Nowcasting inflation in India with daily crowd-sourced prices using dynamic factors and mixed frequency models

ABSTRACT

In this paper, we forecast short-term monthly headline retail inflation in India using daily crowd-sourced food prices and high frequency market-based measures by employing dynamic factors and mixed frequency models. We demonstrate that the forecast using the proposed approach outperforms the forecasts using the conventional approaches. The retail inflation rate for the last month is usually released around the mid of the current month. Hence, there is a delay in the availability of this critical metric. In this context, we leverage the intra-period high frequency data as it becomes available to improve forecast (nowcast) performance, which can be made available much before the official data release.

KEYWORDS:

• Crowdsourced prices
• dynamic factor models
• MIDAS regression
• nowcasting
• inflation forecasting

JEL CLASSIFICATION:

• E310
• E17
• C530

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

¹ https://fcainfoweb.nic.in/PMC/login.aspx

² This is the minimum number of optimal factors using a large window size of 0.05 after several runs of criteria, each with different random subsamples.