Browsing by Author "Widgrén, Miska"

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.