Google Econometrics: A Literature Review

house-holds’ inflation expectations in the previous period on search requests: Google users seek for additional information if they belief prices to rise in the future. Higher inflation forecasts of experts only marginally increase Google search requests, but if professional forecasters disagree a lot on future prices, the resulting uncertainty leads to a large increase in Google users’ demand for information.

With regard to the results of the VAR models, we find that television news coverage is driv-ing newspaper coverage, in addition to a feedback effect. Builddriv-ing on this result, we show that Google search requests for inflation are mainly determined by TV reports and only to a lesser degree by newspaper articles. Again, we find considerable feedback effects, suggest-ing that journalists consider the interests of their readers when decidsuggest-ing on the newspaper’s agenda. Finally, taking into account households’ and professional forecasters’ inflation ex-pectations, we show that households’ forecasts are driven by TV reports, newspaper articles, and Google searches, while the feedback effect from expectations on web searches is rather small and estimated less precisely. Furthermore, the impulse response from shocks on web searches to expectations is estimated more efficiently for weekly data, which indicates that the demand for new information has a rather short-run impact on peoples’ expectations.

About 20% of the forecast error variance decomposition of households’ inflation expecta-tions can be explained by Google search requests.

We start the chapter with a brief description of studies that use Google search requests in economics, with a special focus on how web query data can fit into the expectation forma-tion process (Secforma-tion4.2). We then explain our estimation approach in Section (4.3) before describing the compilation of the media and Google data in Section (4.4). Subsequently, Sec-tion (4.5) presents the results and SecSec-tion (4.6) concludes and discusses various direcSec-tions for further research.

index for five Latin American countries. With the exception of Argentina, where officials are suspected to manipulate official price data, the online-based index captures the official fairly well.

This section provides an overview of using Google search requests in economic research.

We summarize both the work that has been conducted with respect to nowcasting and fore-casting economic variables with the help of internet search data, and discuss how Google search data might be related to expectation formation.

The literature on using Google data in forecasting models can be summarized as follows.

As we describe in detail in the literature survey in Section (C.2) in the appendix, overall, the work conducted so far suggests very good now- and forecasting performance of Google search data. However, two technical questions are still up to debate. The first concerns the choice of the appropriate keyword searches: some authors simply use the variable of inter-est as the keyword (“job”, “car sales”, ...), while others start from the entire list of search categories provided by Google and subsequently reduce the number of queries applying statistical techniques such as principal component analysis. The second question is related to the time aggregation necessary for forecasting. Since the Google series is compiled on a weekly basis, whereas macroeconomic variables are mostly available on a monthly or quar-terly frequency, the Google series has to be aggregated.⁴ This is far from trivial: The week used by Google always ranges from Sunday to Sunday, hence one has to avoid overlapping data. However, as will become clear below, neither the keyword choice problem nor the time aggregation issue is relevant for our analysis.

In the context of inflation expectations, there is by now only one paper that employs internet search data. Guzmán(2011) uses a full set of 38 measures of inflation expectations for the U.S., including Google search requests for the word “inflation”, and compares their forecast performance with regard to future inflation.⁵ Importantly,Guzmán(2011) interprets Google search requests for inflation as a measure of revealed expectations: In her point of view, people only devote time for internet searches of inflation if they feel concerned of the future price development. Her analysis provides a number of interesting findings. Starting with long-run Granger causality tests of inflation expectations and actual future inflation, most of the expectation series are found to Granger-cause future prices changes, however, the Google series is the only variable that does not exhibit a feedback from actual inflation to ex-pected inflation. Next, following the standard rationality tests conducted byThomas(1999), Guzmán (2011) shows that the Google search data is biased but efficient if past inflation, oil prices, unemployment and money growth are tested individually. The most remarkable result, however, concerns the out-of-sample forecast performance: For the time span

Jan-4So far, mixed data sampling regression models (MIDAS) suggested byGhysels et al.(2005,2006) have not yet been used in the context of Google search data.

5The list of expectation measures consists of survey data of households, firms and professional forecasters, as well as expectations derived from a yield difference using the Treasury Inflation Protected Securities.

uary 2006 to October 2008, the root-mean-squared forecast error of Google search requests is considerably lower compared to all other expectation measures. Hence, it seems that us-ing internet search data works fairly well in the context of inflation expectations. However, Guzmán(2011)’s analysis should be treated with care. She only uses one keyword and ne-glects the potential problems caused by Google’s random sampling procedure (see below).

Also, she aggregates the originally weekly Google data to monthly series which drops a lot of information.

In a more general perspective, as we have outlined in the introduction, treating Google search requests as an alternative expectation measure is not the only possible interpretation.

Da et al.(2011) suggest to use Google search requests as a measure of revealed attention: If financial investors do not fully pay attention to news, they do not incorporate all available information in their investment decisions. One way of measuring investor attention con-sist of using the number of articles published in the news media, a route that has also been suggested by Carroll(2003) in the context of inflation expectations. If news coverage of a particular stock or inflation is high, it is assumed that this piece of information will soon or later reach all economic agents.Da et al.(2011), by contrast, argue that using Google search data to capture individuals’ attention is a much more direct and timely measure. In this context, Granka(2010) is the only paper so far that analyzes empirically the links between television and print media news coverage on the one hand, and Google search requests on the other. Comparing the decay of interest in the different media following political as well as sensational news, her results indicate that Google searches are more closely aligned to TV broadcast than to newspaper articles. In addition, she finds that Google users are rather quick to loose interest in political events.

Hence, following the literature, one might interpret Google data as a measure of the demand for information whereas the news media provide the supply of information. However, the link between Google searches on the one hand and expectations and behavior on the other hand is less clear-cut. In line with models of rational inattention (Sims, 2003) and sticky-information (Mankiw and Reis, 2003), one might expect that households’ inflation expec-tations are more rational, i.e. closer to the best-available forecast in an economy, if search intensity is high. To this effect, Google search requests might serve as a link between house-holds’ and professionals’ expectations, instead of a proxy of expectations themselves. This poses the question of what determines search intensity. While the individual’s demand for information might be driven by news coverage of future events in the media, individuals could also increase their search intensity for reasons that are entirely independent of their expectations of the future. To provide an example: Anvik and Gjelstad(2010) use a search-and-matching model in the labor market followingMortensen and Pissarides(1994) to mo-tivate their use of Google search data to predict the unemployment rate. In this context, the number of internet searches related to unemployment would capture the search intensity of workers. While people might increase their search intensity for job vacancies if they expect

higher unemployment in the future, there could also be various other reasons to look for a new job in the internet, such as dissatisfaction with one’s current job or salary which is entirely unrelated to the individual’s expectations of the future. A similar reasoning might be at work in the context of inflation expectations. People might search for inflation in the internet if they want to buy a house, an IPod, make a financial investment, or feel a general need to get information on the state of the economy as a whole. Hence, using Google search data as a one-to-one equivalent for expectations might be too simplistic. It is the purpose of this chapter to explore the various links between newspaper reports, Google search data, and inflation expectations in more detail.