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MFIN 6606 Forecasting Techniques (Fall: 3 )

Course Description

The theory and practice of applied time series analysis will be explored. First the different segments (trend, seasonality, cyclical and irregular) of a time series will be analyzed by examining the Autocorrelation functions (ACF) and Partial Autocorrelation functions (PACF). The specifics model to model the various types of time series include linear regression, panel regression, seasonal decomposition, exponential smoothing, ARIMA modeling as well as combining models.

Instructor(s): Richard McGowan, S.J.

Prerequisites: Undergraduate prerequisite-ECON1151orECON1155 Statistics/Graduate prerequisite -OPER7725

Cross listed with: OPER6606 ECON2229


Last Updated: 13-Aug-14