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Segmented regression modelling with an application to German exchange rate data Susko, Edward Andrew
Abstract
Segmented regression models are the topic of this thesis. These are regression models in which the mean response is thought to be linear in the explanatory variables within regions of a particular explanatory variable. A criterion for estimating the number of segments in a segmented model is given and the consistency of this estimator is established under rather general conditions. There have been many studies on modeling and forecasting foreign exchange rates using various models, notably the random walk model, the forward rate model, monetary models and vector autoregressions, see, for example, Meese and Rogoff (1983) and Baillie and McMahon (1989). The general conclusions have been that most of the models cannot outperform the random walk model by a significant margin. The observation that the dependence of the exchange rate on the key macroeconomic indicators is time varying, nonstationary and nonlinear leads to consideration of nonlinear models. In this thesis segmented models are fitted to German exchange rate data using least squares and forecasting results obtained from these models are compared with forecasting results from widely used models in exchange rate prediction. The segmented models tend to perform better than models that have been established in the literature, notably, the random walk model.
Item Metadata
Title |
Segmented regression modelling with an application to German exchange rate data
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
1992
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Description |
Segmented regression models are the topic of this thesis. These are regression models in
which the mean response is thought to be linear in the explanatory variables within regions
of a particular explanatory variable. A criterion for estimating the number of segments in a
segmented model is given and the consistency of this estimator is established under rather
general conditions.
There have been many studies on modeling and forecasting foreign exchange rates using
various models, notably the random walk model, the forward rate model, monetary
models and vector autoregressions, see, for example, Meese and Rogoff (1983) and Baillie
and McMahon (1989). The general conclusions have been that most of the models cannot
outperform the random walk model by a significant margin. The observation that
the dependence of the exchange rate on the key macroeconomic indicators is time varying,
nonstationary and nonlinear leads to consideration of nonlinear models. In this thesis segmented
models are fitted to German exchange rate data using least squares and forecasting
results obtained from these models are compared with forecasting results from widely used
models in exchange rate prediction. The segmented models tend to perform better than
models that have been established in the literature, notably, the random walk model.
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Extent |
1583717 bytes
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Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2008-12-18
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0086595
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
1992-11
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.