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Predictive ability or data snopping? : essays on forecasting with large data sets

This thesis examines the predictive ability of models for forecasting inflation and financial market volatility. Emphasis is put on evaluation of forecasts and the usage of large data sets. Variety of models are used to forecast inflation, including diffusion indices, artificial neural networks, and traditional linear regressions. Financial market volatility is forecast using various GARCH-type and high-frequency based models. High-frequency data are also used to obtain ex-post estimates of volatility, which is then used to evaluate forecasts. All forecast are evaluated using recently proposed techniques that can account for data snooping bias, nested, and nonlinear models.

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.85018
Date January 2004
CreatorsKışınbay, Turgut
PublisherMcGill University
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Formatapplication/pdf
CoverageDoctor of Philosophy (Department of Economics.)
RightsAll items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated.
Relationalephsysno: 002173864, proquestno: AAINR06312, Theses scanned by UMI/ProQuest.

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