Financial frictions and durable goods in DSGE models with sticky prices
Bibliographic record
Abstract
The focus of this dissertation is to study the role of financial frictions in DSGE models with durable goods and sticky prices, and how key economic variables respond in such an environment to monetary policy shocks. The first chapter studies the empirical evidence regarding the response of durable and non-durable goods to monetary policy shocks. Using quarterly data from Canada and the United States, and a vector autoregressive (VAR) model, we trace out empirically the effects of monetary policy innovations on key macroeconomic variables. We find that in response to an increase in the interest rate, durable consumption, non-durable consumption, output, and household debt decrease, and the nominal interest rate rises. In the second chapter, we show that in the presence of agency costs and equity based borrowing, the two sector sticky price model with collateral frictions resolve the co-movement problem in a way which is consistent with the empirical evidence, even when durable prices are nearly exible. In the third chapter, we examine the effect of financial frictions on the consumption of durables and non-durables in a two-sector DSGE model with sticky prices and heterogeneous agents. The financial frictions are a combination of loan-to-value (LTV) and payment-to-income (PTI) constraints faced by borrowers. In this setting a monetary contraction reduces the maximum amount that consumer that consumers can borrow in order to purchase durable goods. As a result, the model predicts that the consumption of durables falls, along with non-durables even when durable prices are fully flexible. Thus, the model matches better the predictions of the model with the data, relative to the existing literature. The fourth chapter of the dissertation studies the effectiveness of macro-prudential policy measures in curbing house price inflation amid rising outward foreign direct investment from abroad. To assess the usefulness of these macro-prudential policy tools, we use database of housing prices, GDP, bank crises, policy rates, Chinese outward investment and macro-prudential policy measures covering advanced countries at quarterly frequency from 2003 to 2016. The results suggest that Macro prudential policy measures help in reducing house prices and OFDI has a significant and positive correlation with house prices movements.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".