Essays in Honor of Joon Y. Park : Econometric Methodology in Empirical Applications / edited by Yoosoon Chang, Sokbae Lee, and J. Isaac Miller.

Format:

Book

Contributor:

Chang, Yoosoon, editor.

Lee, Sokbae, editor.

Miller, J. Isaac, editor.

Series:

Advances in econometrics ; Volume 45B.

Advances in Econometrics Series ; Volume 45B

Language:

English

Subjects (All):

Econometrics.

Physical Description:

1 online resource (449 pages)

Edition:

First edition.

Place of Publication:

Bingley, England : Emerald Publishing Limited, [2023]

Summary:

Volumes 45a and 45b of Advances in Econometricshonor Professor Joon Y. Park, who has made numerous and substantive contributions to the field of econometrics over a career spanning four decades since the 1980s and counting.

Contents:

Intro

Half title page

Series page

Title page

Copyright page

Contents

List of Contributors

Introduction

Part I: Macroeconometrics

Chapter 1: Aggregate Output Measurements: A Common Trend Approach

1. Introduction

2. Model

2.1. Observability of the Signal: A Key Parameter

3. Estimation

3.1. Numerical and Simulation Evidence

4. Signal Extraction

4.1. Long-Run Objects

4.2. Simulation Evidence (Continued)

5. An Improved Aggregate Output Measure

6. Conclusion

References

Appendix: Proof of Theorem 1 and Derivation of Equations (2) and (3)

Chapter 2: Markov Switching Rationality

2. Econometric Framework

3. Testing for Markov Switching Rationality

3.1. AFE-BS Test for Forecast Rationality

3.2. AFE-H Test for Rationality

4. Monte Carlo Simulation Results

4.1. Monte Carlo Results - Unbiasedness

4.2. Monte Carlo Results - Efficiency

4.3. Discussion

5. A Markov Switching Bias in the FFR Forecasts

Appendix. A Empirical Results Using Federal Funds Futures

Chapter 3: The Econometrics of Oil Market Var Models

2. Conventional Identification Strategies in Oil Market Models

2.1. Short-Run Exclusion Restrictions

2.2. Long-Run Exclusion Restrictions

2.3. Sign Restrictions

2.3.1. The Kilian and Murphy (2012) Model

2.3.2. The Kilian and Murphy (2014) Model

2.3.3. Larger-Dimensional Extensions of the Kilian-Murphy Framework

2.3.4. Incomplete Oil Market Models

2.3.5. Understanding the Impact of Oil Demand and Oil Supply Shocks on Domestic Macroeconomic Aggregates

3. Estimation and Inference

3.1. Standard Bayesian Inference in Sign-Identified Models

3.2. The Role of the Prior in the Conventional Approach.

3.3. An Alternative Bayesian Approach to Estimating Sign-Identified Oil Market Models

3.4. How Credible are Time-Varying Coefficient Models of the Oil Market?

4. Recent Controversies about Modeling Oil Markets

4.1. How to Define the Impact Price Elasticity of Oil Demand

4.2. Is There Evidence for a Much Higher Impact Price Elasticity of Oil Supply?

4.3. Is the Price Elasticity of Oil Demand Positive?

4.4. Has the Shale Oil Revolution Undermined the Stability of Global Oil Market Models?

4.5. How to Measure the Global Business Cycle in Oil Market Models

4.6. How to Transform the Real Price of Oil

5. Non-Traditional Approaches to Identifying Oil Demand and Oil Supply Shocks

5.1. Historical Counterfactuals for OPEC Events

5.2. Oil Supply News Shocks

5.3. Forecast Revisions for Global Growth Forecasts

5.4. The Narrative Approach to Identifying Oil Supply and Oil Demand Shocks

5.5. Text-Based Measures of Oil Demand and Oil Supply Shocks

Part II: Financial Econometrics

Chapter 4: Quantile Impulse Response Analysis with Applications in Macroeconomics and Finance

2. QR Model for a Structural VAR Analysis

3. Quantile Impulse Response Function

3.1. Definition and Construction of QIRF

3.2. Comparison to Recent QIRF Studies

4. Estimation and Statistical Inference

4.1. Assumption

4.2. Estimation

4.3. Asymptotic Inference

4.4. Residual-Based MBB

5. Quantile Impulse Response Analysis of the US Economy

5.1. Data

5.2. Estimated Conditional Quantiles

5.3. Quantile Impulse Response Analysis

5.3.1. QIRF to a Monetary Policy Shock

5.3.2. QIRF to a Financial Shock

5.4. Growth-at-Risk Dynamics During the Global Financial Crisis

5.5. Quantile Responses Under a Hypothetical Distress Scenario

References.

APPENDIX

Chapter 5: Risk Neutral Density Estimation with a Functional Linear Model

1. INTRODUCTION

2. ESTIMATION OF RISK-NEUTRAL DENSITY

2.1. Option Pricing Formula

2.2. RND as the Solution of an Integral Equation

2.3. The Landweber-Fridman Method

2.4. Density Correction

3. ASYMPTOTIC PROPERTIES OF THE LF ESTIMATOR

3.2. Asymptotic Normality

3.3. Data-driven Selection of the Tuning Parameter

4. SIMULATIONS

5. APPLICATION TO S&amp

P500 OPTIONS

6. CONCLUSION

Chapter 6: Estimating Diffusion Models of Interest Rates at the Zero Lower Bound: From the Great Depression to the Great Recession and Beyond

