Essay on international economics

Galeazzi, Giorgia (2023) Essay on international economics. PhD thesis, University of Glasgow.

Full text available as:
[thumbnail of 2022GaleazziPhD.pdf] PDF
Download (5MB)


This thesis should appeal to several audiences. The literature reviews and empirical examinations will aid economists and academic researchers in navigating the literature and will be valuable for their work. Practitioners and forecasters at central banks and commercial companies are likewise interested in learning which predictors, models, and approaches accurately estimate currency rates. Policymakers, for whom the success of policy choices depends heavily on accurate projections, should also be interested in our review of the current
state of the research. Lastly, the regular coverage of exchange rate predictions in the media suggests that this study might be applicable outside academic and policy circles.

This thesis studies two aspects of international economics: international finance and international trade, and it is organised as follows: Part I provides an in-depth description of the background research that formed the basis for this thesis. Part II consists of three empirically-based original chapters that are independent of one another and each make a unique contribution to the international economics literature. In the Appendix, more technical theories, such as machine learning and decomposition analysis, are described in greater detail.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Subjects: H Social Sciences > HB Economic Theory
Colleges/Schools: College of Social Sciences > Adam Smith Business School > Economics
Supervisor's Name: Cerrato, Professor Mario and MacDonald, Professor Ronald
Date of Award: 2023
Depositing User: Theses Team
Unique ID: glathesis:2023-83890
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 27 Oct 2023 15:17
Last Modified: 27 Oct 2023 15:19
Thesis DOI: 10.5525/gla.thesis.83890

Actions (login required)

View Item View Item


Downloads per month over past year