Abstract
This paper constructs and analyzes core inflation indicators for Saudi Arabia for the period of March 2012 to May 2014 using two alternative approaches: the exclusion method (ex food and housing/rent) and the statistical method. The findings of the analysis suggest that the ex food and housing/ rent inflation is more volatile than the overall CPI inflation over the sample period. In contrast, the statistical core inflation is relatively more stable and less volatile. Moreover, the ex food and housing/rent inflation is only weakly correlated with headline inflation, whereas the statistical core inflation exhibits a stronger correlation. This combination of lower volatility and higher correlation with headline inflation makes the statistical method a much better choice for policymakers. From a monetary policy standpoint, using a bundle of core inflation measures, including both properly constructed exclusion and statistical methods, is more desirable, especially when variation across measures is widespread, as is the case in Saudi Arabia.
Highlights
We find that the ex food and housing/rent inflation has the lowest mean value, and it is more volatile than both headline inflation and statistical core inflation
This study analyzes core inflation measures for Saudi Arabia based on two alternative methods: (a) a traditional exclusion-based index that excludes food and housing/rent inflation; (b) a statistical core inflation measure that uses an innovative general dynamic factor model methodology
The findings suggest that there is considerable variation between the two core inflation measures
Summary
The dynamic factor model is based on the theory that each variable in the dataset has two orthogonal components whose values can be determined only by decomposition using statistical analysis3 Such components are: (i) a common component that is highly correlated with the other macro variables that are included in the analysis and contribute to the new index; (ii) the idiosyncratic component, which is unique for each individual variable and has no effect on the other variables that are part of the index (see Cristadoro et al 2005). Our new core inflation indicator is based upon the common shared information that is embedded in the cross-section and time series characteristics of the variables in the dataset (see Wynne 2008). This GDFM-based methodology uses two distinct smoothing procedures. These movements are shared with the other variables used, their high frequency oscillation means that they do not enter into the determination of the core inflation rate
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