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posted on 27 September 2015

Can We Rely On Market-Based Inflation Forecasts?

from the San Francisco Fed

-- this post authored Michael D. Bauer and Erin McCarthy

A substantial decline in market-based measures of inflation expectations has raised concerns about low future inflation. An important question to address is whether the forecasts based on market information are as accurate as alternative forecasting methods. Compared against surveys of professional forecasters and other simple constant measurement tools, market-based inflation expectations are poor predictors of future inflation. This suggests that these measures contain little forward-looking information about future inflation.

The Federal Reserve's dual mandate requires monetary policy to aim for both maximum employment and price stability. Although employment has recovered since the recession, inflation has consistently remained below the Fed's 2% longer-run objective. Because expectations of future inflation play an important role in determining current inflation, decreases in measures of inflation expectations based on market prices have raised some concerns. For example, between June 2014 and January 2015, one-year inflation swap rates, which measure market-based expectations of inflation in the consumer price index (CPI) one year ahead, dropped over 2.5 percentage points. Large decreases were also observed in breakeven inflation rates, the difference between yields on nominal and inflation-indexed Treasury securities, known as TIPS.

Market-based measures of inflation expectations are calculated from the prices of financial securities. Their advantage is that they are readily available at high frequency and therefore are widely monitored. However, they reflect not only the public's inflation expectations but also other idiosyncratic factors that affect market prices, which are difficult to quantify. For example, they include a risk premium to compensate investors for inflation uncertainty and are affected by changes in liquidity, unusual demand flows, and, more broadly, "animal spirits" that change prices but are unrelated to expectations (see Bauer and Rudebusch 2015). Hence it is unclear how much useful information they provide, and how much one should pay attention to these rates when forecasting inflation.

If market-based inflation expectations provided accurate inflation forecasts, then one surely would want to pay close attention to their evolution. In this Economic Letter, we evaluate their performance in comparison with a variety of alternative forecasts for CPI inflation.

Inflation forecasts using market prices

There are two types of market-based measures that one can use to gauge inflation expectations: TIPS breakeven inflation rates and inflation swap rates. Both of these reflect market-based expectations for future headline CPI inflation that includes food and energy prices. TIPS breakeven inflation rates are reliable only at longer maturities, such as five- and ten-year horizons. Since TIPS only started trading more broadly in the early 2000s, there simply are not enough data to analyze the forecast accuracy of these rates.

In contrast, inflation swap rates are consistently available for all annual horizons from one to ten years, and contracts for the next one and two years are very liquidly traded. In our exercise we are particularly interested in these short horizons, since these are most relevant for practical inflation forecasting. Though inflation swaps were introduced somewhat later, in the mid-2000s, focusing on short horizons of one and two years gives us a large enough sample to form some tentative conclusions about their forecast accuracy.

Figure 1 shows the evolution of one- and two-year inflation swap rates, and five- and ten-year TIPS breakeven inflation rates since the beginning of 2013. The declines in the second half of 2014 were quite dramatic and visible, to different extents, in all four series. Notably, these measures have again declined quite substantially more recently, and the ten-year TIPS breakeven rate has reached its lowest level since 2009.

Figure 1

Market-based measures of inflation expectations

Market-based measures of inflation expectations

We construct market-based inflation forecasts from one-year and two-year inflation swap rates. While one can try to account for risk premiums and extract actual inflation expectations from market prices - for example, by using models as in Christensen, Lopez, and Rudebusch (2010) - we use raw, unadjusted market rates. In this way our results are not dependent on the choice of a specific model, and are based on measures of inflation expectations that are available to any market participant and professional forecaster.

Our measure for forecasting inflation one year in the future is the yield on the one-year inflation swap contract. The two-year-ahead inflation forecast is the one-to-two-year forward rate, which corresponds to the forecast from one to two years in the future and is calculated from the one-year and two-year swap yields.

Alternative inflation forecasts

We compare the market-based inflation predictions with four other forecasts that are based on surveys or current inflation rates or use a simple constant corresponding to the Fed's inflation target.

For the survey-based forecasts we use data from the Survey of Professional Forecasters (SPF) and the Blue Chip Financial Forecasts. Surveys are an important benchmark when comparing inflation forecasts. These predictions are made by professional forecasters who aim to appropriately incorporate all information available. Past research has shown that survey forecasts tend to perform best among different competing inflation forecasting methods (see Ang, Bekaert, and Wei 2007 and Faust and Wright 2013). Forecasts from the SPF are based on median predictions of headline CPI inflation one year into the future from the end of the fourth quarter, which we transform into forecasts with the appropriate forecast horizon by taking weighted averages. Similarly, for the Blue Chip forecasts comparison we construct a weighted average of the annual predictions, based on the quarter in which the prediction was made. Due to the limited horizon of the Blue Chip survey forecast, we can only construct one-year forecasts.

In addition to the survey forecasts, we use no-change forecasts based on current values of CPI inflation. Inflation is a highly persistent time series, meaning that current values are closely related to past values. Because of this, another plausible forecast method that has performed quite well is a simple no-change forecast. We use the current core CPI inflation rate, which excludes the volatile prices of food and energy goods, as the no-change forecast for future CPI inflation. Core inflation is less volatile than headline inflation and hence is generally more informative about the true inflation trend in the economy.

