{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/70767"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/70767","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Time Series Analysis and Indicators of Economic Activity (Forecasting)","abstract":"Some of the problems involved in identification of time series models and forecasting variables of interest are considered. A theoretical/empirical comparison of various information criteria of interest is provided and it is concluded that Hannan and Kavalieris criterion is the most appropriate criterion to be used for model selection. Appropriate time series models for forecasting state income tax receipts are identified by employing information criteria and Box-Cox transformation. Furthermore, bivariate time series models with the index of leading indicators as the input variable are used to forecast a coincident indicator of interest. The weights assigned to the elements of the index of leading indicators are found by constructing and minimizing the variance of forecast error function for several steps ahead forecasting.","abstract_html":"Some of the problems involved in identification of time series models and forecasting variables of interest are considered. A theoretical/empirical comparison of various information criteria of interest is provided and it is concluded that Hannan and Kavalieris criterion is the most appropriate criterion to be used for model selection. Appropriate time series models for forecasting state income tax receipts are identified by employing information criteria and Box-Cox transformation. Furthermore, bivariate time series models with the index of leading indicators as the input variable are used to forecast a coincident indicator of interest. The weights assigned to the elements of the index of leading indicators are found by constructing and minimizing the variance of forecast error function for several steps ahead forecasting.","abstract_has_math":false,"creators":["Nazmi, Nader"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Economics","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-12-16T04:05:09Z","date_published":"2014-12-16T04:05:09Z","updated_at":"2026-07-22T22:26:03Z","subjects":["Economics, General"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(UMI)AAI8600275"],"render_values":[{"text":"(UMI)AAI8600275","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/70767","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Nazmi, Nader"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-12-16T04:05:09Z","10000-01-01","1985"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Economics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Economics, General"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/70767","(UMI)AAI8600275"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Some of the problems involved in identification of time series models and forecasting variables of interest are considered. A theoretical/empirical comparison of various information criteria of interest is provided and it is concluded that Hannan and Kavalieris criterion is the most appropriate criterion to be used for model selection. Appropriate time series models for forecasting state income tax receipts are identified by employing information criteria and Box-Cox transformation. Furthermore, bivariate time series models with the index of leading indicators as the input variable are used to forecast a coincident indicator of interest. The weights assigned to the elements of the index of leading indicators are found by constructing and minimizing the variance of forecast error function for several steps ahead forecasting.","Made available in DSpace on 2014-12-16T04:05:09Z (GMT). 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A theoretical/empirical comparison of various information criteria of interest is provided and it is concluded that Hannan and Kavalieris criterion is the most appropriate criterion to be used for model selection. Appropriate time series models for forecasting state income tax receipts are identified by employing information criteria and Box-Cox transformation. Furthermore, bivariate time series models with the index of leading indicators as the input variable are used to forecast a coincident indicator of interest. The weights assigned to the elements of the index of leading indicators are found by constructing and minimizing the variance of forecast error function for several steps ahead forecasting.","Made available in DSpace on 2014-12-16T04:05:09Z (GMT). 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