Bayesian Variable Selection for Nowcasting Economic Time Series

Abstract

We consider the problem of short-term time series forecasting (nowcasting) when there are more possible predictors than observations.Our approach combines three Bayesian techniques: Kalman filtering, spike-and-slab regression, and model averaging.We illustrate this approach using search engine query data as predictors for consumer sentiment and gun sales.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 137 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.