Micro Level Data of Households’ Inflation Expectations

Figure 2.2: News Coverage of Inflation

−5 0 5 10 15

1980 1985 1990 1995 2000 2005 2010

Media

t

π

t

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).

Table 2.1: Summary Statistics - Micro Data - Michigan Survey

Total News Heard News Heard: Bad News: Good News:

Inflation Infaltion Inflation

N 233361 137480 15498 3126 12456

%N / N total 100.0 58.9 6.6 1.3 5.3

N missing 22031 10399 1291 128 1166

% missing / N total 9.4 4.5 0.6 0.1 0.5

% missing / N 9.4 7.6 8.3 4.1 9.4

% extreme / N nonmissing I 5.3 5.7 23.0 80.7 28.6

% extreme / N nonmissing II 1.1 1.9 16.7 79.2 21.0

% females 0.5 0.5 0.5 0.4 0.5

∅age 45.4 45.5 44.5 42.0 45.1

∅income 45120.6 50173.1 49255.5 49378.4 49090.0

∅education 13.5 14.0 14.1 14.6 14.0

∅π^exp 4.6 4.5 6.1 3.4 6.8

∅π^exp,sd 5.8 5.7 6.4 4.8 6.6

∅GAP SQ^{SP F} 30.9 29.0 37.0 22.0 41.0

Note: The table provides summary statistics for all survey participants (“Total”), and those who have stated to have heard news about changes in the economy (“News Heard”), about inflation (“News Heard: Inflation”), as well as bad and goods news on inflation. Expected inflation rates are truncated at +/−30%. N denotes the total number of responses, N/N totalgives the fraction of answers with respect to the total number of survey participants. N missingsums the number of missing responses, and%missing/N total, and%missing/Nshows the fraction of missing answers relative to the total number of responses and the number of answers in each category (news heard, news heard: inflation, ...). % extreme/N nonmissing I/II computes the percentage of extreme answers relative to the number of nonmissing responses, where inI, extreme answers are defined as expected inflation rates>15%and<−5%, whereasIIapplies+30%as the upper limit. The rows%f emales,

∅age,∅income,∅educationshow the average number of females, as well as the average age, income and education of each category, where education is defined as the number of years in school. Finally, the last three columns show the average expected inflation rate, the average standard deviation of expected inflation, and the squared expectation gap with respect to professional forecasters surveyed in the SPF. Sample: 1980:01-2011:11.

The summary statistics reveal a number of interesting features. First, we note that out of a to-tal of 233,361 survey responses over the time period 1980:01-2011:11, only a fraction of 59%

claims to have heard about changes in economic conditions during the previous months.

Considering only news about inflation, this fraction drops to 6.6%. Second, looking at the number of missing responses reveals that in total, about 10% do not answer the question on inflation expectations. The fraction of non-responses slightly drops if participants claim to have heard news about changes in the economy and about inflation and declines further for those who have heard bad news about inflation. While this finding might be taken as informal evidence that people who follow the news are better able to give a precise estimate of future inflation, having heard good news increases the fraction of non-responses. Third, we compute the percentage of extreme answers provided by survey participants relative to the number of non-missing values, defining both an estimate of future inflation of below

−5% and above +15%, as well as below −5% and above+30% as an extreme answer. The results are fairly surprising. Whereas across all households, about 5% of those who have given an estimate of future inflation end up choosing extreme values, this number does not fall if we select only those individuals who have heard about economic news. Taking only those who have heard news about inflation, 23% give extreme answers, whereas for negative news about inflation, even a total of 80% (!) provide estimates for future inflation beyond −5% and +15%. This latter result is in contrast to the general hypothesis of the epidemiology model or the sticky information model, namely that better informed house-holds should give better expectations. While we seek to explore this issue further below, at the moment, we can only come up with a suggestive explanation. Instead of expecting that individuals seek to improve their forecast in response to having received bad news, it might rather be the case that bad news frighten individuals leading to extreme forecasts.¹⁶ Fourth, we check whether a different sociodemographic background influences households’

perception of economic news. As it turns out, this is not the case: For each news category, we find an equal amount of male and female respondents, an average age of about 45 years, and an income level of about $50,000. Moreover, the the level of education does not seem to increase households’ attention to economic news. Finally, for each news category, we com-pute the average expected inflation rate over time. Compared to all households, the mean expected inflation rate is higher if participants have heard news about inflation, lower for bad news on inflation and highest for good news on inflation.¹⁷ The same holds true for the standard deviation, and for the expectation gap defined as the squared difference between households’ and experts’ inflation forecast.

In order to analyze the differences in inflation expectations between households with dif-ferent news perceptions in more detail, we compute the root mean squared forecast error (RMSE) usinge^exp,prof_t = π_t^exp,prof −πt+12.¹⁸ This allows us to assess whether experts are in-deed better in forecasting inflation than households, and whether survey participants who claim to have heard news about inflation are better in predicting future price changes com-pared to all households. Given that we do not know a priori whether survey participants try to forecast headline or core inflation, we compute the forecast errors for both series.

