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We empirically investigate the forecasting ability of USD-INR exchange rate volatility models by considering Google Trends data. Within a multiple regression framework, we use historical volatility and liquidity measures to build our benchmark volatility model (Chandra & Thenmozhi, 2014). Moreover, we extend Bulut (2018) to incorporate indexes for 15 keywords (price-related, income-related, and liquidity-related) from Google Trends data into our benchmark volatility model to evaluate the forecasting ability of the models. Our results indicate that Google Trends data can improve volatility prediction and that among the groups of keywords that we consider, the price-related keywords have the best forecasting ability. Incorporating data on searches for “prices” into the model produces the highest reduction in the forecasting error: a 22.75% decrease compared to the level in the benchmark model. Hence, these empirical findings indicate that Google Trends data contain information that influences exchange rate movements.

Many recent chapters have investigated whether data from internet search engines such as Google can help improve nowcasts or short-term forecasts of macroeconomic variables. These chapters construct variables based on Google searches and use them as explanatory variables in regression models. We add to this literature by nowcasting using dynamic model selection (DMS) methods which allow for model switching between time-varying parameter regression models. This is potentially useful in an environment of coefficient instability and over-parameterization which can arise when forecasting with Google variables. We extend the DMS methodology by allowing for the model switching to be controlled by the Google variables through what we call “Google probabilities”: instead of using Google variables as regressors, we allow them to determine which nowcasting model should be used at each point in time. In an empirical exercise involving nine major monthly US macroeconomic variables, we find DMS methods to provide large improvements in nowcasting. Our use of Google model probabilities within DMS often performs better than conventional DMS methods.

An essential part of the transfer of knowledge in the tourism and hospitality industry, destination image is defined as the expression of objective knowledge, imagination, and the subjective emotions of the tourist. Social media is profoundly changing the way the tourist images and interacts with the destination environment. In turn, firms in the industry are seeking to leverage the power of social media to gain insights into tourist cognition and behavior. In this chapter, we analyze various social media to investigate knowledge transfer relating to two groups of hotels in Philadelphia, and we propose a methodology to predict future lodging demand from empirical data in line with the objectives of the t-Forum.

This chapter analyses potentials of including online search volume data in modeling the demand series of consumer products. Forecasting future demand for products of a company represents one of the important parts of planning and conducting business in general. Thus, the purpose of this chapter is twofold. The first purpose is to give a critical overview of the existing research on the topic of forecasting and nowcasting demand and consumption. The other purpose is to fill the gap in the literature by empirically comparing several approaches of modeling and forecasting demand and consumption on real data. Results of the empirical analysis show that including online search volume data can enhance modeling and forecasting of demand series, especially in times of economic downturns. Thus, it is advised to use such an approach in modeling of consumer demand in a business so that better business performance in terms of profits could be obtained.

This chapter presents an evaluation of the literature on the effect of the pandemic on mental health. It draws mainly on the existing economics literature and presents the state of the art of the COVID-19 effect on mental health. While paying particular attention to how the deterioration of mental health evolved over time and across countries, this chapter also considers variation of mental health across individual demographic characteristics as well as different circumstances through which mental health has been affected. Moreover, it provides a general assessment of the methodological aspects of various studies, by discussing the sample and data used, measures of mental health as well as causality issues. Overall, researchers for various countries around the world adopting different measures of mental health, often non-comparable samples and different methodologies document consistently that the level of mental health has been deteriorated during the pandemic, with the negative effect of the lockdown on mental health being evident in the early stage of the pandemic and on the whole population. Findings point out to a high degree of heterogeneity within demographic groups.

Distance education (DE) is neither a new concept nor process. Learning through postal correspondence can be traced back to more than 150 years. The avenues of DE have evolved from postal correspondence, videotaped lessons, electronic communications, to distance teaching higher education institutes. Up until the start of 2020, DE was an educational process of choice or preference. However, when WHO declared COVID-19 as a pandemic in March 2020, DE was no longer an option but rather the only choice and educational avenue for the majority of the universities worldwide; Lebanon is no exception. This chapter considered the case study of DE at Phoenicia University, Lebanon, studying instructors’ attitudes and perceptions about some of the quality attributes of DE such as interactivity, inclusiveness, and immediacy prior to and after their first online semester. A mixed methods approach was adopted, where pre- and post-test surveys were administered with 54 instructors in Spring 2020. Semi-structured interviews were conducted with 12 instructors toward the end of that semester. This study found that the participants held a more positive stance when it came to instructor immediacy following their first online semester. On the contrary, instructors’ attitudes and perceptions toward interactivity and inclusiveness did not significantly change between the pre- and post-test results, maintaining a less favorable stance of DE for these particular attributes in comparison to physical classroom education. The study concluded that overall, participants perceived DE as an efficient approach given the unprecedented crisis; nonetheless, the effectiveness of such an approach was challenged by many obstacles and limitations due to internet connection issues and the unreliable power infrastructure.

This chapter investigates attention theory by examining retail investors' true intention to purchase. Attention theory indicates that investors, and especially retail investors, typically invest in stocks about which they are aware. Previous studies test attention theory by analyzing stock price behavior or trading volume. However, stock prices and trading volume are primarily driven by institutional investors rather than retail investors. We examine investor attention using initial public offering (IPO) subscriptions in Taiwan because only retail investors are allowed to subscribe to Taiwanese IPOs. We use media coverage as a measure of passive retail investor attention and Google search volume as a measure of active retail investor attention. Our results reveal that active attention has a more profound relationship with retail investor IPO subscriptions than passive attention does. Additionally, information about the value of IPOs taken from trading prices in the pre-IPO market mitigates the effects of attention theory.