Methods opted for analysis

Univariate time series analysis is very common in Econometric where Autoregressive (AR), Moving Average (MA) and Autoregressive integrated Moving average (ARIMA) are used. However, dealing a time series data with many predictor variables using latent variables and principal components methods is unconventional. This thesis aims to analysis a time series with financial and commodity data, as predictor, using statistical regression methods such as – Multiple Linear Regression, Ridge Regression, Principal Component Regression (PCR) and Partial Least Square (PLS) Regression. Apart from these, a subset models which selected from the Multiparty Linear Regression using various criteria are also used. An application of PCR and PLS on time series data makes this thesis distinct.