[下载]Time Series Analysis III

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Advanced Topics

Table of Contents

Fractional Difference of Time Series

• Integer Difference and Fractional Difference
• Example 1

ARMA Analysis for Regression Residuals

• AR(1), MA(1), ARMA(1,1)
• Example 2
• GPE2 Application Module: ARMA.GPE

Auto-Regressive Conditional Heteroscedasticity

• The Model: ARCH(1), ARCH-M(1), GARCH(1,1)
• Model Identification for ARCH Process
• Model Estimation
• Example 3, Example 4, Example 5
• GARCH(1,1) Model Based on Non-Normal Distributions
• Example 6
• GPE2 Application Module: GARCH.GPE

Multi-Equation Time Series Models

• VAR Analysis

Readings

• W. Enders, Chapter 3, 5.
• W. H. Green, Chapter 20.
• K.-P. Lin, Chapter 14.
• Additional Readings:
  • T. Bollerslev, "Generalized Autoregressive Conditional Heteroskedasticity," Journal of Econometrics 31, 1986, 307-327.
  • T. Bollerslev, " A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," Review of Economics and Statistics 69, 1987, 542-547 (Paper).
  • T. Bollerslev and E. Ghysels, "Periodic Autoregressive Conditional Heterscedasticity," American Statistical Association Journal of Business and Economic Statistics 14, 1996, 139-151.
  • R. F. Engle, "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica 50, 1982, 987-1006 (Paper).
  • R. F. Engle, D. M. Lilien, and R. P. Robins, "Estimating Time-Varying Risk Premia in the Term Structure: the ARCH-M Model," Econometrica 55, 1987, 391-407 (Paper).
  • L. R. Glosten, R. Jagannathan, and D. Runkle, "Relationship Between the Expected Value and the Volatility of the Normal Excess Return on Stocks," Journal of Finance, 48, 1993, 1779-1801 (Paper).
  • D. B. Nelson, "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica 59, 1991, 347-370 (Paper).

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关键词：Time Series Analysis Analysi alysis Series 下载 Analysis time Series III

Prediction in ARMA models with GARCH in mean eects

Introduction

• Introduction to time-series analysis. [Slideshow]. [Printable version].

Tools

• Simulation. [Slideshow]. [Printable version].
• Monte carlo simulation. [Slideshow]. [Printable version].
• The bootstrap. [Slideshow]. [Printable version].
• Difference equations.
• Gauss
• Introduction to MLE. [Slideshow]. [Printable version].
• Properties of the MLE. [Slideshow]. [Printable version].
• Numerical computation for the MLE. [Slideshow]. [Printable version].
• Applications of MLE. [Slideshow]. [Printable version].

Stationary time-series models

• Auto-regressive models, AR. [Slideshow]. [Printable version].
• Moving average models, MA . [Slideshow]. [Printable version].
• Auto-regressive moving average models, ARMA . [Slideshow]. [Printable version].
• Building a model for stationary time series. The Box-Jenkins methodology. MLE inference: LR, AIC, SBC. [Slideshow]. [Printable version].

Volatility

• ARCH and GARCH models. [Slideshow]. [Printable version].
• MLE for ARCH/GARCH models. [Slideshow]. [Printable version]. Gauss source for arma models with arch errors, by Tirthankar Patnaik.
• Persistence, half-life: Regime shifts and spurious persistence. [Slideshow]. [Printable version].
• An example of estimating a Garch model in real world data. [Slideshow]. [Printable version].

Non-stationarity

• Spurious regressions in Econometrics. [Slideshow]. [Printable version].
• Deterministic and Stochastic trends. [Slideshow]. [Printable version].
• Testing for I(1). [Slideshow]. [Printable version].
• An example of estimating and testing for trends in real world data. [Slideshow]. [Printable version].

Multivariate stationary models

• Granger causality.
• Vector auto regressions.
• Co-integration and error-correction models.

Books

• Time Series Analysis by J. Hamilton.
• The econometric analysis of time series by A. C. Harvey.
• Forecasting economic time series by C. Granger and P. Newbold.
• Time series analysis -- forecasting and control by G. Box and G. Jenkins.
• Time series forecasting by C. Chatfield.

