Week 4; Stationary and Non Stationary Time Series
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Course Material
- Week 1; Time series , its types, Objectives and Components
- Week 2; Stochastic Process
- Week 3; Filters and Operators
- Week 4; Stationary and Non Stationary Time Series
- Week 5; Transformation of Time Series Data
- Week 6; Important Models of Time Series (Purely Random Process and Random Walk )
- Week 7; Auto-regressive and Moving Average Models with properties and Diagnostic Checking
- Week 8; ARIMA and ARMA Model (ARIMA,ARMA, Seasonal ARMA, Seasonal ARIMA Models)
- Week 9; Model Selection Criteria and Model Diagnostic Checking
- Week 10; The Box Jenkins Method
- Week 11; ARCH, GARCH Models
- Week 12; State Space Models and Kalman Filters
- Week 13; Granger Causality
- Week 14; Vector Autoregressive Models
- Week 15; Time Series Forecasting
- Week 16; Cointegration Analysis and Johansen Approach
- Chapters 16
- Department Statistics
- Teacher
Ms. Sadia Qamar
