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Record W3121543177 · doi:10.22215/etd/2017-12149

Three Essays in Macroeconomics with a Focus on Economic Growth, Monetary Policy and Knowlege Dissemination

2017· dissertation· en· W3121543177 on OpenAlexaffabout
Samira Hasanzadeh

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsCarleton University
Fundersnot available
KeywordsEconomicsSpillover effectInterest rateEndogenous growth theoryStock (firearms)Monetary economicsMacroeconomicsHuman capitalMarket economyEngineering

Abstract

fetched live from OpenAlex

This thesis includes three essays on empirical macroeconomics.The first chapter applies modern ideas-oriented growth accounting, based on the semi-endogenous growth theory of Jones (2002), to compare the sources of Canadian and U.S. economic growth between 1981-2014.Two features stand out in comparison to the U.S. growth experience over the same period.First, over a full percentage point of the average U.S. growth of 1.64 percent is due to excess ideas growth.Second, the constant growth view' that reconciles large sources of transitional growth with relatively stable average growth is not supported in Canada.The second chapter of this thesis examines the empirical link between movements in interest rates and capacity utilization, using 2SLS fixed effects estimations in a panel setting of 21 U.S. manufacturing industries between 1975-2011.The study arrives at three main findings: (a) In most industries a cut in the interest rate does not simultaneously stimulate capacity utilization; (b) in contrast to previous studies, the results do not show evidence that durable-goods industries are more sensitive to interest rate changes than other industries are; and (c) in many industries, the interest rate and capacity utilization move in the same direction.This suggests that First and foremost, I would like to express my special appreciation and gratitude to my advisor, Professor Hashmat Khan, who has been a tremendous mentor for me.He has supported and encouraged

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0030.007
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0110.003

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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2017
Admission routes2
Has abstractyes

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