Time Series Analysis

Syllabus

Chapter 1: Introduction

      1.1 Review of Statistics

      1.2 Review of OLS Estimators: lecture note

      1.3 R Language

           [R]: http://www.r-project.org

           R package: introduction,

Chapter 2: Stochastic Process

Chapter 3: Conditional Mean Models: lecture note

       3.1 ARMA Models: Box-Jenkins Approach

           R packages: TSA, tSeries, fArma,

       3.2 Threshold Models:

            a. Structural Change Models: lecture note, Bruce E. Hansen

                R package: struchange, sac

                GAUSS: C:\gauss6.0\gjob\Zhongjun

                            constancy

               Multiple structural changes in multivariate regressions: Perron and Qu

            b. Markov-Switching Models: lecture note, Bruce E. Hansen

                R package: MSBVAR, tsDyn

            c. Threshold Models: lecture note, Bruce E. Hansem       

                        R package: TSA  (Two regime)          

            d. Self-exciting Threshold Models

       3.2 Kalman Filter and State-Space Models: lecture note

            R package: timsac, dlm, Sspir,

       3.3 Nonparametric Models:

            R package:  sm

       3.4 Unit Root and Cointegration Tests: Bruce E. Hansen's Unit Root, Cointegration

            R packages: uroot,

            MATLAB: c:\MATLAB6p1\work\PANIC 

       3.5 Applications:

            1. Campbel, Stock return predictability

               GAUSS: C:\Gauss6.0\gjob\Pred

            2. Stochastic Dominance

                GAUSS:\C:\gauss6.0\gjob\Linton

Chapter 4. Conditional Variance Models

       4.1 ARCH and GARCH Models

           R packages: fGarch

           MATLAB:   c:\MATLAB6p1\work\CAViaR, c:\MATLAB6p1\work\Shephard\Garch

                            c:\MATLAB6p1\work\CAViaR, c:\MATLAB6p1\work\Shephard\MVGarch

       4.2 Extensions of ARCH and GARCH Models

       4.3 Range-based Models

Chapter 5. Measures of Value-at-Risk

       5.1 Parametric Methods: RiskMetric Method

       5.2 Semiparametric Models: Quantile Regression, CAViaR

            R package: quantreg, C:\MyDocument\R\VaRquant, VaR

            MATLAB: c:\MATLAB6p1\work\CAViaR, c:\MATLAB6p1\work\quantile,

            GAUSS: c:\gauss6.0\gjob\qreg

                       

       5.3 Nonparametric Models: POT, EVT

            R package: fExtremes, evdbayes, POT

Chapter 6. Bayesian Methods

             R packages: mcmc, MCMCpack

Chapter 7. Bootstrap Method

              R packages: simpleboot

              MATLAB: c:\MATLAB6p1\work\Shephard\BootStrap

Chapter 8. Panel Data Model: lecture note, PANIC