Measuring Unobserved Expected Inflation

Abstract

The aim of this study is to develop an eclectic but robust model that allows for a better measure of expected inflation and facilitates testing for all sorts of biases. Improving the measure of expected inflation is of critical importance for conducting monetary policy. In many circumstances, indicators of expected inflation move in opposite directions, and this divergence may be critical for the setting of the interest rate. I estimate the model for a special set of Israeli data via the Kalman filter methodology and then test for systematic biases, a better normalization of the model, liquidity problems and inflation risk -which could all be present in current measures of expected inflation.

Dynamic factor models

“…The Using disaggregate inflation data and unrestricted or restricted factor models can be useful in similar contexts. For example, see Andrade & Zachariadis (2016) for the importance of distinguishing between the price reaction for different types of shocks like macro and micro shocks and, in particular, the importance of distinguishing between global and local shocks; see Luengo-Prado et al (2018) in examining sectoral Phillips curves derived from multi-sector New Keynesian models; see Choi et al (2017) for a decomposition of the volatility of city-level inflation into two components: one reflecting nationwide shocks and one reflecting idiosyncratic regional shocks specific to each region or see Melnick (2016) in the context of improving the measurement of expected inflation.…”