Estimation of Import Demand Functions - Assignment Example

Running head: MBA 5303 MANAGERIAL ECONOMICS Estimation of Import Demand Functions Name Course Information Professor Information Date Due Introduction This paper is a report covering on estimation of import demand function. In order to achieve this objective, textile importation for Qatar and USA will be used. The equation that will be estimated follows the form: Imports of textile = f (price, income) consequently resulting to two demand equations. Conclusions will be drawn based on the two equations. It is further pertinent to assess the statistical qualities of each model using F-test, t-test, and R-square. Given the result of the analysis, various published articles on textile in US and Qatar will be drawn into the discussion to facilitate comparison and contrast. Textile for Qatar and USA Imports of textile = f (price, income) After converting data into log-linear form and by conducting multiple regressions in excel, the estimated demand equation for the two countries follows the form: Qatar: USA: Given the log-linear form, the procedure for obtaining price elasticity of demand is by looking at absolute values of constants. However, negative sign in income elasticity is not eliminated. In this case, income elasticity and price elasticity of demand for the respective countries is summarized in the table below:   Qatar USA Income elasticity of demand 1.401897 2.4419 Price elasticity of demand 1.3349 0.0956 It is apparent from the table above that price elasticity of demand of textile importation in Qatar is elastic given that 1.3349 is more than 1. At the same time, price elasticity of demand of textile importation in US is inelastic since 0.0956 is less than 1. Considering income elasticity of demand, USA imports is more sensitive to changes in income compared with Qatar. Income elasticity of demand in both countries are more than 1 thus can be concluded that textile importation is a luxury good. Application of estimated elasticity First, income elasticity helps a business to predict the future demand for a product through estimation if the rate of change in income and the income elasticity of demand are know (Mankiw, 2012). When a business anticipates a change in personal income, it can be able to forecast demand. Secondly, knowledge of income elasticity assists an individual to identify clearly a normal from an inferior good. While normal good has positive income elasticity for any level of income, an inferior good has negative income elasticity when income is increased past a particular level. A business will also be able to use price elasticity of demand to note when to increase prices to register large profits. Often, businesses decide to increase prices because of an increase in cost of production. In other situations, a business would decide to manipulate prices even when costs of production have not changed. In either case, a decision to increase prices is determined by price elasticity of demand and cross elasticity. The reason behind this assertion is that a rise in price of a product causes price of substitutes to become cheaper. Consumers would move in to buy such substitutes. Nonetheless, a rise in price is good for a firm when demand for the good is inelastic. At the same time, demand for substitute product should attain a cross price elasticity of less than 1 (Dwivedi, 2011). Knowledge on the elasticity helps a business to make wise decisions particularly in generating higher revenue. A business can further deploy knowledge of elasticity of demand to understand factors that affect a product particularly in terms of dependent and independent variables. It is further pertinent to note that elasticity serves the purpose of making estimates of demand function. An example of a function that gives a relationship between dependent and independent variables is the estimate demand function: Imports of textile = f (price, income). A businessperson therefore knows that importation of textiles varies with prices and income. Statistical Adequacy of Equations R-square In the case of USA, R-square is 0.9864, which indicates that 98.64% of variation in dependent variables is explained by regression equation. This percentage is very high which means that the line gives a better fit to the data. Looking at Qatar, R-square is 0.2732. This implies that only 27.32% of variation in dependent variable is explained by regression equation. The conclusion here is that the regression line does not give the best fit. F-test This test is conducted in order to understand lack of fit. The F-test is a test on equality of variances. The hypotheses are as follows: The output from excel for USA are below: F-Test Two-Sample for Variances   Variable 1 Variable 2 Mean 16.03579 -0.34344 Variance 0.076672 0.040146 Observations 32 32 df 31 31 F 1.909848 P(F F critical hence we reject null hypothesis and conclude that variances of the two population are not equal. The output from excel for Qatar are below: F-Test Two-Sample for Variances   Ln (Income) Ln (Price) Mean 10.73226 -0.53771 Variance 0.469133 0.058066 Observations 32 32 df 31 31 F 8.079261 P(F F critical hence we reject null hypothesis and conclude that variances of the two population are not equal. t-Test Student’s t-test is conducted to test whether there is significant relationship between independent variable and dependent variable. A situation that depicts significant relationship will not result in a slope of zero. Hypotheses are stated as follows: Excel output for USA: t-Test: Paired Two Sample for Means   Ln (Income) Ln (Price) Mean 16.03579 -0.34344 Variance 0.076672 0.040146 Observations 32 32 Pearson Correlation 0.867306 Hypothesized Mean Difference 0 df 31 t Stat 645.8526 P(T Read More