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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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. The aim of this work is to explore the possible types of phenomena that simple macroeconomic Agent-Based models (ABMs) can reproduce. We propose a methodology, inspired by statistical physics, that characterizes a model through its “phase diagram” in the space of parameters. Our first motivation is to understand the large macro-economic fluctuations observed in the “Mark I” ABM devised by Delli Gatti and collaborators. In this regard, our major finding is the generic existence of a phase transition between a “good economy” where unemployment is low, and a “bad economy” where unemployment is high. We then introduce a simpler framework that allows us to show that this transition is robust against many modifications of the model, and is generically induced by an asymmetry between the rate of hiring and the rate of firing of the firms. The unemployment level remains small until a tipping point, beyond which the economy suddenly collapses. If the parameters are such that the system is close to this transition, any small fluctuation is amplified as the system jumps between the two equilibria. We have explored several natural extensions of the model. One is to introduce a bankruptcy threshold, limiting the firms maximum level of debt-to-sales ratio. This leads to a rich phase diagram with, in particular, a region where acute endogenous crises occur, during which the unemployment rate shoots up before the economy can recover. We also introduce simple wage policies. This leads to inflation (in the “good” phase) or deflation (in the “bad” phase), but leaves the overall phase diagram of the model essentially unchanged. We have also explored the effect of simple monetary policies that attempt to contain rising unemployment and defang crises. We end the paper with general comments on the usefulness of ABMs to model macroeconomic phenomena, in particular in view of the time needed to reach a steady state that raises the issue of ergodicity in these models. We analyze spatio-temporal data on U.S. unemployment rates. For this purpose, we present a family of models designed for the analysis and time-forward prediction of spatio-temporal econometric data. Our model is aimed at applications with spatially sparse but temporally rich data, i.e. for observations collected at few spatial regions, but at many regular time intervals. The family of models utilized does not make spatial stationarity assumptions and consists in a vector autoregressive (VAR) specification, where there are as many time series as spatial regions. A model building strategy is used that takes into account the spatial dependence structure of the data. Model building may be performed either by displaying sample partial correlation functions, or automatically with an information criterion. Monthly data on unemployment rates in the nine census divisions of the U.S. are analyzed. We show with a residual analysis that our autoregressive model captures the dependence structure of the data better than with univariate time series modeling. 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.