Browsing by Subject "Google"
Now showing items 1-3 of 3
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(2018)Internet search engines produce large amounts of data. This thesis shows how the data about internet searches can be used for inflation forecasting. The internet search data is constructed from searches performed on Google. The sample covers eurozone countries over the period from January 2004 to July 2017. The performance of the internet searches is evaluated relative to traditional inflation forecasting benchmark models. The usefulness of the Google searches is evaluated by Granger causality and out-of-sample performance. Furthermore, to study the robustness of the results, the out-of-sample forecasting accuracy has been evaluated in two separate sub-samples. In this study, a simple autoregressive model augmented with internet searches is found to outperform the traditional benchmark models in predicting the month-over-month inflation of the near future. Moreover, the improvement is statistically significant in one-month ahead forecasting accuracy. The Google model also outperforms the benchmark models in year-over-year inflation forecasting. However, the improvement in year-over-year forecasting accuracy is modest. In addition, this thesis shows that the seasonally adjusted internet search data can improve the performance of the Google model slightly. This thesis is related to fast-growing research on employing Google Trends data in economic forecasting. The findings in this thesis require further research in exploiting the internet search data in macroeconomic forecasting.
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(2017)The internet is a popular channel for finding information. The search queries entered into a search engine contain a huge amount of data, but can it be used in economic forecasting? This thesis investigates if Google searches observe the changes in the Finnish housing market. The focus is this thesis is in housing price and home sales forecasting. Google search data is collected from Google Trends. Google Trends provides data describing the popularity of search queries. Google Trends data is updated every day and thus its publishing frequency is much higher in comparison with the official housing market data. The difference in publishing frequency can help to predict changes in housing markets before the official data is revealed. To evaluate the usefulness of Google data a simple model is extended with the Google search index. The forecasting ability of the simple model and the model with Google searches are then compared. Both models are used to forecast the current values of housing market indicators as well as forecasting near-future values. Furthermore, the Granger causality test is employed to investigate if Google searches are useful in forecasting housing market variables. The robustness of the results is studied using the fixed effects model. Also, housing price changes are forecasted as a robustness check. The results suggest that Google searches are useful in forecasting the Finnish housing market. Adding Google searches to a simple housing price forecasting model improves the accuracy of the contemporaneous forecast by 7.5 percent on average. Google searches improve contemporaneous home sales forecast by 15.9 percent on average. Also, the Granger causality test suggests that Google searches are useful in forecasting home sales. The findings are not as clear for Granger causality between Google searches and housing prices. The Granger causality test results suggest that Google searches could be useful in forecasting the current housing prices but not future values. The results also suggest that Google searches improve the near-future forecasts of both indicators.
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(2019)Studies have shown that the platform companies Google and Facebook have a disruptive nature in how media companies organise their work, and some researchers claim they are a duopoly in digital advertising. However, Google says it supports media by “helping” media industries through funding and training. This study argues that by examining what media projects Google supports, we get a good overview of what challenges journalism is currently facing and the solutions for tackling these problems, and ultimately, how this connects to Google as a platform company and to its narrative. This study aims to investigate which media industry challenges Google tries to address by financial support and to examine the solutions to these challenges proposed in accepted Digital News Innovation Fund (DNI) projects. Thus, this research asks: What are the challenges for media and journalists that Google Digital News Initiative is addressing? What specific challenges get the largest support? What are the main solutions proposed in projects supported by Google DNI? Based on the review of the literature about the relationships between platform companies and media and responses to challenging conditions in the ecosystem of platforms, qualitative content analysis was used to examine the last round of the DNI Fund’s 102 projects. The analysis demonstrated that Google supports projects that classify in three directions: Business Model Innovations, Product Development in Editorial Processes and Ecosystem Development Approaches. One of the most interesting findings shows that Google favours supporting projects that concern solutions for the increase in audience subscriptions rather than addressing what publishers have concerns about the most – Google’s domination over the digital advertisement. The results open the discussion about the possible signs of Google’s support in media industries being a “self-help” for their mission of organising the world’s information. Further research is needed to identify what is the content of the other projects Google presents as “help” to media industries.
Now showing items 1-3 of 3