Predicting Unemployment Claims Using Regional and Exogenous Signals: A Sparse Modeling Approach

TL;DR

This paper presents a sparse VAR modeling approach to predict US unemployment claims across regions using regional data and external signals like search queries, showing promising initial results.

Contribution

It introduces a sparse VAR model incorporating external signals for regional unemployment claim prediction, improving over traditional methods.

Findings

Model captures regional correlations effectively

External signals enhance prediction accuracy

Preliminary results are promising

Abstract

In this paper we apply a time series based Vector Auto Regressive (VAR) approach to the problem of predicting unemployment insurance claims in different census regions of the United States. Unemployment insurance claims data, reported weekly, are a leading indicator of the US unemployment rate. Gathering weekly unemployment claims and aggregating by region, we model correlation between the different census regions. Additionally, we explore the use of external variables such as Bing search query volumes and URL site clicks related to unemployment claims. To prevent any spurious predictors from appearing in the model we use sparse model based regularization. Preliminary results indicate that our approach is promising and in ongoing work we are extending the approach to a larger set of predictors and a longer data range.