Convenient Way of Extend Linear Expenditure System Modeling without Regression

Extend Linear Expenditure System model is a collection of multiple linear models, and modeling is a clearly tedious process. The innovation of this paper is trying to find a simple way of ELES modeling, which means, in order to omit the modeling process one by one, we try to use Excel functionality to create a model workplace. As long as you replace the original sample data in the workspace, you can get the results you want.


Introduction
ELES model is based on the assumption that people's demand for various goods or services in a certain period is determined by people's incomes and prices of various commodities, and the needs of people for a variety of goods are divided into two parts: people's basic needs and additional requirements exceeding the basic needs.At the same time, the model assumes that people's basic needs are irrelevant to the level of income, and people will allocate the residual income in accordance with the marginal propensity to consume to additional demands after people's basic needs are met.However, it is mainly used to calculate the marginal propensity to consume, basic consumption, income elasticity and price elasticity, which are reference quantities to study on structure change.
There are structure researches on ELES model in paper [1]- [4].They are the perfect application.Paper [5] brings the ELES into demand research.Paper [6] [7] are the early studies in ELES.For a long time, ELES has laid a foundation for the quantitative study of the economy.However, its wide application and simple principle are liked by scholars, though they do not give a simple algorithm.We can find that authors use traditional, cumbersome and conservative method to calculate the results.In order to better apply ELES to scientific research, we want to simplify the process.
ELES model is a collection of multiple linear models, and in the case of many types of data, ELES modeling is a clearly tedious process and huge workload.If you need to test it, the equations are as much as stars in the sky, the quantity will make you mad.The innovation of this paper is trying to make the modeling method more simple, which means, in order to omit the modeling process one by one, we try to use Excel functionality to create a model workplace.As long as you replace the original sample data in the workspace, you can get the results you want.

Formulas from ELES Model Derivation
The basic expression of extended linear expenditure system (the abbreviation is ELES) is 0 0  , where i C is consumers' consumer spending on commodity or service i, i Q is consumers' demand for commodity or service i, I is consumers' income, i β is the marginal propensity to consume of commodity or service i, i p is the price of commodity or service i, i q is basic de- mand for commodity or service i, 0 i i p q is basic expenditure for purchasing commodity or service i.Its economic significance is that people's demand for certain goods or services is divided into two parts: basic requirements and additional requirements.In the case of income and prices unchanged, people will allocate the residual income in accordance with the marginal propensity to consume to other all kinds of consumption of goods or services included in additional demands after people's basic needs are met.
Transform the basic expression to get . Therefore, the sample data of consumption and income can be used to estimate model's parameters.At the same time, via summation on either side of the equal sign of formula ①, calculation formula of total expenditure on basic consumption can be obtained as ③, which is used to figure out the basic expenditure for each commodity or service.
In addition, you can also derive the income elasticity of commodity or service the cross price elasticity of commodity or service i the own-price elasticity of commodity or service i ( )

Ordinary Least Squares Estimate in Parameter Estimation of ELES
Suppose residents from n different income levels have m consumer items.Sample is used to estimate parameters of m regression equations by ordinary least squares estimate (its abbreviations is OLS), process is as follows: Assume one of the m equations is ˆˆî According to the principle of OLS, equations are

Establish Workspace in Excel
In this section, data of the annual per capita consumption expenditure of urban households in 2011, divided into n levels on the basis of their income, is selected as samples to illustrate the established ELES model workspace in Excel, steps as follows: 1) Excel book is divided into four workspaces, original data area (A1:J10), the preparation area for calculation (B11:J20), ElES workspace (A21:J23) and elasticity zone (A24:J33).
2) Income group labels are established in the A1:A10, each consumption item label is established in the B2:J10, input sample data in the B2:J10, wherein, data in B10:J10 is the average level of consumption expenditures of urban residents and from NBS (National Bureau of Statistics of China).
6) Input the formula C11 − C22*B11 in C21, and copy the formula into D21:J21.7) Input the formula C21 + C22*B25 in C23, and copy the formula into D23:J23, also, it calculates the basic expenditure for purchasing each commodity or service.
8) Input the formula C22*B10/C10 in C24, and copy the formula into D24:J24, also, it calculates the income elasticity of each commodity or service.9) Input the formula SUM(C21:J21)/(1 − SUM(C22:J22)) in C25, which calculates total basic expenditure of urban households.10) Input the formula-D22*C23/D10 in C27, and copy the formula into the rectangular region C26:J33.What is more, the formula calculates the cross price elasticity of every commodity or service.
Therefore, Table 1 was obtained.

Illustration and Verification
Replace the sample data in Original data area with data of the annual per capita consumption expenditure of urban households in 2012, to verify the above workspace in excel.The results are in we use eview 5.0 software to regress the equations, whose results are in Table 3, to verify the workplace in excel.Therefore, comparing Table 2 and Table 3, its results can be found as follows: the obtained values of marginal propensity to consume are the same, pass t test, and data doesn't have heteroscedasticity.

Weighted Least Squares Estimate in Parameter Estimation of ELES
In the above discussion, perhaps the selected data happens to have just no heteroscedasticity.However, if there is heteroscedasticity data, weighted least squares estimate (its abbreviation is WLS) will be used to estimate the parameters, whose principle is adding a weight in front of residuals, and the derivation process is as follows: In the process of the establishment, put the i W that you defined in front of the formulas into the cell.i W can also be σ , 2  σ , 2 Ŝ , Ŝ or any other.The different establishment process is just slightly modified the formula, other build steps are the same as OLS.

Table 2 .
On the other hand,

Table 1 .
Dynamic ELES modeling workspace in Excel.

Table 2 .
Dynamic ELES modeling workspace of data in 2012 in Excel.

Table 3 .
Results of the regression with eview 5.0 software.