Time Series: Multi-Tapering and Parametric Estimation

This lecture covers the concepts of Multi-Tapering and Parametric Estimation in Time Series analysis. Multi-Tapering involves using a set of tapers to estimate the spectral density, while Parametric Estimation focuses on fitting AR models to time series data. The instructor explains the spectral estimation process, Yule-Walker method, and the importance of AR models in approximating continuous spectra. The lecture also delves into the Whittle Likelihood method for estimating noise variance and the use of Discrete Fourier Transform in analyzing time series data.