Real-Time Density Nowcasts of US Inflation: A Model-Combination Approach

Abstract

We develop a flexible modeling framework to produce density nowcasts for US inflation at a trading-day frequency. Our framework: (1) combines individual density nowcasts from three classes of parsimonious mixed-frequency models; (2) adopts a novel flexible treatment in the use of the aggregation function; and (3) permits dynamic model averaging via the use of weights that are updated based on learning from past performance. Together these features provide density nowcasts that can accommodate non-Gaussian properties. We document the competitive properties of the nowcasts generated from our framework using high-frequency real-time data over the period 2000-2015.

Tracking activity in real time with Google Trends

“…Where the growth rate of GDP on the same quarter of the previous year ( ) 6 is modelled as a non-linear function of the year-over-year log-difference of quarterly averages of search volume indices ( , ) for categories (indexed by ) and country dummies ( ), plus some white noise . Second, the function ̂ estimated from the quarterly model is applied to the weekly Google Trends series, assuming that this relationship is frequency-neutral, in order to yield a weekly tracker:…”

“…The dependent variable is GDP (expenditure approach) in growth rate compared to the same quarter of the previous year, seasonally adjusted, at a quarterly frequency, taken from the OECD Quarterly National Accounts (QNA) 6. For the United Kingdom and Canada, monthly GDP series are available and were used along with monthly logdifferences of Google Trends series 7.…”