Essays in Honor of Joon Y. Park : Econometric Theory / 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 45A.

Advances in Econometrics Series ; Volume 45A

Language:

English

Subjects (All):

Econometrics.

Physical Description:

1 online resource (406 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 editors Page

Title Page

Copyright Page

Contents

List of Contributors

Introduction

Part I: Nonstationarity, Unit Roots, and Fractional Noise

Part II: Nonlinearity

Part III: Inference and Prediction Using Models With Trending Series

Additional References

Chapter 1: Discrete Fourier Transforms of Fractional Processes with Econometric Applications

1. Introduction

2. Preliminaries

3. Frequency Domain Decompositions

3.1. Lemma

3.2. Theorem

3.3. Remark

3.4. Remark

3.5. Remark

3.6. Remark

3.7. Theorem

3.8. Remark

3.9. Remark

4. Asymptotic Approximations

4.1. Component Approximations

4.2. Lemma3

4.3. Theorem

4.4. Theorem

4.5. Approximations for wx(λ)

4.6. Limit Theory

4.7. Lemma

4.8. Lemma

4.9. Theorem

4.10. Theorem

5. Statistical Applications

5.1. Spectrum Estimation for Fractional Processes

5.2. Theorem

5.3. Semiparametric Estimation of d

5.4. Final Remarks

6. Technical Appendix and Proofs

6.1. Preliminary Results

6.1.1. Lemma A

6.1.2. Lemma B

6.1.3. Lemma C

6.1.4. Lemma D

6.1.5. Lemma E

6.1.6. Lemma F

6.2. Proofs of Main Lemmas and Theorems

6.2.1. Proof of Lemma 3.1

6.2.2. Proof of Theorem 3.2

6.2.3. Proof of Theorem 3.7

6.2.4. Proof of Lemma 4.2

6.2.5. Proof of Theorem 4.3

6.2.6. Proof of Theorem 4.4

6.2.7. Proof of Lemma 4.7

6.2.8. Proof of Lemma 4.8

6.2.9. Proof of Theorem 4.9

6.2.10. Proof of Theorem 4.10

6.2.11. Proof of Theorem 5.2

7. Notation

References

Chapter 2: Asymptotic Properties of the Least Squares Estimator in Local to Unity Processes with Fractional Gaussian Noise

2. A Literature Review

3. Asymptotic Properties.

4. Asymptotic Properties with Fitted Intercept

5. Monte Carlo Studies

5.1. Without Fitted Intercept

5.2. With Fitted Intercept

6. Conclusions

Acknowledgments

Chapter 3: Powerful Self-Normalizing Tests for Stationarity Against the Alternative of a Unit Root

2. KPSS

3. Variance Ratio Tests

3.1. Limiting Processes

3.2. Test Statistics

3.3. Eigenstructure

4. Power

4.1. Asymptotic

4.2. Experimental Evidence

5. Summary

Appendix

Chapter 4: A Sequential Test For a Unit Root in Monitoring a p-th Order Autoregressive Process

2. Sequential Test For Near-Unit-Root AR(p) Process

2.1. A Reparameterization of Regression for Near-Unit-Root AR(p) Process

2.2. Normal Equation and Sequential Estimator

3. The Asymptotic Properties of and the Sequential Procedures For a Near-Unit-Root AR(p)

3.1. Convergence to an Ornstein-Uhlenbeck (OU) Process on D[0,∞)

3.2. Asymptotic Properties of the Components of the Normal Equation

3.3. Main Theorems

4. Testing Procedure, Theoretical Values, and Simulation Results

4.1. Testing Procedure

4.2. Theoretical Values for Moments of

4.3. The Simulation Settings and Results When Lag Length is Known

4.4. The Simulation Results When Lag Length is Unknown

5. Concluding Remarks

Chapter 5: Functional-Coefficient Cointegrating Regression with Endogeneity

2. Main Results

2.1. Model With Nonstationary xt and Stationary zt

2.2. Model With Stationary xt and Nonstationary zt

2.3. Multivariate Extension

3. Conclusion

Appendices

A. Proofs of Main Results

B. Proofs of Auxiliary Results.

Chapter 6: A Specification Test Based on Convolution-Type Distribution Function Estimates for Non-Linear Autoregressive Processes

2. Test Statistics

3. Assumptions and Some Preliminaries

4. Main Result

5. Simulation Studies

5.1. Setup

5.2. Selection of BIter, b, and lB

5.3. Tests and for H01 and H02

6. Conclusion

7. Proofs

Chapter 7: Transformation Models with Cointegrated and Deterministically Trending Regressors

2. Model and Estimation

3. Asymptotic Theory

3.1. Assumptions

3.2. Distribution Theory

4. Simulation

5. Conclusion

Proof of the Main Theorem

Chapter 8: Minimax Risk in Estimating Kink Threshold and Testing Continuity

2. Model and Assumptions

3. Estimators and Risk Bound

3.1. Estimators

3.2. Risk Bound

4. Testing Continuity

4.1. Continuity Test

4.2. Test Statistic

4.3. Bootstrapping Continuity Test

5. Monte Carlo Experiment

6. Empirical Application: Growth and Debt

7. Conclusion

A. Proofs of Main Theorems

B. Auxiliary Lemma

C. Figures for Empirical Application in Section 7

Part III: Inference and Prediction Using Models with Trending Series

Chapter 9: Semiparametric Independence Tests Between Two Infinite-order Cointegrated Series

2. Framework and Preliminary Results

3. Test Statistics and Asymptotic Null Distributions

4. Consistency of the Generalized Tests

5. Local Power Analysis

6. Simulation Study

6.1. Description of the Experiment

6.2. Level

6.2.1. Gaussian Innovations

6.2.2. Non-Gaussian Innovations

6.3. Power

Appendix: Proofs.

Chapter 10: Inference in Conditional Vector Error Correction Models With a Small Signal-to-Noise Ratio

1. Introduction and Motivation

2. Model and Main Results

2.1. Assumptions and Parameterization

2.2. Limiting Distributions

3. Simulation Results

4. Empirical Application: Forward Premium Model

Appendix: Proofs of Main Results

A.1. Preliminary Lemma

A.2. Proof of Theorem 1

A.3. Proof of Theorem 2

A.4. Proof of Theorem 3

Chapter 11: Some Extensions of Asymptotic F and t Theory in Nonstationary Regressions

2. Deterministic and Exogenous Cases

3. Endogenous Case

3.1. Cointegration Regression

3.2. Predictive Regression

4. A Simulation Study

Chapter 12: Non-Stationary Parametric Single-Index Predictive Models: Simulation and Empirical Studies

2. Parametric Single-Index Predictive Model

4. Empirical Illustration: Stock Market Return Predictability

Chapter 13: Best Linear Prediction in Cointegrated Systems

2. Switching Predictor of the Random Walk and Cointegration Models

3. Prediction with Non-IID Cointegration Error Dynamics8

4. Monte Carlo Simulation Results

5. Application to the US' Economic Variables

Data Description

Appendix A: Proofs of Theorems

Appendix B: Simulation Results of MSFEs.

Notes:

Includes bibliographical references.

Description based on print version record.

Other Format:

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

ISBN:

9781837532100

OCLC:

1376931750