8/2/2019 Barometric Techniques or Lead

1/2

Barometric Techniques or Lead-Lag indicators method: This consists in discovering a set of series of

some variables which exhibit a close association in their movement over a period or time.

For example, it shows the movement of agricultural income (AY series) and the sale of tractors (ST

series). The movement of AY is similar to that of ST, but the movement in ST takes place after a years

time lag compared to the movement in AY. Thus if one knows the direction of the movement in agriculture

income (AY), one can predict the direction of movement of tractors sale (ST) for the next year. Thus

agricultural income (AY) may be used as a barometer (a leading indicator) to help the short-term forecast

for the sale of tractors.

Generally, this barometric method has been used in some of the developed countries for predicting

business cycles situation. For this purpose, some countries construct what are known as diffusion

indices by combining the movement of a number of leading series in the economy so that turn ing points

in business activity could be discovered well in advance. Some of the limitations of this method may be

noted however. The leading indicator method does not tell you anything about the magnitude of the

change that can be expected in the lagging series, but only the direction of change. Also, the lead period

itself may change overtime. Through our estimation we may find out the best-fitted lag period on the past

data, but the same may not be true for the future. Finally, it may not be always possible to find out theleading, lagging or coincident indicators of the variable for which a demand forecast is being attempted.

Simultaneous Equations Method: Here is a very sophisticated method of forecasting. It is also known

as the complete system approach or econometric model building. In your earlier units, we have made

reference to such econometric models. Presently we do not intend to get into the details of this method

because it is a subject by itself. Moreover, this method is normally used in macro-level forecasting for the

economy as a whole; in this course, our focus is limited to micro elements only. Of course, you, as

corporate managers, should know the basic elements in such an approach.

The method is indeed very complicated. However, in the days of computer, when package programmesare available, this method can be used easily to derive meaningful forecasts. The principle advantage in

this method is that the forecaster needs to estimate the future values of only the exogenous variables

unlike the regression method where he has to predict the future values of all, endogenous and exogenous

variables affecting the variable under forecast. The values of exogenous variables are easier to predict

than those of the endogenous variables. However, such econometric models have limitations, similar to

that of regression method.

Regression method

These involve the use of econometric methods to determine the nature and degree of association

between/among a set of variables. Econometrics, you may recall, is the use of economic theory, statisticalanalysis and mathematical functions to determine the relationship between a dependent variable (say,

sales) and one or more independent variables (like price, income, advertisement etc.). The relationship

may be expressed in the form of a demand function, as we have seen earlier. Such relationships, based

on past data can be used for forecasting. The analysis can be carried with varying degrees of complexity.

Here we shall not get into the methods of finding out correlation coefficient or regression equation; you

must have covered those statistical techniques as a part of quantitative methods. Similarly, we shall not

8/2/2019 Barometric Techniques or Lead

2/2

go into the question of economic theory. We shall concentrate simply on the use of these econometric

techniques in forecasting.

We are on the realm of multiple regression and multiple correlation. The form of the equation may be:

DX = a + b1 A + b2PX + b3Py

You know that the regression coefficients b1, b2, b3 and b4 are the components of relevant elasticity of

demand. For example, b1 is a component of price elasticity of demand. The reflect the direction as well as

proportion of change in demand for x as a result of a change in any of its explanatory variables. For

example, b2< 0 suggest that DX and PX are inversely related; b4 > 0 suggest that x and y are substitutes;

b3 > 0 suggest that x is a normal commodity with commodity with positive income-effect.

Given the estimated value of and b i, you may forecast the expected sales (DX), if you know the future

values of explanatory variables like own price (PX), related price (Py), income (B) and advertisement (A).

Lastly, you may also recall that the statistics R2 (Co-efficient of determination) gives the measure of

goodness of fit. The closer it is to unity, the better is the fit, and that way you get a more reliable forecast.

The principle advantage of this method is that it is prescriptive as well descriptive. That is, besides

generating demand forecast, it explains why the demand is what it is. In other words, this technique has

got both explanatory and predictive value. The regression method is neither mechanistic like the trendmethod nor subjective like the opinion poll method. In this method of forecasting, you may use not only

time-series data but also cross section data. The only precaution you need to take is that data analysis

should be based on the logic of economic theory.

Linear Regression

Regression analysis is a forecasting tool utilizing the relationship between quantitative variables so one variable (thedependent variable) can be predicted from another (the independent variable). The term linear regression, or simplelinear regression indicates that the value of the dependent variable is estimated on the basis of one independentvariable.Polynomial Regression is an extension of Multiple Regression. It is a non-linear regression model, that uses thesecond or higher order of the independent series. ForecastX also combines the power of Stepwise Regression intoPolynomial Regression to find a best regression model.

Box Jenkins (ARIMA modeling)

Box-Jenkins is a multi-step model-building strategy aimed at analyzing and forecasting time-series data. Box-Jenkinsmethodology looks for an adequate model in the group of models known as Auto Regressive (AR), Moving Average(MA), Auto Regression Moving Average (ARIMA) and Auto Regression Integrated Moving Average (ARIMA)processes.