RDP 2016-02: Disagreement about Inflation Expectations 5. Time-series Variation in Disagreement

For each survey measure, there is substantial variation over time in the level of disagreement. In this section, we assess two main aspects of this time-series variation:

• First, the extent to which it can be explained by macroeconomic news surprises. Examining the response of disagreement to economic news provides a means to discriminate between the different models of expectations formation outlined in Section 2. As indicated by Table 1, the noisy-information model and the disagreement-about-means model do not predict a response of disagreement to macroeconomic news surprises. In contrast, the sticky-information model predicts disagreement to increase in response to macroeconomic news, because only a fraction of agents are attentive to news each period. (In the absence of further shocks, disagreement is predicted to then decrease over time.)
• Second, we formally examine whether disagreement is related to mean inflation expectations or realised inflation. This is more directly related to inflation anchoring; if episodes of increased disagreement are also associated with inflation outcomes that are (temporarily) outside of the target, or with higher mean expectations, then this suggests that disagreement might be indicative of weaker anchoring. However, as discussed earlier, the absence of information on long term expectations means we cannot rule out that consumers' long-term inflation expectations remain unchanged.

5.1 Response of Disagreement to Macroeconomic News

We construct a time series of ‘macroeconomic surprises’ for each of: real GDP growth, the unemployment rate, trimmed mean inflation, CPI inflation, fuel price inflation, import price inflation, and the Australian dollar trade-weighted index.^[11] These variables are chosen because they are typically used in inflation forecast models. While surprise changes in these variables are likely to lead to a revision in mean inflation expectations, their impact on forecast disagreement is less clear.

We proxy the surprise component of quarterly changes in macroeconomic variables by the residual of a univariate AR(4) model. Autoregressive model forecasts are a difficult benchmark to beat, so the autoregressive model residuals are likely to be a good measure of the surprise component of macroeconomic news releases. To facilitate comparison across variables, we scale the residuals of each series by their standard deviation.

To estimate the response of disagreement to macroeconomic news surprises, we run the following regression for each series of macroeconomic news surprises:

where σ_t,t+4 is disagreement in year-ahead forecasts and ω_j,t is the estimated surprise component of the change in variable j.^[12] We do not conduct this analysis with survey data from The Age, as the survey is run infrequently, restricting our ability to make inferences from the results. For the consumer survey, we use the standard deviation of the ‘underlying distribution’ estimated above. The estimated relationships between the macroeconomic shocks and forecast disagreement, γ_j,i, are plotted in Figure 9. Coefficients different from zero at the 5 per cent level of significance are denoted with a dot.^[13]

The results provide mixed support for the notion that surprise changes in macroeconomic variables increase forecast disagreement. With the exception of the consumer survey, there is statistically significant evidence that surprise changes in real GDP are associated with an increase in disagreement about future inflation: a one standard deviation surprise in quarterly real GDP growth causes a persistent 0.1 percentage point increase in forecast disagreement among unions, and a smaller but still notable effect among respondents to the RBA and Consensus Economics surveys.

However, disagreement in the consumer survey does tend to increase in response to all the other macroeconomic surprises included here. In particular, the results suggest surprises to trimmed mean inflation are associated with a persistent and statistically significant increase in disagreement among consumer survey respondents. There is also some evidence of a positive impact of trimmed mean inflation surprises on disagreement among professional forecasters and unions.

There is less evidence of a relationship between the surprise component of other macroeconomic releases and forecast disagreement. In terms of the models discussed in Section 2, the variation in forecast disagreement in response to some surprises provides tentative evidence against the baseline noisy-information model. The noisy-information model can explain disagreement among forecasters, but it does not predict a response of disagreement to macroeconomic news surprises. While the rise in disagreement in response to real GDP growth surprises is consistent with the sticky-information model, the absence of a response of most other variables is seemingly inconsistent with the model – disagreement is predicted to rise in response to a macroeconomic surprise in any variable that affects inflation forecasts.

