Forecasting Private Consumption: Survey-based Indicators vs. Google Trends

In this study we introduce a new indicator for private consumption based on search query time series provided by Google Trends. The indicator is based on factors extracted from consumption-related search categories of the Google Trends application Insights for Search. The forecasting performance of the new indicator is assessed relative to the two most common survey-based indicators - the University of Michigan Consumer Sentiment Index and the Conference Board Consumer Confidence Index. The results show that in almost all conducted in-sample and out-of-sample forecasting experiments the Google indicator outperforms the survey-based indicators. This suggests that incorporating information from Google Trends may off er signifi cant benefi ts to forecasters of private consumption.Google Trends, private consumption, forecasting, Consumer Sentiment Indicator

Quantifying trading behavior in financial markets using Google Trends

Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as “early warning signs” of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior

Tracking internet interest in anabolic-androgenic steroids using Google Trends

Background: There is a perception that the prevalence of anabolic-androgenic steroid (AAS) use is increasing in the UK, with consequent individual and public health risks. Nevertheless, there is a lack of real-time surveillance data to support the development of effective policy. This paper explores the potential of Google Trends to complement existing surveillance methods by analysing user generated search term data. Methods: The Google Trends web tool was used to identify patterns of UK online interest in 15 AAS from January 2011 to December 2015, with 10 ultimately suitable for further analysis. Time series analysis was applied to the data. Results: 10 steroids were ranked from most to least popular. All compounds had peaks in interest between April to July, potentially indicating a consumer driven desire to attain a desired physique in time for summer. Oral steroids were among the most searched for drugs which may have relevance for current service provision to steroid users. Conclusion: Alternative data sources such Google Trends may provide useful additional information to supplement existing surveillance data. The limitations of this method however makes cautious interpretation and triangulation with other data sources essential

A monthly consumption indicator for Germany based on internet search query data

In this study we introduce a new monthly indicator for private consumption in Germany based on search query time series provided by Google Trends. The indicator is based on unobserved factors extracted from a set of consumption-related search categories of the Google Trends application Insights for Search. The predictive performance of the indicator is assessed in real time relative to the European Commission’s consumer confidence indicator and the European Commission’s retail trade confidence indicator. In out-of-sample nowcasting experiments the Google indicator outperformed the surveybased indicators. In comparison to the other indicators, the new indicator also provided substantial predictive information on consumption beyond that already captured in other macroeconomic variables.Google Trends, Private Consumption, Forecasting, Consumer Sentiment Indicator

Nowcasting with Google Trends : a keyword selection method

Search engines, such as Google, keep a log of searches entered into their websites. Google makes this data publicly available with Google Trends in the form of aggregate weekly search term volume. Aggregate search volume has been shown to be able to nowcast (i.e. compute real-time assessment of current activity) a variety of variables such as influenza outbreaks, financial market fluctuations, unemployment and retail sales. Although identifying appropriate keywords in Google Trends is an essential element of using search data, the recurring difficulty identified in the literature is the lack of a technique to do so. Given this, the main goal of this paper is to put forward a method (the "backward induction method") of identifying and extracting keywords from Google Trends relevant to economic variables

Nowcasting With Google Trends in an Emerging Market

Most economic variables are released with a lag, making it difficult for policy-makers to make an accurate assessment of current conditions. This paper explores whether observing Internet browsing habits can inform practitioners about real-time aggregate consumer behavior in an emerging market. Using data on Google search queries, we introduce a simple index of interest in automobile purchases in Chile and test whether it improves the fit and efficiency of nowcasting models for automobile sales. We also examine to what extent our index helps us identify turning points in sales data. Despite relatively low rates of Internet usage among the population, we find that models incorporating our Google Trends Automotive Index outperform benchmark specifications in both in-sample and outof- sample nowcasts while providing substantial gains in information delivery times.

Ngram 150th: Race, Sex and Big Data

Data is powerful in the right hands. Aggregate data is even more powerful. And Google is data. One of the odder tools in the Google arsenal is the Ngram viewer a search engine which charts trends within the folds of Google Books\u27 database. Punch in anything. I mean it. Try anything in the Ngram search engine and start falling down the historical trends rabbit hole. [excerpt