Introduction; 1. the nature of time series; 1.1 description of time series; 1.2 white noise; 1.3 stationarity; 1.4 transformations of time series; 1.5 trend, seasonal and irregular patterns; 1.6 arma models of time series; 1.7 stylized facts about time series; 2. difference equations; 2.1 linear difference equations; 2.2 lag operator; 2.3 the solution of difference equations; 2.3.1 particular solution and lag operators; 2.3.2 solution by iteration; 2.3.3 homogenous solution; 2.3.4 particular solution; 2.4 stability conditions; 2.5 stability and stationarity. 3. univariate time series3.1 estimation of an arma model; 3.1.1 autocorrelation function -- acf; 3.1.2 partial autocorrelation function -- pacf; 3.1.3 q-tests; 3.1.4 diagnostics of residuals; 3.1.5 information criteria; 3.1.6 box-jenkins methodology; 3.2 trend in time series; 3.2.1 deterministic trend; 3.2.2 stochastic trend; 3.2.3 stochastic plus deterministic trend; 3.2.4 additional notes on trends in time series; 3.3 seasonality in time series; 3.3.1 removing seasonal patterns; 3.3.2 estimating seasonal patterns; 3.3.3 detecting seasonal patterns; 3.3.4 hodrick-prescott filter. 3.4 unit roots3.4.1 dickey-fuller test; 3.4.2 augmented dickey-fuller test; 3.4.3 phillips-perron test; 3.4.4 shortcomings of the standard unit root tests; 3.4.5 kpss test; 3.5 unit roots and structural change; 3.5.1 perron's test; 3.5.2 zivot and andrews' test; 3.6 detecting a structural change; 3.6.1 single structural change; 3.6.2 multiple structural change; 3.7 non-linear structure and conditional heteroskedasticity and non-linear structure; 3.7.1 conditional and unconditional expectations; 3.7.2 arch model; 3.7.3 garch model; 3.7.4 detecting conditional heteroskedasticity. 3.7.5 the bds test3.7.6 an alternative to the bds test: integration across the correlation integral; 3.7.7 identification and estimation of a garch model; 3.7.8 extensions of arch -type models; 3.7.9 multivariate (g)arch models; 3.7.10 structural breaks in volatility; 4. multiple time series; 4.1 var models; 4.1.1 structural form, reduced form, and identification; 4.1.2 stability and stationarity of var models; 4.1.3 estimation of a var model; 4.2 granger causality; 4.3 cointegration and error correction models; 4.3.1 definition of cointegration; 4.3.2 the engle-granger methodology.