Our last comparison uses a simple constant inflation rate motivated by the fact that the Federal Reserve targets a constant 2% inflation rate over the long run. This target is based on the price index for personal consumption expenditures (PCE). To convert this for our comparison, we use the average spread between PCE and CPI inflation over recent years, which is 0.3 percentage points, and hence forecast a CPI inflation rate of 2.3% (see Bauer and Christensen 2014).

Results on forecast accuracy

As much as possible, we use an identical sample period and frequency for all forecasts. The frequency is quarterly, since the SPF is available only at the quarterly frequency. To correctly line up all the forecasts, we use the end of the first month of each quarter as the relevant forecasting date, since that is when the survey respondents submit their forecasts for the SPF. The series of forecasts starts in July 2005 and goes through July 2013, and the most recent inflation data used in the forecast exercise is from July 2015.

Figure 2 summarizes the forecast accuracy of each method, measured by the so-called root-mean-squared error, the square root of the average squared forecast error. Higher numbers (bars) indicate larger forecast errors on average and hence worse forecast accuracy. The forecast methods are sorted from most to least accurate in the figure.

Figure 2

Average size of forecast errors for future inflation

Average size of forecast errors for future inflation

For the one-year-ahead forecasts, the results indicate that market-based forecasts perform worst. The constant forecast delivers the best performance. The two survey-based and the no-change forecasts have similar accuracy but are all slightly worse than a simple constant forecast. For the two-year forecasts, the market-based predictions also perform the worst, although the differences in performance are slightly smaller. The accuracy of market-based forecasts is comparable to that of the no-change forecast. The SPF and constant forecasts perform best.

In addition to the full sample, we analyzed two subsamples to check whether our results might have been driven by observations over a specific time period. The first subsample includes the years 2005 - 08 and the second includes 2009 - 13. Our results show that the ranking of the forecast performance is very similar between the two subsamples and in line with the results for the full sample. The market-based inflation forecasts consistently perform the worst. Since the results are robust, this suggests that no particular episode, such as the Great Recession or the subsequent recovery, drove our results.

We can get a better understanding of the performance of the different forecasts by visualizing them over time. Figure 3 compares the one-year-ahead forecasts for the five different methods with the actual headline CPI inflation reported a year later. The forecasts are aligned with the reported inflation rate so that a perfectly accurate forecast would lie on top of the line for actual CPI inflation. The figure illustrates that market-based forecasts are off target mainly because they tend to be highly correlated with past inflation. It appears that market participants take a very strong signal from current inflation when forming expectations of future inflation, that is, they appear to simply extrapolate from the current headline rates. This is consistent with Faust and Wright (2013), who note that "while these short-term inflation swap rates may be telling us something about near-term inflation expectations, they appear to move almost in lockstep with past inflation." Unexpected shocks to inflation caused huge errors in all forecasts except for the constant, which appears to be the main reason for its good performance.

Figure 3

Forecasts and actual CPI inflation

Forecasts and actual CPI inflation


We find that market-based measures of inflation are poor predictors of future inflation. In particular, they perform much worse than forecasts constructed from survey expectations of future inflation, which incorporate all the information used by professional forecasters. Interestingly, a simple constant inflation rate corresponding to the Federal Reserve's 2% inflation target consistently performs best. While our analysis is based on a short sample that displays a lot of volatility during the Great Recession, our results appear quite robust as they are consistent across two subsamples.

Our results add to the discussion about how much attention policymakers and professional forecasters should pay to market-based inflation forecasts. These measures mostly reflect current and past inflation movements, and do not contain a lot of useful forward-looking information. Idiosyncratic market forces and inflation risk premiums appear to be important drivers of market-based inflation expectations. Overall, it is important to keep this caveat in mind when interpreting market-based inflation expectations.

About the Authors

Michael D. Bauer is a senior economist in the Economic Research Department of the Federal Reserve Bank of San Francisco.

Erin McCarthy is a research associate in the Economic Research Department of the Federal Reserve Bank of San Francisco.


Ang, Andrew, Geert Bekaert, and Min Wei. 2007. "Do Macro Variables, Asset Markets, or Surveys Forecast Inflation Better?" Journal of Monetary Economics 54(4), pp. 1,163 - 1,212.

Bauer, Michael D., and Jens H.E. Christensen. 2014. "Financial Market Outlook for Inflation."FRBSF Economic Letter 2014-14 (May 12).

Bauer, Michael D., and Glenn D. Rudebusch. 2015. "Optimal Policy and Market-Based Expectations." FRBSF Economic Letter 2015-12 (April 13).

Christensen, Jens H.E., Jose A. Lopez, and Glenn D. Rudebusch. 2010. "Inflation Expectations and Risk Premiums in an Arbitrage-Free Model of Nominal and Real Bond Yields." Journal of Money, Credit, and Banking 42(6), Supplement, pp. 143 - 178.

Faust, Jon, and Jonathan H. Wright. 2013. "Forecasting Inflation." In Handbook of Economic Forecasting, vol. 2A, eds. Graham Elliott and Allan Timmermann. Amsterdam: North Holland.

Source september/market-based-inflation-forecasting-and-alternative-methods/


Opinions expressed in FRBSF Economic Letter do not necessarily reflect the views of the management of the Federal Reserve Bank of San Francisco or of the Board of Governors of the Federal Reserve System.

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