Time Series Analysis

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The course provides an accessible introduction to the application of time series methods. Topics covered include an introduction to the dynamic properties of time series, stochastic difference equations, stationary univariate models, forecast evaluation, state-space models, non-stationary models and unit roots, vector autoregression models, structural vector autoregression models, Bayesian vector autoregression models, dynamic factor models and factor augmented vector autoregression models, cointegration and error-correction, heteroskedastic and stochastic volatility models, as well as nonlinear regime-switching models. The course will also emphasize recent developments in time series analysis and areas of ongoing research. The main objective of the course is to develop the skills that are needed to conduct empirical research using time series data. Therefore, the course provides students with an understanding of the techniques that are required to select, estimate, and assess the qual

Time Series Analysis Time Series Analysis ECO5069S - Semester II Kevin Kotzé (Room 5.04) 1 Lecture times: Tuesday 16H00 (LT1) Tutorial times: Thursday 16H00 (Lab) 1 Course Description The course provides an accessible introduction to the application of time series methods. Topics covered include an introduction to the dynamic properties of time series, stochastic difference equations, stationary univariate models, forecast evaluation, state-space models, non-stationary models and unit roots, vector autoregression models, structural vector autoregression models, Bayesian vector autoregression m

Time Series Analysis

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