Handouts

Handout Topic
Notes 1 Introduction, Basics
Notes 2 Elementary Frequency Domain Facts and Techniques
Pgram Figure: Periodograms
Notes 3 Leakage, and its Reduction by Data Windows
Notes 4 Properties of the Discrete Fourier Transform
Notes 5 Smoothing, Linear Filtering
Filter Figure: Linear Filtering of Interest Rates
Notes 6 The Fast Fourier Transform
Notes 7 The Periodogram of a Noise Series
Notes 8 Formulas Involving the Periodogram and Sample Autocovariances
Notes 9 The Spectrum
Notes10 The Spectral Representation for Weakly Stationary Processes
Notes 11 More on Linear Filters
Notes 12 Autoregressive and Moving Average Processes in Discrete Time
Notes 13 The Linear Prediction Problem
Notes 14 Spectrum Estimation
ARMAbasics The Basics of ARMA Models
NonLin Nonlinear Models
GARCH Conditional Heteroscedasticity and GARCH Models
Chaos Chaos and Nonlinear Time Series
Spectrum The Spectrum of a Weakly Stationary Process
Bispectrum The Bispectrum and Tests for Nonlinearity
UnitRoot Differencing and Unit Root Tests
Mem.Intro Introduction to Long Memory Series
TreasMem US Treasury Bills: Figures for Intro to Long Memory
FARIMA The ARIMA(0,d,0) Model
Fractal Fractals and Fractional Dimension
fBm Continuous Time Long Memory Models: Fractional Brownian Motion and Fractional Gaussian Noise
FARIMA2 The Fractional ARIMA(p,d,q) Model
Whittle Whittle's Approximation to the Likelihood Function
Semipar A Smiparametric Long Memory Model
Coint Fractional Cointegration
YuleWalker The Yule-Walker Equations
AICC The AICC Criterion for Autoregressive Model Selection