I. Statistical Concepts, Distributions and Statistical Inference
§ Standard descriptive statistics
§ Some important statistical distributions: Distributions of returns
§ Histograms and QQ-plots
§ Interval estimation
§ Hypothesis testing
§ Covariance and correlation analysis
§ Applications and examples with EViews and Excel
II. Regression Analysis
§ Simple linear regression: specification, estimation and statistical inference
§ Multiple linear regression: specification, estimation and statistical inference
§ Dummy variable regression models
§ Relaxing the regression assumptions: multicollinearity, heteroscedasticity and autocorrelation
§ Specification errors and diagnostic testing
§ Applications and examples with EViews and Excel
III. Time Series Analysis and Forecasting
§ Introduction to time series: trends, cycles, seasonality, and noise
§ Forecasting evaluation, training and test samples
§ Stationarity, autocorrelation and partial autocorrelation
§ White noise, moving average (MA) model and autoregressive (AR) model
§ Non-seasonal and seasonal ARMA models
§ Nonstationary time series and unit root tests
§ ARIMA and SARIMA models
§ Identification, estimation, diagnostic checking, selection and forecasting
§ Autoregressive conditional heteroskedasticity (ARCH) models
§ Applications and examples with EViews and Excel