{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/85672"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/85672","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Essays on Time Series Modeling","abstract":"The first chapter of this dissertation introduces a few important concepts which are used throughout the rest of the dissertation. Chapter 2 revisits the inertial inflation hypothesis for Brazil. In this chapter we use time series techniques to model the long-run dynamics of the Brazilian inflationary process. Our results reveal that, although there is some inertia in the Brazilian inflation, the degree of inertia is rather small. Another important policy issue conjectured by Friedman in his Nobel Lecture is the relationship between inflation and uncertainty about the future level of inflation. The third chapter uses quantile regression techniques to test Friedman's hypothesis using the U.S. implicit price deflator for GNP as a measure for inflation. We find some evidence in favor to Friedman's claim that there exists a positive and significant relationship between the inflation level and inflation uncertainty (measured here as the conditional scale of inflation). However, it is shown that this relationship only holds for certain ranges (higher quantiles) of the conditional quantiles of our measure of scale. Finally, the fourth chapter evaluates the predictive accuracy of interval forecasting methods for ARCH-type models proposed in the literature under different misspecification scenarios: misspecification of the conditional distribution, misspecification of the conditional variance, and misspecification of both the conditional variance and the distribution. Our first main result is that, overall, the bootstrap method outperforms the alternatives, yielding prediction intervals whose empirical levels and nominal levels match well, even under a misspecified conditional variance. Our second important result in this chapter is that the GARCH(1,1) specification is reliable for building prediction intervals which approximate well the nominal confidence levels when the true data generating process comes from a different conditional heteroskedastic model.","abstract_html":"The first chapter of this dissertation introduces a few important concepts which are used throughout the rest of the dissertation. Chapter 2 revisits the inertial inflation hypothesis for Brazil. In this chapter we use time series techniques to model the long-run dynamics of the Brazilian inflationary process. Our results reveal that, although there is some inertia in the Brazilian inflation, the degree of inertia is rather small. Another important policy issue conjectured by Friedman in his Nobel Lecture is the relationship between inflation and uncertainty about the future level of inflation. The third chapter uses quantile regression techniques to test Friedman's hypothesis using the U.S. implicit price deflator for GNP as a measure for inflation. We find some evidence in favor to Friedman's claim that there exists a positive and significant relationship between the inflation level and inflation uncertainty (measured here as the conditional scale of inflation). However, it is shown that this relationship only holds for certain ranges (higher quantiles) of the conditional quantiles of our measure of scale. Finally, the fourth chapter evaluates the predictive accuracy of interval forecasting methods for ARCH-type models proposed in the literature under different misspecification scenarios: misspecification of the conditional distribution, misspecification of the conditional variance, and misspecification of both the conditional variance and the distribution. Our first main result is that, overall, the bootstrap method outperforms the alternatives, yielding prediction intervals whose empirical levels and nominal levels match well, even under a misspecified conditional variance. Our second important result in this chapter is that the GARCH(1,1) specification is reliable for building prediction intervals which approximate well the nominal confidence levels when the true data generating process comes from a different conditional heteroskedastic model.","abstract_has_math":false,"creators":["Campelo, Ana Katarina"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Economics","degree_department":null,"school":null,"contributors":["Anil Bera"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T22:47:53Z","date_published":"2015-09-25T22:47:53Z","updated_at":"2026-07-22T22:26:25Z","subjects":["Economics, Theory"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI9989950"],"render_values":[{"text":"(MiAaPQ)AAI9989950","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/85672","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Anil Bera"]},{"key":"dc:creator","label":"Author","values":["Campelo, Ana Katarina"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T22:47:53Z","10000-01-01","2000"]},{"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, Theory"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/85672","(MiAaPQ)AAI9989950"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The first chapter of this dissertation introduces a few important concepts which are used throughout the rest of the dissertation. Chapter 2 revisits the inertial inflation hypothesis for Brazil. In this chapter we use time series techniques to model the long-run dynamics of the Brazilian inflationary process. Our results reveal that, although there is some inertia in the Brazilian inflation, the degree of inertia is rather small. Another important policy issue conjectured by Friedman in his Nobel Lecture is the relationship between inflation and uncertainty about the future level of inflation. The third chapter uses quantile regression techniques to test Friedman's hypothesis using the U.S. implicit price deflator for GNP as a measure for inflation. We find some evidence in favor to Friedman's claim that there exists a positive and significant relationship between the inflation level and inflation uncertainty (measured here as the conditional scale of inflation). However, it is shown that this relationship only holds for certain ranges (higher quantiles) of the conditional quantiles of our measure of scale. Finally, the fourth chapter evaluates the predictive accuracy of interval forecasting methods for ARCH-type models proposed in the literature under different misspecification scenarios: misspecification of the conditional distribution, misspecification of the conditional variance, and misspecification of both the conditional variance and the distribution. Our first main result is that, overall, the bootstrap method outperforms the alternatives, yielding prediction intervals whose empirical levels and nominal levels match well, even under a misspecified conditional variance. Our second important result in this chapter is that the GARCH(1,1) specification is reliable for building prediction intervals which approximate well the nominal confidence levels when the true data generating process comes from a different conditional heteroskedastic model.","Made available in DSpace on 2015-09-25T22:47:53Z (GMT). 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In this chapter we use time series techniques to model the long-run dynamics of the Brazilian inflationary process. Our results reveal that, although there is some inertia in the Brazilian inflation, the degree of inertia is rather small. Another important policy issue conjectured by Friedman in his Nobel Lecture is the relationship between inflation and uncertainty about the future level of inflation. The third chapter uses quantile regression techniques to test Friedman's hypothesis using the U.S. implicit price deflator for GNP as a measure for inflation. We find some evidence in favor to Friedman's claim that there exists a positive and significant relationship between the inflation level and inflation uncertainty (measured here as the conditional scale of inflation). However, it is shown that this relationship only holds for certain ranges (higher quantiles) of the conditional quantiles of our measure of scale. Finally, the fourth chapter evaluates the predictive accuracy of interval forecasting methods for ARCH-type models proposed in the literature under different misspecification scenarios: misspecification of the conditional distribution, misspecification of the conditional variance, and misspecification of both the conditional variance and the distribution. Our first main result is that, overall, the bootstrap method outperforms the alternatives, yielding prediction intervals whose empirical levels and nominal levels match well, even under a misspecified conditional variance. Our second important result in this chapter is that the GARCH(1,1) specification is reliable for building prediction intervals which approximate well the nominal confidence levels when the true data generating process comes from a different conditional heteroskedastic model.","Made available in DSpace on 2015-09-25T22:47:53Z (GMT). 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