ABSTRACT
At the beginning of the COVID-19 pandemic, the entire world was waiting for a medical solution (for example, vaccines) in order to return to normality. Sanitary restrictions changed our consumption behaviors and feelings. Therefore, this paper analyzes the stochastic properties of consumer sentiment during the COVID-19 episode and the appearance of vaccines against the virus in December 2020 in the United States of America. This study adds a new dimension to the literature because it is the first research paper that uses advanced methodologies based on fractional integration and fractional cointegration analysis to understand the statistical properties of these time series and their behavior in the long term. The results using fractional integration methodologies exhibit a high degree of persistence, finding behavior of mean reversion during the pandemic episode. Therefore, the shock duration in consumer sentiment will be transitory, recovering to its previous trend in the short run. Focusing on the cointegrating part, we arrive at two main conclusions. First, an increase in total vaccination produces a positive reaction or impact on the behavior of consumers. On the other hand, an increase in new COVID-19 cases negatively affects the behavior of the consumer.
ABSTRACT
The main aim of this paper is to analyse and estimate the behaviour of the Spanish economic activity in the next 12 months, by means of a Real-Time Leading Economic Indicator (RT-LEI), based on Google Trends, and the real GDP. We apply methodologies based on fractional integration and cointegration to measure the degree of persistence and to examine the long-term relationship. Finally, we carry out a forecast using a Machine Learning model based on an Artificial Neural Network. Our results indicate that the Spanish economy will experience a contraction in 1Q-21 and will require strong measures to reverse the situation and recover the original trend.
ABSTRACT
This paper deals with the analysis of the evolution of international trade after COVID-19, examining commodity prices, the shipping industry, and the influence of the cost of bunker fuel. To this end, we use techniques based on fractional integration, fractional cointegration VAR (FCVAR) and wavelet analysis. Monthly data relating to heavy fuel oil prices and the shipping market from October 2011 to September 2021 are used. Using fractional integration in the post-break period, a lack of mean reversion is observed in all cases, which means that, for the commodity prices and shipping market indices, a change in trend will be permanent after COVID-19 unless strong measures are carried out by the authorities. Using wavelet analysis, we conclude that the demand shock represented in the indices mentioned above has led the price of fuel oil since the beginning of the pandemic, and bunker fuel is not relevant in determining the cost of maritime transport.
ABSTRACT
Since December 2019 we have been living with the virus known as SARS-CoV-2, a situation which has led to health policies being given prevalence over economic ones and has caused a paralysis in the demand for raw materials for several months due to the number confinements put in place around the world. Since the worst days of the pandemic caused by COVID-19, most commodity prices have been recovering. The main objective of this research work is to learn about the evolution and impact of COVID-19 on the prices of raw materials in order to understand how it will affect the behavior of the economy in the coming quarters. To this end, we use fractionally integrated methods and an Artificial Neural Network (ANN) model. During the COVID-19 pandemic episode, we observe that commodity prices have a mean reverting behavior, indicating that it will not be necessary to take additional measures since the series will return, by themselves, to their long term projections. Moreover, in our forecast using ANN algorithms, we observe that the Bloomberg Spot Commodity Index will recover its upward trend, increasing some 56.67% to the price from before the start of the COVID-19 pandemic episode.
ABSTRACT
This paper deals with the relationship between the CO2 emissions and the global temperatures across the various pandemic episodes that have been taken place in the last 100 years. To carry out the analysis, first we conducted unit root tests finding evidence of nonstationary I(1) behavior, which means that a shift in time causes a change in the shape of distribution. However, due to the low statistical power of unit root tests, we also used a methodology based on long memory and fractional integration. Our results indicate that the emissions display very heterogeneous behavior in relation to the degree of persistence across pandemics. The temperatures are more homogeneous, finding values for the orders of integration of the series smaller than 1 in all cases, thus showing mean reverting behavior.
ABSTRACT
People with autism spectrum disorder (ASD) have great difficulties in social interaction and in the management of personal and other people’s emotions. This work aimed at developing an intelligent bracelet, capable of inferring the children’s emotional state, transmitting it to others, and, above all, informing the patients themselves so that they can learn to recognise, control, and work with, as well as to improve their self-knowledge and their relationship with their environment. Electrodermal activity (EDA) and photoplethysmography (PPG) are useful in combined psychophysiological and medical studies to determine the mood of patients. Due to COVID-19, no experiments with subjects could be carried out, although the modules were validated, and a public database was used to test the system’s application. The results concluded that, in general, when an individual is altered or becomes nervous, either positively or negatively (also known as valence) to a stimulus, their heart rate and sweating increase. This is the kind of relationship between physiological signals and external stimuli that the design of these circuits was intended to confirm. Finally, with the indicators of nervous system activity and knowing the behaviour of skin conductance in response to each basic emotion, it can be determined whether the subject is in a situation of pleasure or frustration in response to each reaction.
ABSTRACT
ABSTRACT This research paper makes an empirical analysis based on long memory to understand the historical behavior of initial unemployment claims (ICSA) in the United States (U.S.) during all the recession periods and epidemic diseases such as Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS) and COVID-19 since 1967 applying statistical methods based on long range dependence and fractional differentiation. Using unit root/stationarity tests (ADF, PP and KPSS) we discover that the original time series is stationary I(0) and the subsamples are non-stationary I(1). Finally, to analyze the original time series as well as the several periods corresponding to the recessions that occurred in U.S. and the three epidemic diseases, we use AIC and BIC criterion to fit the best ARFIMA model. We conclude that the results display long memory with a degree of integration strictly below 1 (d < 1) for the COVID-19 episode and for the rest of the subsamples, except for the original time series and the 2nd subsample. Thus we can conclude that the impacts will be transient and with long lasting effects of shocks and expecting to disappear on their own in long term. Finally, we use a methodology proposed by Bai and Perron to estimate structural breaks not being necessary to know the time of the breaks in advance. The results are similar to those obtained previously.