The rise in disagreement in response to real GDP growth surprises could reflect forecasters having different beliefs about the slope of the Phillips curve, and so updating their inflation forecasts by varying degrees in response to changes in real GDP growth. Consistent with this, there is some evidence that surprise changes in unemployment raise disagreement, although we cannot reject there being no response.

5.2 The Relationship between Disagreement and Inflation

In Section 4 we observed some suggestive visual evidence of a positive relationship between inflation and disagreement in inflation expectations. For the United States, Mankiw et al (2004) find strong evidence of such a relationship, which they argue is consistent with the staggered adjustment sticky-information model of expectations formation. But the sticky-information model is symmetric with respect to increases and decreases in inflation, suggesting that disagreement should be high whether inflation rises above or falls below the midpoint of the RBA's inflation target band.

To test these possibilities formally, we regress the measures of inflation forecast disagreement on the level of headline CPI inflation and deviations of CPI inflation from the midpoint of the target band:

where π_t is year-ended CPI inflation at time t. The results, reported in Table 5, suggest that there is no relationship between inflation forecast disagreement and the level of year-ended CPI inflation. The absence of a response contrasts with Mankiw et al (2004), and may reflect the fact that the sample period used for the United States included a shift in the mean of inflation, following the Volcker disinflation, and our sample period mostly includes only the recent low-inflation period.

However, we find that disagreement does tend to be relatively high when year-ended CPI inflation deviates from the middle of the RBA's inflation target, except for the consumer survey measure. For the Consensus Economics and RBA survey measures, a 1 percentage point deviation in year-ended CPI inflation from the midpoint of the target is estimated to raise forecast disagreement by about 0.04 percentage points, and by about 0.1 percentage point for the year-ahead and medium-term union measures. While a rise in disagreement when current inflation deviates from 2½ per cent is consistent with the sticky-information model, it is also consistent with imperfect anchoring of inflation expectations, although such inference is necessarily very tentative because our data reflect short-horizon inflation expectations.

We have tested for a relationship between disagreement and realised inflation, but the sticky-information model also predicts a relationship between disagreement and mean inflation expectations. Under this model, any news that causes attentive agents to revise their expectations will also increase disagreement. For the consumer measure, Figure 6 provides strong graphical evidence of co-movement between disagreement and mean inflation expectations. We test for such a relationship in each survey measure by respecifying Equation (6) as follows:

where is mean inflation expectations in period t, is mean inflation expectations over the full sample period for each measure of inflation expectations, and all other variables are defined as before.

For the professional economist survey measures, the relationship between disagreement and expected inflation (Table 6) is qualitatively similar to the relationship between disagreement and CPI inflation. For the union officials and consumer survey measures there is evidence of a significant positive relationship between disagreement and mean inflation expectations.

For the consumer measure in particular, the relationship is statistically and economically significant, with each percentage point increase in inflation expectations associated with a one-third of a percentage point increase in disagreement. If this relationship holds in the period before the sample of unit record data starts, it suggests that disagreement in consumer inflation expectations fell sharply over 1990–91, alongside the mean and median expectations (for which data are available). This could explain the absence of a trend decline in disagreement for this measure, noted in Section 4.2.^[14]

Footnotes

The two CPI series exclude interest charges prior to the September quarter 1998 and are adjusted for the tax changes of 1999–2000. Removing the GST effect is necessary as the policy was anticipated, so it should not be captured as a macroeconomic surprise. [11]

For the regressions with disagreement in Consensus Economics expectations, we also include a set of quarterly dummy variables to remove the mechanical effect of changes in the forecast horizon on uncertainty. [12]

Pagan (1984) shows that under the null hypothesis that γ_j,i = 0 the standard errors do not need to be adjusted for the use of a generated regressor. [13]

If this is the case, it indicates that consumers were more attentive to the taming of inflation and/or introduction of inflation targeting in the early 1990s compared with professional forecasters and unions. This runs counter to our prior beliefs. [14]