Essays on the general determinants of consumption and savings

This thesis consists of 4 studies linked together by my attempts to study the determinants and behavior of consumption and savings. Chapter One provides an introduction and background for this thesis. Chapter Two replicates Fiorito and Kollintzas (2004). This paper examines the crowding-out effect between government consumption and private consumption. My replication confirms their original findings by re-creating their dataset and estimation methods using the same sources listed in Fiorito and Kollintzas’ appendix. Furthermore, I concluded that their results are robust when employing more recent data. Chapter Three investigates why savings are so high in China from the perspective of the One-Child Policy (OCP). Using data from the 2014 Gallup World Poll and Global Findex database. I compare the saving behavior of Chinese people with people from regions that do not have restrictive population policies. These regions share many cultural, demographic, and economic characteristics with China, suggesting they can be used as a counterfactual for China. The rich dataset also enables me to adopt the Blinder-Oaxaca decomposition procedure to disentangle the different channels by which the OCP could affect savings. My results suggest that there is little difference in the savings behaviour of Chinese people with their regional counterfactuals, and my estimates are generally small. Therefore, I find no evidence to support that the OCP can explain China’s high saving rate. My findings also suggest that the relaxation of the OCP is unlikely to increase Chinese consumption significantly. Chapter Four focuses on using search engine data from Baidu and Google to predict consumption-related aggregates in China. Over the last 15 years, researchers have used search intensity data like Google Trends to analyze whether the volume of internet searches can help predict consumption and consumer behavior, while limited attention has been put on economies where other search engines like Baidu dominates the market. In Chapter Four, I investigate whether Baidu and Google can help to forecast total retail sales of consumption goods in China. I estimate both the baseline models and the models augmented with Baidu/Google search term series, using both OLS and Lasso methodologies. My results show that adding information from Baidu search intensities to the baseline model can improve the accuracy of the predictions. Furthermore, the improved performance from the Baidu data is greater than that from Google Trends or Chinese Consumer Confidence surveys. Chapter Five investigates whether the forecasting procedures I used for Chinese consumption would also be effective in the New Zealand context. To achieve this goal, I adopt a similar estimation procedure as Chapter Four to nowcast and forecast quarterly household consumption using data from Statistics New Zealand for the period 2005 Q1 to 2020 Q4. My results indicate that models with Google Trends reduce prediction errors by 18% for nowcasting and up to 45% for forecasting over a baseline OLS model with AR terms. Chapter Six concludes this thesis. It provides an overview of my chapters, as well as a summary of my main findings

Forecasting tourism demand with an improved mixed data sampling model

Search query data reflect users’ intentions, preferences and interests. The interest in using such data to forecast tourism demand has increased in recent years. The mixed data sampling (MIDAS) method is often used in such forecasting, but is not effective when moving average (MA) dynamics are involved. To investigate the relevance of the MA components in MIDAS models to tourism demand forecasting, an improved MIDAS model that integrates MIDAS and the seasonal autoregressive integrated moving average process is proposed. Its performance is tested by forecasting monthly tourist arrivals in Hong Kong from mainland China with daily composite indices constructed from a large number of search queries using the generalised dynamic factor model. The forecasting results suggest that this new model significantly outperforms the benchmark model. In addition, comparing the forecasts and nowcasts shows that the latter generally outperform the former

Understanding new products’ market performance using Google Trends

This paper seeks to empirically examine diffusion models and Google Trends’ ability to explain and nowcast the new product growth phenomenon. In addition to the selected diffusion models and Google Trends, this study proposes a new model that incorporates the two. The empirical analysis is based on the cases of the iPhone and the iPad. The results show that the new model exhibits a better curve fit among all the studied ones. In terms of nowcasting, although the performance of the new model differs from that of Google Trends in the two cases, they both produce more accurate results than the selected diffusion models

A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis.

The main aim of this paper is to build a Real Time Leading Economic Indicator (RT-LEI) that improves Composite Leading Indicators (CLI)’s performance to anticipate GDP trends and turning points for the Spanish economy. The indicator has been constructed using a Factor Analysis and is composed of 21 variables concerning motor vehicle activity, financial activity, real estate activity, economic sentiment, and industrial sector. The data sources used are Google Trends and Thomson Reuters Eikon-Datastream. This work contributes to the literature, studying the dynamics of GDP, CLI and RT-LEI using Fractional Cointegration VAR (FCVAR model) and Continuous Wavelet Transform (CWT) for its resolution. The results show that the model does not present mean reversion and it is expected the RT-LEI reveals a bear trend in the next two years, alike IMF and Consensus FUNCAS′ forecasts. The reasons are mostly associated with escalating global protectionism, uncertainty related to Catalonia and faster monetary policy normalization.pre-print990 K

