Question

A) Suppose you had a large number of variables which you believe might have some forecast information. How would you use these variables to forecast in an efficient way?

B) How would you extend this procedure to allow for dynamic effects?

Answer 1

A)

Forecasting is the estimation of the value of a variable (or set of variables) at some future point in time. In this note we will consider some methods for forecasting. A forecasting exercise is usually carried out in order to provide an aid to decision-making and in planning the future.

Applications for forecasting include:

• inventory control/production planning - forecasting the demand for a product enables us to control the stock of raw materials and finished goods, plan the production schedule, etc
• investment policy - forecasting financial information such as interest rates, exchange rates, share prices, the price of gold, etc. This is an area in which no one has yet developed a reliable (consistently accurate) forecasting technique (or at least if they have they haven't told anybody!)
• economic policy - forecasting economic information such as the growth in the economy, unemployment, the inflation rate, etc is vital both to government and business in planning for the future.

Even if you have only a handful of predictor variables to choose from, there are infinitely
many ways to specify the right hand side of a regression. How do you decide what variables to
include? The most important consideration when selecting a variable is its theoretical
relevance. A lot of things can go wrong when you add variables willy-nilly without a reasonably
sound theoretical foundation (also known as “regressing faster than you think.”) Of course, the
definition of “reasonably sound” is a bit murky, and you can do just as much harm by excluding
too many variables.

B)

dynamic analysis can be used to find:
• natural frequency
• dynamic displacements
• time history results
• modal analysis