Time Series Econometrics / Klaus Neusser.

PART I. UNIVARIATE TIME SERIES ANALYSIS
1. Introduction ; 1.1. Some Examples ; 1.2. Formal Definitions ; 1.3. Stationarity ; 1.4. Construction of Stochastic Processes ; 1.5. Properties of the Autocovariance Function ; 1.6. Exercises
2. ARMA Models ; 2.1. The Lag Operator ; 2.2. Some Important Special Cases ; 2.3. Causality and Invertibility ; 2.4. Computation of Autocovariance Function ; 2.5. Exercises
3. Forecasting Stationary Processes ; 3.1. Linear Least-Squares Forecasts ; 3.2. The Wold Decomposition Theorem ; 3.3. Exponential Smoothing ; 3.4. Exercises ; 3.5. Partial Autocorrelation ; 3.6. Exercises
4. Estimation of Mean and ACF ; 4.1. Estimation of the Mean ; 4.2. Estimation of ACF ; 4.3. Estimation of PACF ; 4.4. Estimation of the Long-Run Variance ; 4.5. Exercises
5. Estimation of ARMA Models ; 5.1. The Yule-Walker Estimator ; 5.2. OLS Estimation of an AR(p) Model ; 5.3. Estimation of an ARMA(p, q) Model ; 5.4. Estimation of the Orders p and q ; 5.5. Modeling a Stochastic Process ; 5.6. Modeling Real GDP of Switzerland
6. Spectral Analysis and Linear Filters ; 6.1. Spectral Density ; 6.2. Spectral Decomposition of a Time Series ; 6.3. The Periodogram and the Estimation of Spectral Densities ; 6.4. Linear Time-Invariant Filters ; 6.5. Some Important Filters ; 6.6. Exercises
7. Integrated Processes ; 7.1. Definition, Properties and Interpretation ; 7.2. Properties of the OLS Estimator in the Case of Integrated Variables ; 7.3. Unit-Root Tests ; 7.4. Generalizations of Unit-Root Tests ; 7.5. Regression with Integrated Variables
8. Models of Volatility ; 8.1. Specification and Interpretation ; 8.2. Tests for Heteroskedasticity ; 8.3. Estimation of GARCH(p, q) Models ; 8.4. Example: Swiss Market Index (SMI).

PART II. MULTIVARIATE TIME SERIES ANALYSIS
9. Introduction
10. Definitions and Stationarity
11. Estimation of Covariance Function ; 11.1. Estimators and Asymptotic Distributions ; 11.2. Testing Cross-Correlations of Time Series ; 11.3. Some Examples for Independence Tests
12. VARMA Processes ; 12.1. The VAR(1) Process ; 12.2. Representation in Companion Form ; 12.3. Causal Representation ; 12.4. Computation of Covariance Function
13. Estimation of VAR Models ; 13.1. Introduction ; 13.2. The Least-Squares Estimator ; 13.3. Proofs of Asymptotic Normality ; 13.4. The Yule-Walker Estimator
14. Forecasting with VAR Models ; 14.1. Forecasting with Known Parameters ; 14.2. Forecasting with Estimated Parameters ; 14.3. Modeling of VAR Models ; 14.4. Example: VAR Model
15. Interpretation of VAR Models ; 15.1. Wiener-Granger Causality ; 15.2. Structural and Reduced Form ; 15.3. Identification via Short-Run Restrictions ; 15.4. Interpretation of VAR Models ; 15.5. Identification via Long-Run Restrictions ; 15.6. Sign Restrictions
16. Cointegration ; 16.1. A Theoretical Example ; 16.2. Definition and Representation ; 16.3. Johansen's Cointegration Test ; 16.4. Estimation and Testing of Cointegrating Relationships ; 16.5. An Example
17. Kalman Filter ; 17.1. The State Space Model ; 17.2. Filtering and Smoothing ; 17.3. Estimation of State Space Models ; 17.4. Examples ; 17.5. Exercises
18. Generalizations of Linear Models ; 18.1. Structural Breaks ; 18.2. Time-Varying Parameters ; 18.3. Regime Switching Models
A. Complex Numbers
B. Linear Difference Equations
C. Stochastic Convergence
D. BN-Decomposition
E. The Delta Method.