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.
Choi and Varian (See @cite_2 ) used google trends data to predict initial jobless claims. Our work differs from theirs in the modeling technique, selection of predictor variables and level of data aggregation. They looked at jobless claims only at the national level. Since initial jobless claims is often a leading indicator of the reported unemployment rate it is valuable to review works predicting unemployment rates too. In this realm, Askitas and Zimmermann @cite_4 , Suhoy @cite_1 and D'Amuri and Marcucci @cite_6 use search data to predict unemployment in Germany, Israel, and the US respectively. One downside of unemployment rate prediction is that most rates are reported on a monthly basis, while initial unemployment claims data are reported weekly. Additionally, Choi and Varian do a review of predicting various variables in the present, including unemployment along with travel and consumer confidence. We expand on this approach by using a more general forecasting model accounting for cross dependence between regions and also the different exogenous signals (query terms and clicks on links to specific sites to further improve predictions).
@cite_1 @cite_4 @cite_6 @cite_2
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* 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.