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Record W4230953543 · doi:10.22215/etd/2013-09947

Three Essays on Real-Financial Linkage in Dynamic Stochastic General Equilibrium Models

2013· dissertation· en· W4230953543 on OpenAlexaff
Abeer Reza

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsCarleton University
Fundersnot available
KeywordsDynamic stochastic general equilibriumEconomicsBusiness cycleNew Keynesian economicsConsumption (sociology)Shock (circulatory)Investment (military)Linkage (software)Government spendingFinancial acceleratorGeneral equilibrium theoryMonetary policyMacroeconomicsMonetary economicsEconometricsKeynesian economics

Abstract

fetched live from OpenAlex

In this thesis, I contribute to the growing literature on the linkage between real and financial sectors, and study how financial frictions affect responses of important economic variables to different shocks in a New-Keynesian dynamic stochastic general equilibrium (DSGE) framework.The first chapter of this thesis shows that financial frictions can mitigate an important puzzle in the literature related to investment-specific technology shocks.Evidence from estimated DSGE models and SVAR analysis suggests that investment shocks are an important source of business cycle fluctuation in the post-war U.S. economy.In most models that include investment shocks, consumption falls on impact, contradicting its observed comovement with investment, hours worked and output over the business cycle.This chapter shows that introducing financial frictions alongside endogenous capacity utilization in a New-Keynesian model can produce a positive consumption response to an investment shock.By attenuating the response of investment, the financial accelerator mechanism suppresses households' intertemporal substitution towards savings and allows consumption to rise.Search frictions in the labour market introduce a wedge between marginal product of labour and wages determined through Nash bargaining and magnify the positive consumption response.The second chapter of this thesis documents a new challenge for a class of models with binding borrowing constraints related to government spending shocks.We highlight that DSGE models with housing and collateralized borrowing predict a fall in ii both house prices and consumption following positive government spending shocks.The quasi-constant shadow value of lenders' housing and the negative wealth effect of future tax increases on their consumption are the key reasons for this result.By contrast, we show house prices and consumption in the U.S. rise after identified positive government spending shocks, using a structural vector autoregression methodology and accounting for anticipated effects.The counterfactual joint response of house prices and consumption poses a new challenge when using this class of models to address policy issues for the housing market which have come to fore due to the weak recovery after the 2008 financial crisis.The final chapter of this thesis develops a search-theoretic banking model in a New Keynesian DSGE framework that can simultaneously explain cyclical movements in interest spreads and flows in gross loan creation and destruction, that were recently emphasized in the literature.The model features endogenous match separation, and allows bank loans for productive capital purchases to vary in both intensive and extensive margins.Search frictions in the banking sector generates a counter-cyclical interest spread that amplifies business-cycles.In addition, the model generates responses in gross loan destruction and net loan flows to a credit supply shock that can qualitatively match empirical responses estimated in a vector auto-regression framework.iii To my daughters,

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)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.005
Scholarly communication0.0040.005
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0110.002

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 head0.052
GPT teacher head0.245
Teacher spread0.193 · 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 designSimulation or modeling
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
Published2013
Admission routes1
Has abstractyes

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