2. Federal Reserve Data

3. Diffusion Models of the Interest Rate

4. Indirect Inference

5. Empirical Results

Chapter 7: A Market Crash or Tail Risk? Heavy Tails and Asymmetry of Returns in the Chinese Stock Market

1.2. Chinese Equity Market and the 2015 Crash

1.3. Research Problems and Contributions

1.4. Organization of the Chapter

2. Inference on Heavy-Tailedness

3. Data

4. Empirical Analysis

4.1. Tail Index Estimates

4.2. (A)symmetry in the Left and Right Heavy-Tailedness

4.3. Structural Breaks in Heavy-Tailedness

5. Empirical Analysis: Heavy-Tailedness Determinants

5.2. Tail Index Regressions: Estimation

5.2.1. Absolute returns

5.2.2. One-Sided Returns

Part III: Pandemic, Climate, and Disaster

Chapter 8: Predicting Crashes in Oil Prices During The Covid-19 Pandemic with Mixed

2. Mixed Causal-Noncausal Models and Filtering

2.2. Filtering the Data

3. MONTE CARLO ANALYSIS - EFFECTS OF DETRENDING

3.1. Accuracy of Detrending

3.2. Effects of Detrending on Model Identification

4. Predicting Crashes in Oil Prices.

4.1. Economic Variables to Detrend Series

4.2. In-sample Analysis

4.3. Real-time Analysis

5. Conclusion

Chapter 9: Depth-weighted Forecast Combination: Application to COVID-19 Cases

2. FORECAST DEPTH AND FORECAST COMBINATION

3. COMPARISONS AND DISCUSSIONS

3.2. Inversed MSE

4. LIMITING DISTRIBUTION OF COMBINED FORECAST

5. FORECASTING NEW COVID-19 CASES

6. CONCLUDING REMARKS

REFERENCES

APPENDIX: PROOF OF THEOREM 1

Chapter 10: Identification of Beliefs in the Presence of Disaster Risk and Misspecification

1.1. Relation to the Existing Literature

1.2. Outline of the Chapter

2. Why Model-implied Probabilities May Confirm the Rare Events Hypothesis?

2.1. Model-implied Empirical Probabilities

2.2. Model-Implied Population Probability Distribution

2.3. The Cases of EL

2.4. The Rare Disaster Interpretation

3. Disaster Risk and Problematic Characterization of Distorted Beliefs

3.2. About the Choice of a -Divergence

3.2.1. Case of a Well-specified Asset Pricing Model

3.2.2. Case of a Misspecified Asset Pricing Model (Or Counterfactual Analysis)

4. Sufficient Conditions for the Existence of Model-implied Population Probabilities

4.1. The Case of Bounded Variables

4.2. How to Deal with Unbounded Pricing Errors?

Chapter 11: A New Model for Agricultural Land-Use Modeling and Prediction in England Using Spatially High-Resolution Data

2. System of TL-RE-SED Tobit Equations

2.1. Methodological Explorations

2.1.1. Exploring the Use of Spatially Disaggregated Data

2.1.2. Modeling Land-Use Shares as Censored Responses

2.1.3. Allowing for Potential SED

2.1.4. Modeling Unobserved Heterogeneity in an Error Component Structure.

2.1.5. Reducing the Computational Burden via a Hybrid Estimation Procedure

2.2. Constructing the TL-RE-SED-Tobit Model for Panel Data

2.3. System Variance-Covariance Structure

2.4. Other Useful Results and Transformations

3. Hybrid QML/GMM Estimation Procedure

3.1. Estimating the TL-Tobit Panel Data Models

3.1.1. Heteroscedasticity

3.1.2. Pooled QML Estimation

3.1.3. Standardized Residuals

3.2. Estimating &amp

Constructing Cochrane-Orcutt Transformations

3.3. Estimating System of TL-RE-SED Tobit Equations

3.4. Prediction Under the TL-RE-SED Tobit Model

4. Empirical Analysis of Selected Land-Use Shares in England

4.1. Data Descriptions and Sources

4.2. Empirical Specifications

4.3. Estimation Results and Important Findings

4.3.1. Pooled QML Estimation of the TL-Tobit Panel Data Models

4.3.2. GMM Estimation of the Spatial Parameters

4.3.3. Iterative QML Method

4.4. Improvement in Prediction Accuracy

5. Conclusions

Chapter 12: Local Climate Sensitivity: What Can Time Series of Distributions Reveal About Spatial Heterogeneity of Climate Change?

2. Physical and Statistical Models

3. Methodology

3.1.1. Step 1:

3.1.2. Step 2: Climate Sensitivity,

3.1.3. Step 3: NHT Function, X(r)

3.1.4. Synthesis: Local Climate Sensitivity, C(r)

3.2. Time Scales and Dynamic Statistical Models

4. Data and Empirical Results

4.2. Main Estimation Results

4.2.2. Step 2: Climate Sensitivity,

4.2.3. Step 3: NHT Function, X(r)

4.2.4. Synthesis: Local Climate Sensitivity, C(r)

5. Concluding Remarks

Appendix

Part IV: Microeconometrics and Panel Data

Chapter 13: Maximum Likelihood Estimation of Dynamic Panel Data Models with Interactive Effects: Quasi-Differencing Over Time or Across Individuals?

1. Introduction.

2. Model and Assumptions.

Notes:

Includes bibliographical references.

Description based on print version record.

Other Format:

Print version: Chang, Yoosoon Essays in Honor of Joon Y. Park

ISBN:

9781837532148

OCLC:

1376932093