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Record W7038872321

International capital flows

2019· dissertation· en· W7038872321 on OpenAlexaboutno aff

Bibliographic record

VenueAcademica-e (Universidad Pública de Navarra) · 2019
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsPanel dataPortfolio investmentGlobalizationPortfolioNet foreign assetsDynamic stochastic general equilibriumInstrumental variableRecessionConstruct (python library)Capital account
DOInot available

Abstract

fetched live from OpenAlex

This thesis uses modern macroeconomic modeling techniques and panel data econometrics to quantitatively measure the determinants of financial globalization and its e↵ects on advanced and developing economies. The first two chapters of this thesis provide the starting point for the quantitative analysis of international gross capital flows and valuation e↵ects between two asymmetric countries and it serves policymakers to quantify these matters in an diaphanous manner. In the first chapter, I construct a novel two-country DSGE model with endogenous portfolio choice to study the role of structural asymmetries in explaining the size and composition of capital flows between emerging and advanced economies. In the second chapter, we calibrate an extension to the previous model in order to discuss the potential determinants of the large increase in Canadian Net Foreign Assets with the US observed after 2012. The last two chapters of this thesis provide an econometric analysis which uses empirical data at the world level to quantitatively measure economic integration determinants and its e↵ects. In the third chapter, we examine the link between economic globalization and spatial inequality in a panel of 142 countries over the period 1992-2012 using instrumental variable techniques. In the fourth chapter, I provide results to show how the Lucas Paradox has turned even more pronounced during the Great Recession than in the previous decades.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.223
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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