Alternative Data News and Insight

Global supply shortages and pandemic-driven stimulus measures have been fueling prices in many major economies. Some economists are even starting to throw down terms like ‘stagflation’ and ‘wage-inflation spiral’. Are these price spikes temporary? Or are they a reflection of longer-term price trends?

With current concerns over rising inflation across major economies, we summarise two studies that anticipate official CPI figures ahead of their release. These studies use high-frequency alternative data sources, which are found to improve the forecast accuracy of inflation models and provide timely insights into global pricing dynamics.

When expectations for headline economic indicators are off-beat, economists sometimes point to the Keynesian notion of ‘animal spirits’ as the cause of error. Animal spirits can be loosely defined as the emotional and psychological drivers of economic decision making, that commonly diverge from models of rational, self-optimizing behaviors. But what if we could quantify and measure the animal spirits in headline indicators? Would it make forecasts for growth and inflation more accurate?

Academic research suggests that official CPI figures in the post-pandemic era have not been an accurate reflection of consumer prices. We summarise key findings from a recent study and outline the alternative data sources that can help build a more accurate gauge of inflation.

The growth of e-commerce has facilitated inflation tracking on a daily basis, namely from aggregating online pricing data. Moreover, the ubiquity of e-commerce in-itself is changing aggregate inflation dynamics. We believe product pricing datasets have a particularly potent use case in generating early inflation signals in opaque markets, such as LatAm, where official statistics are challenging to obtain. In this report, we review the academic literature that examines the behavior of online prices and its response to aggregate national shocks. We also outline where to find alternative sources of inflation data.