Seminar in Non-Linear Econometrics
Applied Times Series Analysis
The course covers methods for analyzing univariate and multivariate time series and will balance methodological and applied concepts. Focus will be on linear and nonlinear techniques with emphasis on applications in macroeconomics, finance and risk analysis. Linear methods include univariate and multivariate autoregressive moving average models, state space models, Kalman filtering, time-varying parameter models, unit roots and cointegration, and structural vector autoregressions; and applications will predominantly come from the field of macroeconomics. Approaches to nonlinear, univariate and multivariate time series econometrics concentrate on threshold autoregression techniques, conditional heteroskedasticity, dependence dynamics, stochastic volatility, and realized volatility. Applications cover topics in macroeconomic s, finance and interactions between real and financial sectors.
Instructor: Serken Yener