Measuring the Level and Uncertainty of Trend Inflation

Abstract

Firmly-anchored inflation expectations are widely viewed as playing a central role in the successful conduct of monetary policy. This paper presents estimates of trend inflation, based on information contained in survey expectations, the term structure of interest rates, and realized inflation rates. My application combines a variety of data sources at the monthly frequency and it can flexibly handle missing data arising from infrequent observations and limited data availability. In order to assess whether inflation expectations are anchored, uncertainty surrounding future changes in trend inflation-measured by a time-varying volatility of trend shocks-is estimated as well. Not surprisingly, the estimates suggest that trend inflation in the U.S. rose and fell again over the 1970s and 1980s, accompanied by increases in uncertainty. Considering the recent crisis, full-sample estimates of trend inflation fell quite a bit, but not too dramatically. In contrast, real-time estimates recorded sizeable increases of trend uncertainty during the crisis of 2007/2008, which have abated since then.

Measuring Price-Level Uncertainty and Instability in the United States, 1850–2012

“…For instance, Cogley, Primiceri, and Sargent (2010) investigate autocorrelation in the inflation gap, π t −μ t , and Cecchetti et al (2007) model log-volatility innovations as a two-state Markov process, thus allowing jumps in r t and q t . Kozicki and Tinsley (2012) and Mertens (2011) introduce survey data on inflation forecasts to aid identification, 14 Mertens (2011) and Stella and Stock (2012) develop multivariate forecasting models by incorporating data on other macroeconomic outcomes such as unemployment and the term structure of interest rates, 15 and Chan, Koop, and Poller (2013) introduce a priori bounds on the random walk component μ t . 16 Another worthy extension, especially for modeling nineteenth-century inflation dynamics, would be to consider adding a stationary component to the log price level.…”

Measuring Inflation Anchoring and Uncertainty: A U.S. and Euro Area Comparison

“…In particular, our measure is different to the one proposed by Mertens (), which reflects inflation uncertainty but does not take into account the distance between inflation expectations and the target.…”

“…This allows us to easily cast the model into a linear state‐space form, which is the required form of the model for the standard Kalman filter algorithm to be applied. This is a fundamental difference between our approach and alternative inflation models exhibiting stochastic volatility (see, e.g., Stock and Watson , Mertens ). Indeed, while the latter models entail closed‐form expressions for the first two conditional moments of inflation, the second‐order moments are nonlinear in the unobserved factors, which substantially complicates the model estimation.…”

“…, Speck ). Other measures include the response of (changes in) long‐term inflation expectations to (changes in) short‐term ones (Buono and Formai , Gerlach, Moessner, and Rosenblatt ), the precision around estimates of the level of inflation (Mehrotra and Yetman ), the volatility of shocks to trend inflation (Mertens ), and the closeness of average beliefs to the central bank's inflation target (Kumar et al. , Łyziak and Paloviita ).…”

Measuring Unobserved Expected Inflation

“…It is also similar to the study by Reis and Watson (), who specified a common factor model to decompose the change in prices of US consumer goods into three components, two of which measured relative price changes and a third that tracked general changes in inflation rates . Finally, it follows the work of Mertens (), who estimates trend inflation based on survey expectations, the term structure of interest rates and realized inflation. His model allows an assessment of how well inflation expectations are anchored.…”