Bibliographic record
Abstract
Essay 1: Exchange Rate Dynamics and Global Monetary Policy Spillovers: Time-Varying Dynamic Causal Effects. We propose a novel econometric approach to estimate time-varying dynamic causal effects of structural shocks using external instruments in a factor-augmented-VAR model. Using the Bayesian MCMC estimation method we estimate a model consisting of output, inflation rates, interest rates, and exchange rates for the United States, Canada, Germany, Japan, and the United Kingdom. We find uniformly strong evidence over time that the exchange rate overshoots in response to an exogenous monetary policy shock. Furthermore, we find that the monetary policy shock played a significant role in the exchange rate dynamics, and its contributions were particularly large during the 2008-2009 financial crisis. Essay2: Commodity Return Comovement, Exchange Rate, and Uncertainty Shock. This paper investigates how the uncertainty shock affects the commodity return empirically, using a time-varying parameter dynamic factor model with stochastic volatility (TVP-DFM-SV). We extract a common factor and eight sector-specific factors from 43 different commodity returns (Ma, Vivian, and Wohar, 2019). The common factor is interpreted as the combination of the shocks which only affects the commodity markets idiosyncratic through the general equilibrium (Alquist, Bhattarai and Coibion, 2019), and is orthogonal to the sector-specific factors and commodity-idiosyncratic factors. Then we show the time-varying impulse responses of the common factor and several relevant marco fundamentals to uncertainty shock identified by the external instrument variable (IV). We find that uncertainty shock decreases the commodity return through the common factor, and this effect is time-varying, specifically, during the recession it is stronger. Besides, the uncertainty shock could be a reason behind the negative relationship between commodity return and the strength of the US dollar. Essay 3: A New Approach to Connecting the Dividend-Price Ratio and Stock Returns. There is a long debate on the performance of dividend-price ratio on the stock returns predictability. Most of the literature argues that the predictability decreases after the 1990s. Since Campbell-Shiller decomposition shows that the dividend-price ratio contains the information of both the future returns and future dividend growth, a linear predictive regression of stock returns on the dividend-price ratio may generate biased results due to the measurement error or omitted variable issues. Therefore, this paper proposes a new approach to study the nonlinear Granger causality of dividend-price ratio on stock returns. We conduct an unobserved component model and connect stock returns and the dividend-price ratio through their innovations. We show that our model can be represented by a reduced-form ARMAX process, and it can increase the in-sample predictability.--Author's abstract
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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.
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".