Moreover, we calculate the forecast error for different subsamples classified with the help of structural break tests to be discussed below.

16Using the pseudo panel dimension of the Michigan survey,Dräger and Lamla(2013b) andPfajfar and San-toro(2013) have indeed found that having heard bad news on inflation increases households’ forecast error.

In our paper, however, we did not want to use households’ news perception as explanatory variable, but instead investigate whether households who have heard about inflation show different updating behavior and reaction to media reports. As we will show below, households who have heard bad news on inflation adjust faster to the best available forecast and are also more receptive to media reports. Since the forecast error of these households is higher compared to other survey participants, our results suggest that this can be explained by a false adjustment to media reports.

17Remember that these averages are computed with truncated data.

18Results are virtually the same if the mean absolute error is used. Detailed tables are available on request.

Overall, we find that experts are indeed better than households in forecasting inflation.¹⁹ This holds true for forecast errors with respect to headline inflation given in Table (2.2) and with respect to core inflation in Table (2.3). Only between 1980:01 and 1992:12, experts are slightly worse in predicting headline inflation compared to all survey participants. More surprisingly, however, we find that households who have perceived news about inflation are worse in predicting future price changes compared to all households. And the largest errors are made by those who have heard bad news on inflation, which corresponds to our earlier finding that this group of households also has the highest fraction of extreme re-sponses. This result might partly been driven by the low number of participants stating to have heard news about inflation. However, we also do not observe a significant improve-ment of expectations for households having heard news about economic issues in general, where the number of responses is considerably larger. Next, we observe that the forecasting ability varies over time. For both experts and the full sample of households, the forecast er-ror reaches its lowest value between 1993:01 and 2003:06, a time period where the level and in particular the volatility of the inflation rate have been low. Finally, the pattern of forecast errors is unaffected by the choice of the inflation rate, with the exception of the fact that both households and experts make lower errors if the less volatile core inflation is used.

Table 2.2: Forecast Precision: RMSE - Headline Inflation

80/1- 80/1- 93/1- 03/7- 07/2-11/11 92/12 03/6 07/1 11/11

e^exp,spf_t 1.32 1.43 0.80 1.18 2.04

e^{exp,hh agg}_t 1.78 1.59 1.39 1.31 3.19 e^{exp,hh all}_t 1.64 1.34 1.25 1.30 3.15

e^{exp,hh nh}_t 1.62 1.33 1.16 1.27 3.17

eexp,hh ninf l

t 2.29 1.97 2.30 1.52 3.54

eexp,hh ngood

t 2.44 2.16 2.07 1.94 4.08

eexp,hh nbad

t 3.03 2.73 3.33 1.61 4.02

mean(πt) 3.63 5.25 2.52 2.93 2.22 var(π_t) 6.69 10.18 0.43 0.68 3.38 Note: The RMSE is defined as e_t = q1

T

P π_t,t+12^exp −πt+12

2

. e^{exp,hh agg}_t denotes the forecast error computed with the cross-sectional mean of households’ inflation expectations as provided by the Michigan University. By contrast, e^{exp,hh all}_t uses the cross-sectional mean computed from the micro data using the truncation rule +/−30%.mean(πt)gives the average inflation rate, andvar(πt)the variance.

19Note that we use two series for the average inflation expectation of all households. e^{exp,hh agg}_t denotes the forecast error computed with the cross-sectional mean of households’ inflation expectations as provided by the Michigan University. By contrast,e^{exp,hh all}_t uses the cross-sectional mean computed from the micro data using the truncation rule+/−30%.

Table 2.3: Forecast Precision: RMSE - Core Inflation

80/1- 80/1- 93/1- 03/7- 07/2-11/11 92/12 03/6 07/1 11/11

e^exp,spf_t 0.75 1.01 0.49 0.31 0.59

e^{exp,hh agg}_t 1.40 1.05 1.24 1.50 2.42 e^{exp,hh all}_t 1.31 1.01 1.06 1.44 2.32

e^{exp,hh nh}_t 1.29 1.05 0.95 1.40 2.31

eexp,hh ninf l

t 2.02 1.56 2.17 1.77 2.99

eexp,hh ngood

t 2.20 2.19 1.92 1.52 3.20

eexp,hh nbad

t 2.79 2.19 3.24 2.00 3.75

mean(π_t) 3.66 5.71 2.56 2.03 1.77 var(πt) 6.09 7.36 0.18 0.24 0.32 Note: The RMSE is defined as et = q1

T

P π_t,t+12^exp −π_t+12²

. e^{exp,hh agg}_t denotes the forecast error computed with the cross-sectional mean of households’ inflation expectations as provided by the Michigan University. By contrast, e^{exp,hh all}_t uses the cross-sectional mean computed from the micro data using the truncation rule +/−30%.mean(π_t)gives the average inflation rate, andvar(π_t)the variance.

Im Dokument Media Reports and Inflation Expectations (Seite 49-53)