The unemployment rate is typically forecasted in the literature using initial claims for unemployment, a lagged value of unemployment, and an autoregressive component. This paper looks to improve upon existing models by adding Google search data to traditional models using initial claims or replacing initial claims with Google searches. One hypothesis is that Google searches may improve forecast accuracy due to employees knowing or getting a sense when they may become unemployed and be searching for jobs prior to filing for unemployment. Study of this is important to improve the accuracy of models and to provide more accurate out-of-sample forecasts for policymakers. If it showed a significant increase, it could perhaps diminish the frequency of surveys conducted by the Bureau of Labor Statistics. Several ARIMA models are used to determine whether or not the addition of specific Google searches can be more useful in an out-of-sample predictive model of unemployment. The results show that while Google was a good way for predicting the model in the past, it does not beat traditional models that use initial claims as an independent variable in predicting changes in the direction of the unemployment rate. In addition, models including regime switching do not improve the forecast accuracy compared with standard models.
","abstract_html":"<p>The unemployment rate is typically forecasted in the literature using initial claims for unemployment, a lagged value of unemployment, and an autoregressive component. This paper looks to improve upon existing models by adding Google search data to traditional models using initial claims or replacing initial claims with Google searches. One hypothesis is that Google searches may improve forecast accuracy due to employees knowing or getting a sense when they may become unemployed and be searching for jobs prior to filing for unemployment. Study of this is important to improve the accuracy of models and to provide more accurate out-of-sample forecasts for policymakers. If it showed a significant increase, it could perhaps diminish the frequency of surveys conducted by the Bureau of Labor Statistics. Several ARIMA models are used to determine whether or not the addition of specific Google searches can be more useful in an out-of-sample predictive model of unemployment. The results show that while Google was a good way for predicting the model in the past, it does not beat traditional models that use initial claims as an independent variable in predicting changes in the direction of the unemployment rate. In addition, models including regime switching do not improve the forecast accuracy compared with standard models.</p>","abstract_has_math":false,"creators":["Dyk, Brennan"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Economics & Finance","degree_department":null,"school":null,"contributors":["Cullen Goenner"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-01-01T08:00:00Z","date_published":"2013-01-01T08:00:00Z","updated_at":"2026-07-24T03:26:24Z","subjects":["forecasting unemployment, google, google domestic trends, google trends, unemployment rate, us unemployment"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.und.edu/theses/1415","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Cullen Goenner"]},{"key":"dc:creator","label":"Author","values":["Dyk, Brennan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Economics & Finance"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["forecasting unemployment, google, google domestic trends, google trends, unemployment rate, us unemployment"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.und.edu/theses/1415"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The unemployment rate is typically forecasted in the literature using initial claims for unemployment, a lagged value of unemployment, and an autoregressive component. This paper looks to improve upon existing models by adding Google search data to traditional models using initial claims or replacing initial claims with Google searches. One hypothesis is that Google searches may improve forecast accuracy due to employees knowing or getting a sense when they may become unemployed and be searching for jobs prior to filing for unemployment. Study of this is important to improve the accuracy of models and to provide more accurate out-of-sample forecasts for policymakers. If it showed a significant increase, it could perhaps diminish the frequency of surveys conducted by the Bureau of Labor Statistics. Several ARIMA models are used to determine whether or not the addition of specific Google searches can be more useful in an out-of-sample predictive model of unemployment. The results show that while Google was a good way for predicting the model in the past, it does not beat traditional models that use initial claims as an independent variable in predicting changes in the direction of the unemployment rate. In addition, models including regime switching do not improve the forecast accuracy compared with standard models.
"]},{"key":"dc:title","label":"Title","values":["Does Google Search Data Aid In Predicting Unemployment?"]}]}],"canonical_facts":{"dc:contributor":["Cullen Goenner"],"dc:creator":["Dyk, Brennan"],"dc:description.abstract":["The unemployment rate is typically forecasted in the literature using initial claims for unemployment, a lagged value of unemployment, and an autoregressive component. This paper looks to improve upon existing models by adding Google search data to traditional models using initial claims or replacing initial claims with Google searches. One hypothesis is that Google searches may improve forecast accuracy due to employees knowing or getting a sense when they may become unemployed and be searching for jobs prior to filing for unemployment. Study of this is important to improve the accuracy of models and to provide more accurate out-of-sample forecasts for policymakers. If it showed a significant increase, it could perhaps diminish the frequency of surveys conducted by the Bureau of Labor Statistics. Several ARIMA models are used to determine whether or not the addition of specific Google searches can be more useful in an out-of-sample predictive model of unemployment. The results show that while Google was a good way for predicting the model in the past, it does not beat traditional models that use initial claims as an independent variable in predicting changes in the direction of the unemployment rate. In addition, models including regime switching do not improve the forecast accuracy compared with standard models.
"],"dc:identifier":["https://commons.und.edu/theses/1415"],"dc:subject":["forecasting unemployment, google, google domestic trends, google trends, unemployment rate, us unemployment"],"dc:title":["Does Google Search Data Aid In Predicting Unemployment?"],"thesis:degree_discipline":["Economics & Finance"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T03:26:24Z"}