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

Can machine learning algorithms associated with text mining from internet data improve housing price prediction performance?

Housing frenzies in China have attracted widespread global attention over the past few years, but the key is how to more accurately forecast housing prices in order to establish an effective real estate policy. Based on the ubiquitousness and immediacy of Internet data, this research adopts a broader version of text mining to search for keywords in relation to housing prices and then evaluates the predictive abilities using machine learning algorithms. Our findings indicate that this new method, especially random forest, not only detects turning points, but also offers prediction ability that clearly outperforms traditional regression analysis. Overall, the prediction based on online search data through a machine learning mechanism helps us better understand the trends of house prices in China. First published online 10 June 202

Using the baidu index to predict chinese housing price and volume - a survey-based keyword selection approach

The paper uses a survey-based keyword selection approach to examine the effect of the Baidu Index on Chinese real estate trends. After obtaining weights from 546 questionnaires to composite the indexes, I find that in the transaction volume model with the survey-based indexes, the adjusted R2 increases by 8.240 percentage points compared to the baseline model. Such improvement also exists in a forecasting test, reducing the Mean Absolute Error by 2.931 percent and the Mean Squared Error by 5.079 percent. The paper further contributes to the keyword selection method and the model by exploiting an up-to-date dataset

Global disease monitoring and forecasting with Wikipedia

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data such as social media and search queries are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with $r^2$ up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.Comment: 27 pages; 4 figures; 4 tables. Version 2: Cite McIver & Brownstein and adjust novelty claims accordingly; revise title; various revisions for clarit

Does sustainability engagement affect stock return volatility? Evidence from the Chinese financial market

This paper examines the impact of firms’ sustainability engagement on their stock returns and volatility by employing the EGARCH and FIGARCH models using data from the major financial firms listed in the Chinese stock market. We find evidence of a positive association between sustainability engagement and stock returns, suggesting firms’ sustainability news release in favour of the market. Although volatility persistence can largely be explained by news flows, the results show that sustainability news release has the significant and largest drop in volatility persistence, followed by popularity in Google search engine and the general news. Sustainability news release is found to affect positively stock return volatility. We also find evidence that market expectation can be driven by the dominant social paradigm when sustainability is included. These findings have important implications for market efficiency and effective portfolio management decision

Critical review of text mining and sentiment analysis for stock market prediction

The paper is aimed at a critical review of the literature dealing with text mining and sentiment analysis for stock market prediction. The aim of this work is to create a critical review of the literature, especially with regard to the latest findings of research articles in the selected topic strictly focused on stock markets represented by stock indices or stock titles. This requires examining and critically analyzing the methods used in the analysis of sentiment from textual data, with special regard to the possibility of generalization and transferability of research results. For this reason, an analytical approach is also used in working with the literature and a critical approach in its organization, especially for completeness, coherence, and consistency. Based on the selected criteria, 260 articles corresponding to the subject area are selected from the world databases of Web of Science and Scopus. These studies are graphically captured through bibliometric analysis. Subsequently, the selection of articles was narrowed to 49. The outputs are synthesized and the main findings and limits of the current state of research are highlighted with possible future directions of subsequent research