L-Moments and calibration-based variance estimators under double stratified random sampling scheme: Application of Covid-19 pandemic

Extreme events gives rise to outrageous results in terms of population-related parameters and their estimates are usually done using traditional moments. Traditional moments are usually affected by extreme observations. This study aims to propose some new calibration estimators considering the L-Moments scheme for variance, which is one of the most important population parameters. a number of suitable calibration constraints under double stratified random sampling were defined for these estimators. The proposed estimators, which were based on L-Moments, were relatively more robust despite extreme values. The empirical efficiency of the proposed estimators was also assessed through simulation. Covid-19 pandemic data from January 22, 2020 to August 23, 2020 was taken into account in the simulation study. (c) 2023 Sharif University of Technology. All rights reserved.

GDP responses to supply chain disruptions in a post-pandemic era: Combination of DL and ANN outputs based on Google Trends

With the recent Russian-Ukraine conflict, the frequency and intensity of disruptive shocks on major supply chains have risen, causing increasing food and energy security concerns for regulators. That is, the combination of newly available sophisticated deep learning tools with real-time series data may represent a fruitful policy direction because machines can identify patterns without being pre-conditioned calibration thanks to experimental data training. This paper employs Deep Learning (DL) and Artificial Neural Network (ANN) algorithms and aimed predicts GDP responses to supply chain disruptions, energy prices, economic policy uncertainty, and google trend in the US. Sampled data from 2008 to 2022 are monthly wrangled and embed different recession episodes connected to the subprime crisis of 2008, the COVID-19 pandemic, the recent invasion of Ukraine by Russia, and the current economic recession in the US. Both DL and ANN outputs empirically (and unanimously) demonstrated how sensitive monthly GDP variations are to dynamic changes in supply chain performances. Findings identify the substantial role of google trends in delivering a consistent fit to predicted GDP values, which has implications While a comparative discussion over the larger forecasting performance of DL compared to ANN experiments is offered, implications for global policy, decision-makers and firm managers are finally provided. © 2023 Elsevier Inc.

Dynamic connectedness among climate change index, green financial assets and renewable energy markets: Novel evidence from sustainable development perspective

This paper investigates the connectedness among the climate change index, green financial assets, renewable energy markets, and geopolitical risk index from June 1, 2012 to June 13, 2022, using Quantile Vector Autoregressive (QVAR) and wavelet coherence (WC). The Total connectedness index (TCI) varies as long as the highest TCI originates in the upper quantile. We also note that the higher TCI decreases after the second wave of COVID19 and increases during the first 100 days of the Russia-Ukraine conflict. Moreover, the results show that Geopolitical risk (GPR) is a net transmitter of the climate change index during the Russian invasion of Ukraine. The green bond and clean energy markets are negatively connected to the GPR at extreme 10 th and 90 th quantiles. The wavelet coherence confirms the QVAR results that the climate change market can be a safe haven against GPR during the Russian invasion. The climate change index, green financial assets, and clean energy are strong influencers in the financial markets and are vital to international peace, reducing geopolitical risk. The study reports a few novel conclusions and implications from a sustainable development perspective.

Green innovation, resource price and carbon emissions during the COVID-19 times: New findings from wavelet local multiple correlation analysis

This paper investigates how oil price, COVID-19, and global energy innovation can affect carbon emissions under time-and frequency-varying perspectives. We contribute to the literature by being the first research to document the relationship between these variables in the short and long run (dynamically) at different frequencies in a multivariate context, thus providing a more detailed picture of the forces driving CO2 emissions. For this purpose, we use a novel methodology, i.e., the wavelet local multiple correlation (WLMC) recently developed by Polanco-Martinez et al. (2020). The results provide fresh evidence of long-run asymmetric dynamic correlations, highlighting how the oil price plays a key role in the dynamics of CO2 emissions. Moreover, we find that, during the long period, there is a strong negative co-movement between CO2 and the global energy innovation index, i. e., more investment in clean energy induces less emission. Supported by our findings, this research suggests crucial policy implications and insights for the governments worldwide in their efforts to revive their economies amidst the pandemic and environmental uncertainties.

Analyzing Cyberchondriac Google Trends Data to Forecast Waves and Avoid Friction: Lessons From COVID-19 in India

This article examines the Google Trends data related to the second COVID-19 wave in India. We investigate the phenomenon of cyberchondria, which potentially causes individuals to avoid getting tested and quarantined directly upon experiencing symptoms for fear of losing their salaries or jobs. We utilize Google Trends data to predict future disease statistics, like the pandemic's impact on human activities and health-related issues in India. By means of a bootstrapped Pearson correlation, a time-lead correlation, and a quantile regression, we found a strong relationship between Google Trend searches and COVID-19 cases. Contextualizing the second COVID-19 wave in India through the lenses of cyberchondria and protection motivation theory, our article notes that, when people develop COVID-19 symptoms, they turn to Google for confirmation and treatment, rather than getting themselves checked early, only getting medically tested, and treated when their health deteriorates. At that stage, given the patients’critical conditions, hospitalization is the only option. This places an unsustainable burden on hospitals, resulting in capacity constraints and increased mortality rates. We suggest using Google Trends data to forecast COVID-19 waves and mobilize the health infrastructure to save lives and facilitate friction-free growth. IEEE

Can tourism sustain itself through the pandemic: nexus between tourism, COVID-19 cases and air quality spread in the 'Pineapple State' Hawaii. (Special Issue: COVID-19 and tourism.)

The COVID-19 pandemic has severely hit the United States of America (USA) with tourism being one of the most directly affected sectors. The effect is even more striking in Hawaii, which has been one of the most popular tourist destinations in the United States since the 1950s. While the state government's early reaction has resulted in a decrease in COVID cases in this state, travel restrictions established in response to the pandemic have wreaked havoc on the state's tourist economy. To quantitatively measure this impact, this paper investigates the nexus between the international tourist arrivals, COVID-19 spread, and air quality in Hawaii. Using the daily data from March 2020 to August 2020, the study employs the robust methodology comprising Wavelet coherence, partial and multiple Wavelet coherence methods. The empirical results reveal a significant coherence between international tourists, COVID-19 cases, and air quality at different time-frequency compositions.