locational asymmetry. We find strong evidence of the EKC and PHH for China based on the empirical results of linear and nonlinear ARDL models. Similarly, findings of quantile Granger causality validate the bidirectional relationship among all variables in upper and lower quantiles. Moreover, the results of the Wald test confirm the asymmetric long-run relationship between FDI and carbon emissions (CO2). Thus, legal measures must be enhanced, accepted, rigorously imposed, and monitored in all provinces to assure a further reduction in carbon emissions. This study will be conducive for the policymakers to combat environmental contamination concerning economic growth and FDI inflow in China.

The step towards environmental mitigation in Pakistan: do transportation services, urbanization, and financial development matter?

This study is a scholarly effort to broaden the existing literature on the impact of transportation services, urbanization, and financial development on ecological footprints in Pakistan. Data used in this study covers the period of 39 years from 1980 to 2018. This study adopted the QARDL model to tackle the non-linear association of variables and test their long-run stability across the different quantiles. The findings of this study indicated a significant negative association of transportation services and financial development with ecological footprints in Pakistan at almost all quantiles whereas, the urban population was found to be positively associated with the ecological footprint in Pakistan. Results also justify the existence of the EKC hypothesis in the scenario of Pakistan. Policymakers are advised to frame strategies for investors to invest more in eco-friendly projects to curtail the ecological footprints in Pakistan. Minimizing the dependency of the transportation sector on fossil fuel, and increased use of energy-efficient appliances in the urban population would be beneficial to control the negative influence on ecological footprints in Pakistan.

On the nonlinear effects of energy consumption, economic growth, and tourism on carbon footprints in the USA

The present paper implements the quantile autoregressive lagged (QARDL) approach of Cho et al. (2015) and the Granger causality in quantiles tests of Troster et al. (2018) to explore the nonlinear effects of US energy consumption, economic growth, and tourist arrivals on carbon dioxide (CO2) emission. Our results unveil the existence of substantial reversion to the long-run equilibrium connectedness between the variables of interest and CO2 emissions. The outcomes show that tourist arrivals decrease CO2 emissions in the long term for each quantile. In addition, we found that the output growth positively influences the carbon emissions at lower quantiles but negatively influences the carbon emissions at upper quantiles. Moreover, our findings of short-term dynamics validate an asymmetric short-run effect of tourist arrivals and economic growth on CO2 emissions in the US economy. Further results and their corresponding policy implications are discussed.

Model-robust designs for nonlinear quantile regression

We construct robust designs for nonlinear quantile regression, in the presence of both a possibly misspecified nonlinear quantile function and heteroscedasticity of an unknown form. The asymptotic mean-squared error of the quantile estimate is evaluated and maximized over a neighbourhood of the fitted quantile regression model. This maximum depends on the scale function and on the design. We entertain two methods to find designs that minimize the maximum loss. The first is local - we minimize for given values of the parameters and the scale function, using a sequential approach, whereby each new design point minimizes the subsequent loss, given the current design. The second is adaptive - at each stage, the maximized loss is evaluated at quantile estimates of the parameters, and a kernel estimate of scale, and then the next design point is obtained as in the sequential method. In the context of a Michaelis-Menten response model for an estrogen/hormone study, and a variety of scale functions, we demonstrate that the adaptive approach performs as well, in large study sizes, as if the parameter values and scale function were known beforehand and the sequential method applied. When the sequential method uses an incorrectly specified scale function, the adaptive method yields an, often substantial, improvement. The performance of the adaptive designs for smaller study sizes is assessed and seen to still be very favourable, especially so since the prior information required to design sequentially is rarely available.

Revisiting the growth-carbon dioxide emissions nexus in Pakistan

Pakistan is the most polluted country according to the concentration of air pollution criterion and it has experienced a significant rise in economic growth during the recent years. This paper analyzes the nexus of CO₂ emissions and economic growth using quarterly data over the period of 1960Q1-2014Q4. To provide conclusive policy recommendations, this study applied different econometric methodologies such as the quantile causality approach, the linear ARDL (autoregressive distribution lag) model and the quantile ARDL (QARDL) model. The results indicate evidence of causality running from economic growth to CO₂ emissions at medium quantiles at the 5% level and at low and medium quantiles at the 10% significance level. Findings of linear and nonlinear ARDL models also support the transmission of growth to CO₂ emissions in the long and short run. The Wald test for symmetry sustains the nonlinear ARDL model. Useful policy implications can be learned from the empirical findings.

Parameter instability in quantile regression

The paper analyzes the behavior of a test for structural break based on quantile regression estimates. It considers the case of an estimated break in conjunction with independent and identically distributed (i.i.d.) and non-i.i.d. errors. It compares the null and the alternative models, where the null imposes stability, while the alternative allows the regression coefficients to change in response to the break. The test relies on the increase of the objective function and the worsening of the fit when unnecessary constraints are imposed. An example with serially correlated real data and a Monte Carlo study taking into account non-normal and non-i.i.d. errors analyze the behavior of the test.