Inflation Expectations and Media Reports

but to the average prediction of the general public. This can be motivated with the pres-ence of a learning process in which households share their beliefs and thus converge to the cross-sectional average (Malmendier and Nagel, 2013). As an alternative approach, we fit a pseudo panel defining cohorts using the age of survey participants.

Non-Linear Media Effects The original version of the epidemiology model in equation (2.2) including media coverage is formulated such that the amount of news coverage affects the degree of updating in a non-linear way. With the exception ofLamla and Sarferaz(2012), the literature has bypassed the non-linear framework by focusing on the linear transforma-tion in equatransforma-tion (2.3). This formulatransforma-tion, however, can be too restrictive. Households might not always react to the news media in the same way, instead, they might either miss a sin-gle article about inflation if the general interest in inflation is low. Likewise, news coverage might reach a satiation level beyond which readers ignore additional articles on inflation.

Finally, the degree of non-linearity and the attention and satiation level might differ across households, depending on whether households generally follow the news or not. In order to test for non-linear media effects, we estimate equation (2.2) with the Smooth Transition Autoregressive model.

a rather conservative truncation rule of +/- 30% throughout the analysis.⁹ Data from the Michigan Survey is available on a monthly basis from January 1978 onwards.

As regards professional forecasters’ expectations, we employ data from the Survey of Pro-fessional Forecasters (SPF) which is available since the third quarter of 1981.¹⁰ Each quarter, about fifty economists mainly working in nonfinancial and financial firms are asked to state their quarter-by-quarter forecast for the CPI inflation rate over the next year. These quarterly forecasts are transformed into a one-year-ahead prediction via a geometric average:

π^exp,prof_t,t+4 = 100 (

1 + π_t,t+1^exp

100 1 + π_t+1,t+2^exp

100 1 + π_t+2,t+3^exp

100 1 + π^exp_t+3,t+4 100

^1/4

−1 )

(2.5)

In what follows, the quarterly SPF one-year-ahead forecast is transformed into monthly fore-casts by linearly interpolating the missing months. We choose to conduct our analysis on monthly data instead of computing quarterly averages of the Michigan data for the follow-ing reasons. First, we want to keep as many observations as possible. Second, since our focus is on explaining households’ expectations, we did not want to impose too many a priori restrictions on our dependent variable. Finally, the news media are relatively fast in emphasizing certain topics, hence, the actual impact of news media coverage on the expec-tation formation of households might be downplayed by an analysis using quarterly data.

As a cross-check of the interpolation, we compare the SPF series with data fromConsensus Economics, a survey conducted on a monthly basis.¹¹ Figure (A.1) in the Appendix plots our interpolated SPF series together with the Consensus forecast. With the exception of the financial crisis in 2008/2009 where the volatility of the Consensus forecast is much higher compared to the SPF, the two series move quite closely together. Hence, we are confident that our results are not affected by interpolating the quarterly SPF forecast.

Due to the availability of the micro data of the Michigan survey, our analysis covers the time span January 1980 - November 2011.¹² Figure (2.1) shows households’ inflation ex-pectations measured by the Michigan survey, professional forecasters’ price predication, and the annual change of the seasonally adjusted CPI index. They gray shaded areas de-note recession periods as dated by the NBER.¹³ We can roughly distinguish four periods.

From the beginning of the sample until the mid 1990s, the two series of expected inflation

9Due to the dependence of the mean on different truncation rules, the median might be a more robust measure of the general public’s inflation forecast. However, since the theoretical model derives predictions only for the mean, we also stick to this measure in the empirical analysis. Furthermore, whereas the mean is typically 1 percentage point higher than the median, the two series move very closely together.

10For data download and further information, seeSPF.

11SeeConsensusfor details. We did not use the Consensus survey in the analysis since it is subject to a fee and covers a shorter time span than the SPF.

12In order to include the missing data from the SPF in 1980, we followLuoma and Luoto(2009) and proxy the CPI forecast with the prediction for the GDP deflator.

13SeeNBERfor details.

moved fairly closely together. Afterwards, households’ expectations shifted upwards and constantly stayed above the forecast of experts but the two series still behaved rather simi-larly. Since 2003, households’ forecast trended upwards in line with the rising inflation rate, whereas experts continued to expect an inflation rate of about 2%. Finally, households’ ex-pectation fluctuated a lot since the beginning of the financial crisis, while the prediction of experts remained rather constant. As we will discuss in more detail below, these different sub-periods correspond to those found by structural break tests of the epidemiology model which are highlighted by the vertical lines in the graph.

Figure 2.1: Households’ and Professional Forecasters’ Inflation Expectations

−2 0 2 4 6 8 10 12 14 16

1980 1985 1990 1995 2000 2005 2010

π

π

π

Note: The graph shows the mean inflation expectations of households (π^exp,hht ), and of pro-fessional forecasters (π^exp,prof_t ), together with the annual inflation rateπt. The gray shaded areas denote NBER recessions, and the vertical lines indicate the structural breaks in 1992:12, 2003:06 and 2007:01 found in applying the QLR-test to equation (2.6).

In order to measure media coverage of inflation, we follow Carroll (2003) and Pfajfar and Santoro(2013) and count all articles published inThe New York TimesandThe Washington Post that contain words with the root “inflation”. The corresponding articles can be accessed in the database Lexis Nexis¹⁴ and are available on a monthly basis since January 1980. This way of measuring news coverage of inflation has the advantage that the data is costless and readily available, but also suffers from some limitations. First, the automatic search procedure ofLexis Nexis does not allow us to detect whether an article containing the word

14SeeLexis Nexisfor details.

“inflation” is actually about current or future price developments or refers to an historical episode unrelated to the present situation. Second, we cannot separate press reports from opinions, or capture whether an article describes the current inflationary (or deflationary) environment as problematic. However, we can compare our media measure with a more sophisticated news series compiled by the media research instituteMedia Tenor¹⁵. This data is manually collected and adjusted and thus does not suffer from the problems of the Lexis Nexis series. Comparing the two news series in Figure (A.2) in the Appendix reveals that the differences are not too large, overall, we find a correlation of 0.7. We do not use the Media Tenor series in this chapter since it is only available since January 1998, however, due to the close connection of the series with the Lexis Nexis measure, we are confident that our results are not affected too much by the measurement problems of our news series.

Next, we have to scale the media data in order to rule out that a decreasing number of articles on inflation is simply due to the fact that the size of the newspaper is shrinking.

FollowingCarroll(2003), we divide the number of news reports by the maximum number of articles on inflation published in any quarter of the sample. While this might not fully take into the effect of a shrinking newspaper size, our series is quite close to an alternative scaling procedure used byPfajfar and Santoro (2013). As it is shown in Figure (A.2) in the Appendix, dividing the number of articles on inflation by the total number of articles in each quarter does lead to virtually unchanged results. Plotting our news series in Figure (2.2) shows that in general media coverage of inflation moves together with the inflation rate, albeit the correlation varies slightly over time.

15SeeMedia Tenorfor details.

Figure 2.2: News Coverage of Inflation

−5 0 5 10 15

1980 1985 1990 1995 2000 2005 2010

Media

π

Note: The graph shows the number of news reports on inflation published inThe New York TimesandThe Washington Post, together with the annual inflation rateπt. The news series is scaled by its maximum value. The gray shaded areas denote NBER recessions, and the vertical lines indicate the structural breaks in 1992:12, 2003:06 and 2007:01 found in applying the QLR-test to equation (2.3).