Intro
Preface
Contents
About the Author
Acronyms
1 Basics of Time Series
1.1 Useful Characteristics of Time Series
1.2 Linear Time Series Models
1.2.1 Autoregressive Models
1.2.2 Moving Average Models
1.2.3 Autoregressive Moving Average Models
1.3 Random Coefficient AR Models
1.3.1 Random Lag Autoregressive Model of Order p (RLAR(p))
1.4 Other Non-linear Time Series Models
1.5 Non-Gaussian Time Series
1.6 Model Specifications
1.6.1 Marginal Specific Models
1.6.2 Error Specific Models
1.6.3 Conditionally Specified Models
References

2 Statistical Inference for Stationary Linear Time Series
2.1 Methods of Estimation
2.2 Yule-Walker Method of Estimation
2.3 Maximum Likelihood Methods
2.3.1 ML Method for ARMA Models with Non-Gaussian Innovations
2.3.2 MLE for Stationary AR(p) Model
2.3.3 Modified Method of Maximum Likelihood Estimation (MMLE)
2.3.4 Maximum Probability Estimation
2.4 Quasi-Maximum Likelihood Method
2.5 Method of Conditional Least Squares
2.5.1 Two-Stage Conditional Least Squares Method
2.6 Generalized Method of Moments

2.7 Godambe Estimating Functions and Quasi-likelihood Methods
2.7.1 Estimating Functions for Stochastic Processes
2.7.2 Quasi-likelihood Scores Based on Conditional Mean and Variance
2.7.3 Asymptotic Theory of Estimating Functions
2.8 Other Methods of Estimation
2.9 Methods of Model Identification, Diagnosis and Forecasting
References
3 AR Models with Stationary Non-Gaussian Positive Marginals
3.1 Constant Coefficient Exponential Autoregressive Models
3.1.1 First-Order Exponential Autoregressive Models
3.1.2 Higher Order Exponential Autoregressive Models

3.1.3 ACF of EAR(p) Processes
3.2 Estimation for Stationary Exponential AR Models
3.2.1 Estimation in the Presence of Zero-Defects
3.2.2 Conditional Least Square Method for EAR(p) Models
3.3 Random Coefficient Exponential AR Models
3.3.1 Transposed EAR (TEAR) Models
3.3.2 New Exponential AR(1) (NEAR(1)) Model
3.3.3 Generalized Exponential AR(1) (GEAR(1)) Model
3.3.4 New Exponential AR(2) (NEAR(2)) Model
3.3.5 NEAR(p) Models
3.4 Estimation for Random Coefficient Exponential AR Models
3.4.1 Estimation for NEAR(1) Model
3.4.2 Estimation for NEAR(2) Model

3.4.3 Estimation in NEAR(p) Model
3.4.4 Quasi-likelihood Estimates for NEAR(p) Model
3.5 Gamma Autoregressive Models
3.5.1 Constant Coefficient Gamma AR(1) Models
3.5.2 Random Coefficient GAR(1) Models
3.5.3 Beta-Gamma ARMA Models
3.5.4 Gamma Models by Random Thinning
3.5.5 GAR(1) Models with Conditional Specifications
3.6 Estimation for Gamma Time Series
3.7 Other Non-negative Stationary AR(1) Models
3.7.1 Mixed Exponential AR(1) Model
3.7.2 Birnbaum-Saunders AR Model
3.7.3 Inverse Gaussian Time Series Models
3.7.4 Mittag-Leffler Processes
References