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.
* Research Department, Bank of Israel. http: www.boi.org.il Tanya Suhoy – Phone: 972-2-655-2620; Email: tanya suhoy@boi.org.il 1 This study was undertaken in 2008 at the suggestion of Prof. Stanley Fischer. I thank Prof. Hal Varian for help and access to the Google's experimental database, Jonathan Sidi for research assistance, and Ran Sharabani for his contribution to the discussion at the Research Department seminar. The current economic crisis requires fast information to predict economic behavior early, which is difficult at times of structural changes. This paper suggests an innovative new method of using data on internet activity for that purpose. It demonstrates strong correlations between keyword searches and unemployment rates using monthly German data and exhibits a strong potential for the method used. We suggest the use of an Internet job-search indicator (the Google Index, GI) as the best leading indicator to predict the US unemployment rate. We perform a deep out-of-sample forecasting comparison analyzing many models that adopt both our preferred leading indicator (GI), the more standard initial claims or combinations of both. We find that models augmented with the GI outperform the traditional ones in predicting the monthly unemployment rate, even in most state-level forecasts and in comparison with the Survey of Professional Forecasters. In this paper we show how to use search engine data to forecast near-term values of economic indicators. Examples include automobile sales, unemployment claims, travel destination planning and consumer confidence.