How to Select a mos tefficien t‘ols’ model for A time Series Data

HOW TO SELECT A MOST EFFICIENT ‘OLS’ MODEL FOR A TIME SERIES DATA By John C. Pickett, David P. Reilly and Robert M. McIntyre Ordinary Least Square (OLS) models are often used for time series data, though they are most appropriated for cross-sectional data … provides a check list of conditions that must be satisfied for an OLS model to be most efficient … also, gives sufficiency variables that can be used to overcome various problems in the model. Practicing forecasters seek techniques that maximize forecasting accuracy and minimize forecast error. Their usual challenge is to make forecasts of the next period on the basis of time series data, which has a monthly, quarterly or annual data. Despite the tomes of econometricians’ ponderous recommendations residing in (sometimes dusty) university libraries, it is not unusual for many practitioners to resort to an “old friend” — the ordinary least squares (OLS) model. In this article, we will show how to use our old OLS friend for optimum results. We also provide ways of identifying and estimating the most efficient OLS model, the model that minimizes the forecasting error. OLS models, developed in the early 20th Century, ...