LG Company: Demand Estimation

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Table of Contents

Estimation of the Demand Function
Estimation of Elasticities
References

Estimation of the Demand Function

OLS Regression Analysis

Table 1.

Model Summary
Model	R	R Square	Adjusted R Square	Std. The error of the Estimate
1	.857^a	.735	.691	7.704
a. Predictors: (Constant), Total Sales, Price of LG, Advertisement Expenditure, Price of Electricity, Price of Mitsubishi

Table 2.

ANOVA
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	4933.629	5	986.726	16.624	.000^b
	Residual	1780.676	30	59.356
	Total	6714.306	35
a. Dependent Variable: Quantity of LG
b. Predictors: (Constant), Total Sales, Price of LG, Advertisement Expenditure, Price of Electricity, Price of Mitsubishi

Table 3

Coefficients
		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
Model		B	Std. Error	Beta	t	Sig.
1	(Constant)	45.379	14.943		3.037	.005
	Price of LG	-.001	.000	-.296	-2.832	.008
	Price of Mitsubishi	.000	.000	-.180	-1.072	.292
	Price of Electricity	-.375	4.189	-.014	-.090	.929
	Advertisement Expenditure	.000	.000	.499	3.751	.001
	Total Sales	.000	.000	.633	5.127	.000
a. Dependent Variable: Quantity of LG

Estimation of the Influence

Estimated Influence of Price of LG

The coefficient of the price of LG is negative, which means that there is a negative relationship between the price of LG and the quantity of LG. The estimated influence is that a unit change in the price of LG results in 0.001 changes in the quantity of LG in opposite direction. For example, a unit increase in the price of LG causes the quantity of LG to decline by 0.001 units.

Estimated Influence of Price of Mitsubishi

The coefficient of the price of Mitsubishi is zero (0.000), which means that the price of Mitsubishi and the quantity of LG have no relationship. The coefficient shows that price of Mitsubishi has no influence on the demand for LG air conditioners. Hence, the absence of the estimated influence means that Mitsubishi is not a substitute product of LG air conditioner.

Estimated Influence of Price of Electricity

The coefficient of the price of electricity is negative, which shows that the price of electricity and the quantity of LG have an inverse relationship. In essence, the estimated influence is that a unit change in the unit change in the price of electricity results in 0.375 changes in the quantity of LG in the opposite direction. For instance, an increase in the price of electricity by a unit results in a decline in the quantity of LG by 0.375 units. Hubbard, Garnett, and Lewis (2012) note that a product that has a negative relationship with the demand for a given product is a complementary product. Therefore, the estimated coefficient reveals that electricity is a complementary product of LG air condition.

Estimated Influence of Advertisement Expenditure

From Table 3, it is apparent that the coefficient of advertisement expenditure is zero (0.000), which implies that there is no relationship between the advertisement expenditure and the quality of LG. Regarding the estimated influence, the coefficient indicates that the advertisement expenditure has no influence on the demand for LG. In this view, it means that advertisement does not promote the demand for LG air conditioners.

Estimated Influence of Total Sales

Given that the coefficient of total sales is zero (0.000), it means that total sales and quantity of LG have no relationship. The absence of a relationship indicates that total sales do not influence the demand of LG in the market. Hence, a change in the total sales does not lead to a change in the demand of LG.

Estimation of Elasticities

Log-log Regression Analysis

The log-log model (Table 4) indicates that the predictors, namely, lots, nlADVE, lnPLG, lnPMIT, and lnPELC, have a strong relationship with lnQLG (R = 0.741). The R-square indicates that these predictors explain 54.8% of the variation in lnQLG (R² = 0.867).

Table 4.

Model Summary
Model	R	R Square	Adjusted R Square	Std. The error of the Estimate
1	.741^a	.548	.473	.45630
a. Predictors: (Constant), Natural log of TS, Natural log of PLG, Natural log of ADVE, Natural log of PELC, Natural log of PMIT

The F-statistics of the log-log model is statistically significant in predicting the influence of lots, nlADVE, lnPLG, lnPMIT, and lnPELC on lnQLG, F(5,30) = 7.286, p = 0.000).

Table 5.

ANOVA
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	7.585	5	1.517	7.286	.000^b
	Residual	6.246	30	.208
	Total	13.831	35
a. Dependent Variable: Natural log of QLG
b. Predictors: (Constant), Natural log of TS, Natural log of PLG, Natural log of ADVE, Natural log of PELC, Natural log of PMIT

Table 6.