Predicting Tourism Demand in Indonesia Using Google Trends Data

Tourism data is one of the strategic data in Indonesia. In addition, tourism is one of the ten priority programs of national development planning in Indonesia. BPS-Statistics Indonesia has collected data related to tourism demand in Indonesia, but these data have different time period. Several data can be provided monthly, while the other data can be provided annually. However, accurate and real time tourism data are needed for effective policy making. In this era, all of information about tourism destination or accommodation can be gotten easily through internet, especially information from Google search engine, such as information about tourism places, flights, hotels, and ticket for tourism attractions. Since 2004, Google has provided the information of user behavior through Google Trends tool. This paper aims to analyze and compare the patterns of tourism demand in Indonesia from Google Trends data with tourism statistics from BPS-Statistics Indonesia. In order to understand tourism demand in Indonesia, we used Google Trends data on a set of queries related to tourism. This paper shows that the search intensity of related queries provides the pattern of predicted tourism demand in Indonesia. We evaluated the prediction result by comparing several time series models. Furthermore, we compared and correlated the Google Trends data with official data. The result shows that Google Trends data and tourism statistics have similar pattern when there were disasters. The result also shows that Google Trends data has correlation with official data and produced accurate prediction of tourism demand in Indonesia. Therefore, Google Trends data can be used to predict and understand the pattern of tourism demand in Indonesia

Forecasting tourism arrivals with an online search engine data: A study of the Balearic Islands

Este estudio analiza diferentes temas relacionados con la predicción en el área del revenue man‑agement sobre el número de llegadas turísticas para las Islas Baleares. Específicamente, el estudio utiliza búsquedas de un buscador online (Google Trends) para demostrar su poder predictivo en comparación con los métodos tradicionales. He desarrollado la base de datos en base a dos principales volúmenes de llegadas turísticas, y después he comparado cada modelo con su correspondiente modelo de referencia para descubrir si el indicador de Google Trends puede mejorar la precisión de la predicción. Consecuentemente, el test de causalidad de Granger indicó una causalidad positiva entre las variables indicando unos buenos resultados de la estimación. Además, calculé el porcentaje de error absoluto medio (MAPE) para cada modelo y los resultados mostraron una mejora considerable en los modelos que incluyen Google Trends respecto al modelo de referencia. Los resultados muestran algunas pistas para mejorar la eficiencia de las compañías y realzar la toma de decisiones de los legisladores.This study explores issues related to the forecasting in revenue management in the prediction of tourism arrivals for the Balearic Islands. Specifically, the study uses queries from a web search data (Google Trends) in order to demonstrate the forecasting power of such measures compared to traditional methods. I developed a database formed by the two main tourist volumes and then, I compared each model with its corresponding baseline to figure out whether the Google Trends indicator can increase accuracy of the predic‑tion. Consequently, Granger causality test indicated a positive causality between variables suggesting good estimating results. Besides, I calculated the Mean Absolute Percentage Errors (MAPE) for each model and the results showed a considerable improvement of the Google Trends models compared to baseline models. The results provide some hints for increasing company efficiency and enhance policy maker decision making

Research on CPI Prediction Based on Natural Language Processing

In the past, the seed keywords for CPI prediction were often selected based on empirical summaries of research and literature studies, which were prone to select omitted and invalid variables. In this paper, we design a keyword expansion technique for CPI prediction based on the cutting-edge NLP model, PANGU. We improve the CPI prediction ability using the corresponding web search index. Compared with the unsupervised pre-training and supervised downstream fine-tuning natural language processing models such as BERT and NEZHA, the PANGU model can be expanded to obtain more reliable CPI-generated keywords by its excellent zero-sample learning capability without the limitation of the downstream fine-tuning data set. Finally, this paper empirically tests the keyword prediction ability obtained by this keyword expansion method with historical CPI data

Forecasting tourist arrivals at attractions: Search engine empowered methodologies

© The Author(s) 2018. Tourist decision to visit attractions is a complex process influenced by multiple factors of individual context. This study investigates how the accuracy of tourism demand forecasting can be improved at the micro level. The number of visits to five London museums is forecast and the predictive powers of Naïve I, seasonal Naïve, seasonal autoregressive moving average, seasonal autoregressive moving average with explanatory variables, SARMAX-mixed frequency data sampling and artificial neural network models are compared. The empirical findings extend understanding of different types of data and forecasting algorithms to the level of specific attractions. Introducing the Google Trends index on pure time-series models enhances the forecasts of the volume of arrivals to attractions. However, none of the applied models outperforms the others in all situations. Different models’ forecasting accuracy varies for short- and long-term demand predictions. The application of higher frequency search query data allows for the generation of weekly predictions, which are essential for attraction- and destination-level planning