Internet Search Data as Alternative Measure of Inflation Expecta- Expecta-tionsExpecta-tions

Whereas our previous analysis has been built on measuring inflation expectations with the help of survey data, Chapter (4) extends this analysis. Whereas survey data have proven to be useful in forecasting inflation (Ang et al., 2007) and in predicting individual investment decisions (Armantier et al.,2012), they also face a number of difficulties. Results can strongly depend on the exact question wording, which is particularly relevant with regard to inflation forecasts since respondents easily confuse price level and inflation rate depending on how they are asked (Bruine de Bruin et al.,2012,Dräger and Fritsche,2013). Moreover, designing and implementing a questionnaire consumes time and money, hence, existing surveys often face a small sample problem, both across time and respondents. Third, survey respondents might lack an incentive to state their best possible expectations due to the absence of finan-cial consequences or peer pressure. Moreover, if the same individuals participate repeatedly a survey, learning effects might result in much better predictions compared to individuals that do not take part in the survey. Finally, many countries still lack surveys that ask respon-dents to express their expectations in terms of a precise number or within predefined ranges.

Instead, qualitative answers are provided making it necessary to apply data transformations that depend on various, often restrictive assumptions (Nardo,2003).

In this chapter, we propose the use of Google search requests as a supplementary measure for inflation expectations. People increasingly turn to the internet if they feel the need to get more information on a certain topic. Compared to surveys, internet search intensity does not depend on framing effects stemming form question wording. Moreover, the number of searches comes as a by-product of users’ internet activities: if individuals use the Google web page in order to find information on a certain topic, they do so because they already feel the need to get informed, either because they are reluctant to seem uninformed in daily talks, or because they have a specific economic transaction in mind which makes it necessary to possess the latest news on inflation. Finally, since Google search data is available on a weekly basis, this means that internet search requests could serve as a supplement to the existing survey data which is often compiled on a monthly basis and only released with some time lag. This is of particular interest for monetary policy that seeks to monitor price developments as timely as possible.

We analyze U.S. data from January 2005 to May 2011 on households’ and professional fore-casters’ expectations measured via survey data, newspaper articles and television reports on inflation, and Google search requests for inflation. The contribution of this chapter is

twofold: First, we explore the news content of web searches on inflation. More precisely, we want to know whether search intensity evolves in a systematic way that can be attributed to real economic data. Note that Google searches might simply mirror the news coverage of inflation in the media, hence there might be no additional gain of using web searches in addition to the number of newspaper articles. To test whether Google searches are different, we compare the reaction of Google searches, TV reports and newspaper articles to changes in prices, variables describing the stance of monetary policy and lagged values of house-holds’ and professional forecasters’ expectations. In a second part, we take into account the various feedback effects among the news media, Google search requests and the inflation ex-pectations of households and professional forecasters by estimating Vector Autoregressive models.

Our results show that users’ demand for information can indeed be linked to economic fundamentals: Google search requests can be explained by price changes much better than media reports. Google users distinguish between headline and core inflation and they react asymmetrically: the demand for information increases if core inflation falls. In periods of historically high inflation rates, the number of search requests is significantly larger. More-over, internet users pay attention to central bank behavior: unscheduled conference calls as well as issued statements increase search intensity. In addition, we find a positive effect from households’ inflation expectations in the previous period on search requests: Google users seek for additional information if they predict prices to rise in the future. Higher infla-tion 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 arti-cles, and Google searches, while the feedback effect from expectations on web searches is rather small and estimated less precisely. Furthermore, the impulse response function 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’ ex-pectations. About 20% of the forecast error variance decomposition of households’ inflation expectations can be explained by Google search requests.

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