Coefficients
		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
Model		B	Std. Error	Beta	t	Sig.
1	(Constant)	20.683	9.913		2.086	.046
	Natural log of PLG	-2.652	.951	-.378	-2.790	.009
	Natural log of PMIT	-.011	.024	-.095	-.467	.644
	Natural log of PELC	.110	.649	.030	.169	.867
	Natural log of ADV	.058	.021	.468	2.748	.010
	Natural log of TS	.605	.204	.434	2.968	.006
a. Dependent Variable: Natural log of QLG

Computation of Elasticity Coefficients

Log-log equation

lnQLG = 20.683 – 2.652lnPLG – 0.011lnPMIT + 0.110PELC + 0.058ADVE + 0.605TS.

Given that:-

lnPLG = 10
lnPMIT = 10
lnPELC = 1
lnADVE = 10
lnTS = 15
lnQLG = 20.683 – 2.652 (10) – 0.011(10) + 0.110(1) + 0.058(10) + 0.605(15).
inlQLG = 20.683 – 26.52 – 0.11 + 0.11 + 0.58 + 9.075
lnQLG = 3.818

Price Elasticity of LG Demand

According to the estimation coefficient of PLG, one percent increase in the price of LG air conditioner results in a decline of the LG demand by 2.652 percent when other predictors remain constant. The price elasticity of LG demand shows that the price and LG demand have negative relationship, which is very elastic. Sexton (2012) asserts that price elasticity of demand is an economic parameter, which quantifies the sensitivity of demand to price changes in the market.

E_PLC=(dQ/dLG)*PLC/Q

E_LG= -2.652(10/3.818)

E_LG= -6.946

PMIT Elasticity of LG Demand

The estimation coefficient of PMIT indicates that a one percent increase in the price of Mitsubishi causes a decline in the demand of LG by 0.011 percent when other predictors remain constant. The PMIT elasticity of LG demand calculated as shown below has a negative value, which means that price of Mitsubishi and the LG demand has a negative relationship, which is moderately inelastic. Nechyba (2010) explains that the elasticity coefficient that is greater than one is elastic while the elasticity coefficient that is less than one is inelastic.

E_PMIT=(dQ/dPMIT)*PMIT/Q

E_PMIT= -0.011(10/3.818)

E_LG= -0.0288

PELC Elasticity of LG Demand

The estimation coefficient of PELC shows that a one percent increase in the price of electricity results in a 0.11 percent increase in the demand of LG when other predictors remain constant. The PELC elasticity of LG demand determined as indicated below shows that the price of electricity and LG demand have a positive relationship, which is relatively inelastic.

E_PELC=(dQ/dPELC)* PELC/Q

E_PELC= 0.11(1/3.818)

E_PELC= 0.0288

ADVE Elasticity of LG Demand

The estimation coefficient of ADVE predicts that a one percent increase in the advertisement expenditure causes a 0.058 percent increase in the demand of LG. The ADVE elasticity of LG demand reveals that the advertisement expenditure and the LG demand have a positive relationship, which is relatively elastic.

E_ADV=(dQ/dADV)*ADV/Q

E_PMIT= 0.058(10/3.818)

E_LG= 0.1519

TS Elasticity of LG Demand

The estimation coefficient of TS shows that a one percent increase in the total sales causes the demand for LG to increase by 0.605. Thus, the TS elasticity of LG demand indicates that total sales and LG demand have a positive relationship, which is moderately elastic.

E_TS=(dQ/TS)*TS/Q

E_PMIT= 0.605(15/3.818)

E_LG= 2.3769

Determination of Statistical Significance

The linear demand model as shown in Table 1 indicates that total sales (TS), price of LG (PLG), advertisement expenditure (ADVE), price of electricity (PELC), and price of Mitsubishi (PMIT) are determinants of the quantity of LG because they have a very strong relationship (R = 0.857). The R-square reveals that these determinants of demand explain 73.5% of the variation in the demand for LG air conditioners (R² = 0.735).

The F-test determines if the regression model is statistically in predicting the influence of independent variables on a dependent variable. The F-test indicates that the regression model is statistically significant in predicting the influence of total sales, price of LG, advertisement expenditure, price of electricity, and price of Mitsubishi on the quantity of LG air conditioner, F(5,30) = 16.624, p = 0.000).

Table 3 below indicate that price of LG (p = 0.005), advertisement expenditure (p = 0.001), and total sales (p = 0.000) are statistically significant predictors of LG demand because their t values are greater than 2 or their p values are less than 0.05. In contrast, the price of Mitsubishi (p = 0.292) and price of electricity (p = 0.929) are statistically insignificant predictors of LG demand because their t values are less than 2 or their p values are greater than 0.05.

References

Hubbard, G., Garnett, A., & Lewis, P. (2012). Essentials of Economics. New York: Person Higher Education.

Nechyba, T. (2010). Microeconomics: An Intuitive Approach with Calculus. New York: Cengage Learning.

Sexton, R. (2012). Exploring Economics. New York: Cengage Learning.

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