Research and Applications of Several Nonparametric Methods in Nonlinear Time Series Analysis

声明

摘要

Abstract

Contents

List of Acronyms

List of Tables

List of Figures

1 Introduction

1．1 Regression Models

1．2 Time Series Models

2 Nonlinear Regression Model Fitting

2．1 Kernel Regression (Smoothing)

2．2 Local Polynomial Regression

2．3 Local Linear Regression

2．4 Double Smoothing for Local Linear Regression

2．5 Alternating Conditional Expectation Algorithm

2．6 Penalized Cubic Regression Method

3 Nonlinear Time Series Models

3．1 Introduction

3．2 Self-Exciting Threshold Autoregressive Models

3．3 Additive Autoregresive Models

4 Applications in Nonlinear Time Series

4．1 Kernel Smoothing in Nonlinear Time Series Analysis

4．2 ACE in Nonlinear Time Series Analysis

4．3 PCR in Nonlinear Time Series Analysis

4．4 LL in Nonlinear Time Series Analysis

4．5 DSLL in Nonlinear Time Series Analysis

5 Real Data Analysis

5．1 Introduction

5．2 Linear VS Nonlinear Models

5．2．1 Building Linear Models

5．2．2 Building Nonlinear Models

5．3 Parametric vs Nonparametric

5．3．1 Applying ACE method

5．3．2 Applying LL and DSLL Method

5．3．3 Applying PCR method

5．4 Modelling S＆P SL20 Index

5．5 Conclusions

6 Discussion

Appendix

References

Acknowledgments