Acceso usuarios
Nii Ayi Armah, Norman R. Sawson
Central banks regularly monitor select financial and macroeconomic variables in order to obtain early indication of the impact of monetary policies. This practice is discussed on the Federal Reserve Bank of New York website, for example, where one particular set of macroeconomic 'indicators' is given. In this article, we define a particular set of 'indicators' that is chosen to be representative of the typical sort of variable used in practice by both policy-setters and economic forecasters. As a measure of the 'adequacy' of the 'indicators', we compare their predictive content with that of a group of observable factor proxies selected from amongst 132 macroeconomic and financial time series, using the diffusion index methodology of Stock and Watson (SW, 2002a, b) and the factor proxy methodology of Bai and Ng (2006a, b) and Armah and Swanson (2010). The variables that we predict are output growth and inflation, two representative variables from our set of indicators that are often discussed when assessing the impact of monetary policy. Interestingly, we find that the indicators are all contained within the set the observable variables that proxy our factors. Our findings, thus, support the notion that a judiciously chosen set of macroeconomic indicators can effectively provide the same macroeconomic policy-relevant information as that contained in a large-scale time-series dataset. Of course, the large-scale datasets are still required in order to select the key indicator variables or confirm one's prior choice of key variables. Our findings also suggest that certain yield 'spreads' are also useful indicators. The particular spreads that we find to be useful are the difference between treasury or corporate yields and the federal funds rate. After conditioning on these variables, traditional spreads, such as the yield curve slope and the reverse yield gap are found to contain no additional marginal predictive content. We also find that the macroeconomic indicators (not including spreads) perform best when forecasting inflation in nonvolatile time periods, while inclusion of our spread variables improves predictive accuracy in times of high volatility.