Useful links

• Ideas for computational tools.

不错!

有链接不正常，如J. D. Hamilton, "State-Space Models," Handbook of Econometrics, Vol. IV, eds. R. F. Engle and D. L. McFadden, Chapter 50, 3039-3080, Elsevier, 1994 (Paper). -->

还请楼主修正，感谢

Dear Mr. Admin

I just copy & paste the whole Course Outline here in order to keep its original style and feature. For those missing parts such as J. D. Hamilton, "State-Space Models," Handbook of Econometrics, Vol. IV, eds. R. F. Engle and D. L. McFadden, Chapter 50, 3039-3080, Elsevier, 1994 as Mr.Admin mentioned, I would like to find them out and upload to here as per request. I am sorry for the inconvenience.

Thank your for your suggestion!!

Time Series Econometrics - Spring 2005

• Lecture 1 (syllabus: full course)
• Lecture 1 (syllabus: local and global optimization)
• Lecture 1 (local and global optimization)
• Lecture 2 (syllabus: elements of linear time series in the time domain)
• Lecture 2 (elements of linear time series in the time domain)
• Lecture3-4 (syllabus: state space representations and optimal filtering)
• Lecture 3 (state space representations)
• Lecture 4 (optimal filtering)
• Lecture 5 (syllabus: simulation and bootstrap methods)
• Lecture 5 (simulation and bootstrap methods)
• Lecture 6 (syllabus: persistence, unit roots (integration) and cointegration
• Lecture 6 (persistence, unit roots (integration) and cointegration
• Lecture 7 (syllabus: volatility)
• Lecture 7 (volatility)
• PROBLEMS (due April 13, in class)

• Paper "Implied Volatility String Dynamics"

Time Series Econometrics by Chris Sims

Bayesian Basics

Structural VAR's

Dummy observation priors

Notes on comparing models

Exercise on model comparison, due Thursday, 11/14

Real GDP data

Answers to model comparison exercise"

Simple Markov Chain Monte Carlo Exercise, due Monday 12/9

Answers

Granger Causal Priority, Interpretations of Exogeneity

Hidden Markov chain notes (including an exercise from a previous version of the course)

Take-home final exam. There are two parts, to be downloaded separately, corresponding to the first and last halves of the course. Part 1 is worth 45 points, part 2 worth 135 points.

Part 1: pdf format MS word format

Part 2

Answers to part 2 of the final

CDF of total point scores

Introduction to Time Series Analysis

1. Definitions, Applications and Techniques
2. What are Moving Average or Smoothing Techniques?
   1. Single Moving Average
   2. Centered Moving Average
3. What is Exponential Smoothing?
   1. Single Exponential Smoothing
   2. Forecasting with Single Exponential Smoothing
   3. Double Exponential Smoothing
   4. Forecasting with Double Exponential Smoothing
   5. Triple Exponential Smoothing
   6. Example of Triple Exponential Smoothing
   7. Exponential Smoothing Summary
4. Univariate Time Series Models
   1. Sample Data Sets
   2. Stationarity
   3. Seasonality
   4. Common Approaches
   5. Box-Jenkins Approach
   6. Box-Jenkins Model Identification
   7. Box-Jenkins Model Estimation
   8. Box-Jenkins Model Validation
   9. SEMPLOT Sample Output for a Box-Jenkins Model Analysis
   10. SEMPLOT Sample Output for a Box-Jenkins Model Analysis with Seasonality
5. Multivariate Time Series Models
   1. Example of Multivariate Time Series Analysis

Time Series Analysis and Forecasting Techniques

MENU

• A Summary of Forecasting Methods
• How to Do Forecasting by a Regression Analysis
• Moving Average and Exponential Smoothing Methods (Fortran Codes)
• Winters' Method (Fortran Codes)
• Smoothing the Data (Interactive Fortran Codes)
• Transfer Functions Methodology
• Box-Jenkins Methodology
• Modeling Financial Time Series
• Census II Method of Seasonal Analysis
• SPSS Programs Listing for Trends and the Box-Jenkins
• SAS Programs Listing for Exponential Smoothing and Winters Methods
• Measuring for Accuracy (Interactive Fortran Codes)