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Econometrics Case Study. The main objective of this project is to apply the econometrics methods that we have obtained through out the course to create a reliable and useful model for capturing the potential relationship between variables of interest.

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Introduction

Submitted to Abdulnasser Hatemi Jarabad Done by: Hiba Al Farsi Khadija ali Al Hanjiri 200810792 Sec : Content Executive summary In this report we are going to examine the relationship between the variable to estimate a good econometrics model .An econometric model is only as good as the underlying desirable assumptions. If these assumption are not fulfilled the model is useless. Using Diagnostic tests would help us in testing wither the assumption is fulfilled or not. And these are: No Misspecification, No Autocorrelation, Homoscedasticity and normality. If any of the assumption is not fulfilled then a potential remedy should be found before the model can be used .Then in each testing we will demonstrate if we create a reliable and useful model for capturing the potential relationship between variables of interest . Introduction Econometrics is fundamental for economic measurements. However, it is important extends far beyond the discipline of economics. Econometrics is a set of research tools also employed in the business discipline of accounting, finance, marketing and managements. In Addition, econometrics plays a special role in the training of economist .As a Finance student we are assigned by Dr. Abdulnasser Hatemi Jarabad to learn on how to be an economist in applying econometrics model that we have acquired from the course. In this report we collect data from Dubai financial market in 2008 by selecting two variables to use; market price index and stock price of one of the listed banks in the market .First dependent variable (y) is market price index and the second independent variable (x) is the stock price of Commercial Bank of Dubai (CBD) .Using Regression analysis is to examine the potential relation of a dependent variable (y) to specified independent variable (x). To simplify things we define dependent variable as (y) and independent variable as (x) because this variable explains the variation in (y). These two have cause and effect relationship. ...read more.

Middle

Because this required to obtain unbiased estimated parameters. This can be tested by RESET test as suggested by Ramsey which is mentioned by the following steps: 1. 1. Estimate the regression model (yt=b1+b2 xt+et ) and obtain t , 2t , 3t and 4t . After that estimate the following regression : yt=b1+b2 xt+c1 2t + c23t + c3 4t +ut 1. 2. Test the following null hypoythisis of no-mispicification against the alternative hypothesis of misspecification by using the F-test : