Luis Uzeda is a Senior Economist of the Real Economic Activity division in the Canadian Economic Analysis Department. His research interests are in Applied Macroeconomics, Bayesian Econometrics and Time Series Analysis. Prior to joining the Bank, Luis held positions in the Research Departments of the Reserve Bank of Australia and the Reserve Bank of New Zealand. He holds a Ph.D. in Economics from the Australian National University.
The rise in inflation in 2021–22 sparked a growing literature and debate over the causes of the surge as well as the near- and medium-term path for inflation. This review offers three key messages.
We propose a new empirical framework that jointly decomposes the conditional variance of economic time series into a common and a sector-specific uncertainty component. We apply our framework to a disaggregated industrial production series for the US economy. We identify unexpected changes in durable goods uncertainty as drivers of downturns, while unexpected hikes in non-durable goods uncertainty are expansionary.
The goods and services sectors have experienced considerably different dynamics over the past three decades. Our goal in this paper is to understand how such contrasting behaviors at the sectoral level affect the aggregate level of trend inflation dynamics.
We introduce a new class of time-varying parameter vector autoregressions (TVP-VARs) where the identified structural innovations are allowed to influence — contemporaneously and with a lag — the dynamics of the intercept and autoregressive coefficients in these models.
Implications for signal extraction from specifying unobserved components (UC) models with correlated or orthogonal innovations have been well investigated. In contrast, the forecasting implications of specifying UC models with different state correlation structures are less well understood.
