Sampson Michael. Time Series Analysis

Loglinear Publishing, 2001. — 239 p.Guides economics students in the use of econometrics and forecasting, up to and including multivariate time series.Trends, Cycles and Seasonality:The trends.
Seasonality.
Modeling and Cycles.Stationary Stochastic Process:The short memory property.
The AR(1) model.
The autocovariance function.
The autocorrelation function.
Forecasting.
The backward shift operator.
The Wold representation.
The Yule-Walker Equation.AR(p) process.Some derivation using B.
The Wold representation for AR(p) process.
Stationarity conditions for AR(p) process.
Forecasting.
Some worked examples.
Estimation and Hypothesis testing.
The partial autocorrelation function.ARMA(p,q) models:
MA(q) process.
Invertibility.
Mixed process.
Box-Jenkins identification.
Maximum likelihood estimation.
ARIMA(p,d,q) models.
Diagnostic tests.TS versus DS models:Implication of TS approach.
Implication of DS approach.
Summary of differences.
Testing for Unit Roots.Multivariative time series:
VARMA(p,q) models.
VAR(p) estimation.
Granger causality.
Forecasting.
Cointegration.Special topics:
The frequency domain.
Fractional differencing.
Non-linearity and Non-normality.