Time Series Analysis University of Sao Paulo
1. Concepts: stochastic processes and time series, stationarity, autocovariance function and spectrum. 2. Stationary ARMA models; ARIMA models, the general linear model and harmonic models; long memory models. 3. Non-linear Models: ARCH, GARCH and extensions. 4. Spectral Analysis: Fourier series, analysis of periodic and non-periodic functions, spectral representation of stationary processes and linear filters. 5. Estimation in the time domain: estimation of the mean and covariance function, identification, estimation and forecasting using ARIMA models and conditionally heteroscedastic models. 6. Estimation in the frequency domain: the finite Fourier transform and the periodogram and smoothed estimators; 7. State space models: definition, the Kalman filter, estimation and forecasting.
Online Course Requirement
Chang Chiann, Airlane Pereira Alencar
Site for Inquiry
Please inquire about the courses at the address below.
Email address: https://www.ime.usp.br/en