Chapter 1. TIME SERIES ECONOMETRICS FOR BUSINESS. PROPERTIES OF TIME SERIES AND STATISTIC FRAMEWORK FOR THEIR ANALYSIS
1.1 Econometrics for economists. Quantitative methods for business
1.2 Random samples and properties of time series
1.3 Components of a time series: trend, seasonality, cycle and disturbances.
1.4 Trend and seasonal. Stationarity transformations
1.7.1 Deterministic trends and seasonality
1.7.2 Segmented trends
1.7.3 Stochastic trend and seasonality
Chapter 2. LINEAR UNIVARIATE ARIMA MODELS
2.1 Stationary stochastic processes
2.2 Autocorrelation function and its estimation
2.3 White noise process
2.4 First order autoregressive model: AR (1)
2.5 Generalization to AR (p) models
2.6 Integrated models: ARI (l, p)
2.7 ARMA and ARIMA models
Chapter 3. SPECIFICATION, ESTIMATION AND DIAGNOSIS OF ARIMA MODELS
3.1 Box-Jenkins methodology
3.2 Initial specification
3.2.1 Unit root tests
3.2.2 Analysis of correlograms and partial correlograms of the original series and its transformations
3.2.3 Information criteria
3.3 Estimation: hipothesis testing
3.4 Diagnosis of ARIMA models:
a) Residual analysis
b) Tests of alternative models
Chapter 4. STATIONARY MULTIVARIATE MODELS
4.1 Stationary VAR(p) model.
4.2 Granger causality.
4.3 Estimation of VAR models
4.4 VAR models with exogenous variables
4.5 Uniequational dynamic models: autoregressive distributed lag models (ADL)
4.6 Impact and long run multipliers
Chapter 5. NON-STATIONARY MULTIVARIATE MODEL
5.1 Models with integrated variables. Spurious regression.
5.2 Cointegration
5.3 Vector equilibrium correction models (VEqCM)
