Techniques for Demand Estimation in Economics and Their Applications

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Estimation of probable demand is crucial for economic planning. This method utilizes past demand data to forecast future demand, aiding in decision-making, budgeting, and pricing strategies. It helps in managing business fluctuations, understanding consumer needs, and analyzing market influences. Methods such as census and sample surveys provide valuable insights by either covering the entire population or selecting a sample. Time series methods like linear equations further enhance predictive capabilities.

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Sandeep Sundas(7063082473) ASSISTANT PROFESSOR IN ECONOMICS Kaliyaganj College

It is a technique for estimation the probable demand for a product or services in the future. It is a technique of estimating the present demand on the basis of past demand for a product or service. It is a scientific and statistical method in estimating the future demand.

It is useful for economic planning. It is useful in meeting efficient decision. Usefulness in accounting. Formulate a pricing policy. Preparations of budget.

To get rid of business fluctuations. To get rid of over demand or over supply. To understand the necessity of a good for different consumers. Forecast sales and revenue. It analyze the market influences on different products. To match the competitive strength of business unit. Planning for inventory control.

Purchasing power of customers Demography Price Replacement demand Socio-economic conditions Conditions within the industry.

Census and Sample survey method: Census survey or complete enumeration method is the method of collecting information from each and every individual of a population under review. Sample survey method: Sample survey method is the method of randomly selecting a sample from the population under review.

Sl. No Census Survey 1 It deals with the entire observation of a population under review. Sample Survey It deals with a sample of observations from the population under review. The information collected are approximately accurate. It requires less time. The method is economical. There may be sampling error. Less reliable and accurate. Appropriate for population of homogenous nature. 2 It presents the accurate information. 3 4 5 6 7 It is a time consuming method. It is very costly. No error. The results are reliable and accurate. Appropriate for population of heterogeneous nature.

It is a time series method. We start with the linear equation y= a+bx under the least square conditions y= na+b x and xy=a x+b x2 For example Suppose the year and sales of certain firm is given 1990, 1991, 1992, 1993, 1994, 1995, 1996 and you are required to predict the sales for 1998.

You are required to construct the following Y is the sales that will be given in the question. Construct x( in order to construct x, if the number of observations are odd then we assign 0 to the middlemost year and increase x by 1 to the succeeding years and decrease x by 1 to the preceding years). After constructing x you are supposed to find the summation of xy, x2, x and y. With two least square conditions find the value of a and b. Put the value of a and b in the equation y= a+bx. Now for 1998 what would be the value of x? Put that value of x and you will get y. This is the required predicted value.

Under the regression method we can find the regression equation of x on y and that of y on x. The values for two variables x and y will be given and you are supposed to construct x, y, x2, y2 and xy. Suppose that x is a rainfall and y is production/yield. One variable depending on the other. We can find the value of y when x is given and vice versa. This way we can predict the value using regression method.