# Econometric Forecasting Models

Econometric model building holds considerable promise as a method of forecasting demand. The best  starting point towards an understanding of the basis of econometric forecasting is regression analysis. But  the difficulty with regression analysis is that it is used to forecast a single dependent variable based on the  value and the relations between one or more independent variables and each of these independent variables is  assumed to be exogenous or outside the influence of the dependent variable. This may be true in many  situations. But unfortunately, in most broad economic situations an assumption that each of the variable,  is independent is  unrealistic.

For example, let us assume that demand is a function of Gross National Product (GNP), price and  advertising. In  regression  terms we would assume that all three independent variables are exogenous to  the system and hence are not influenced by the level of demand itself or by one another. This is  fairly  correct assumption so far as GNP is concerned. If, however, we consider price and advertising, the same  assumption may not hold good, for instance, of the per unit cost is of some quadratic form, a different  level of cost. Again, advertising expenditures will often influence the price of the product, since the  production and selling cost influence the per-unit price. The price, in turn, is influenced by the magnitude  of demand, which can also influenced by the magnitude of demand, which can also influence the level of  advertising or promotional expenditure. All of these point to the independence of all four of the valuables  in our equation. When this independence is strong, regression analysis cannot be used. If we want to be  accurate, we must express this demand relationship by developing a system of four simultaneous equation  that to be accurate, we must express this demand relationship by developing a system of four  independent equations.

Thus is econometric form we can have;

1. Demand = f (GNP, price and  advertising)
2. Cost = f (production and inventory levels)
3. Selling expenses = f (advertising and other selling expenses)
4. Price = f (cost and selling expenses)

That is, instead of one relationship, we now have four. As in regression analysis, we must (a) determine the  functional form of each of the equations (b) estimate in a simultaneous manner the value of their  parameters and (c) test for the statistical significance of the results and validity of the assumption. It  should be realized that the advantages of econometric forecasting is that is provides the values of several  of the independent variables from within the model itself, thus freeing he forecaster from having to  estimate them exogenously.

The estimation of the equation parameters involves problems for more complex than those encountered in  regression analysis. This makes the application of econometric forecasting difficult and expensive.

An econometric model includes a number of simultaneous equation that can be of different types and  functional forms. The translation of econometric theory to the right type of form of equation and their  development into a set of functional relationship is termed as specification. The accurate and most  appropriate specification of an econometric model is a key step in use of this technique of forecasting.

A major part of specification is the identification of the exogenous and endogenous variables, one must  arbitrarily decide on the degree of influence of the different factors and choose those that are least  determined within the system as exogenous factors. This is kind to the distinction made in regression  analysis between the independent variables and the dependent variables. in an econometric model we will  want to separate those factors that are most strongly influenced by one another into the endogenous  group and those than can be assumed to be determined outside the system of simultaneous equations  into the exogenous group.

Once the choice of  endogenous  and exogenous variables has been made, on equation must be specified  for each of the endogenous variables. When the number of the equations specified is equal to the number  of exogenous variables, the model is said to be just specified. When the number of equations, the model is  under specified and one or more of the variables has to be set arbitrarily to some initial value. these  variables then become exogenous variables is greater than the number of equations, the model that is  most often used for estimating the parameters of a set of simultaneously equations.

Econometric forecasting models are used most widely to forecast macro series of inter related economic data such as  income, consumption and capital spending and much less for business forecasts.

The great advantage of econometric forecasting models  are indirect. It can be used to predict the direction and extent  of change of the overall economic activity or any its components. This information can then become the  input required to estimate the independent variables of a single equation forecasting model. Since this  information can be obtained from outside sources, organizations do not have to develop their own models  but can rely on outsiders to provide them with forecasts when they are required. Thus individual  companies can forego all the high costs associated with developing maintaining, and running a large scale  econometric model and obtain he information it offers through third parties.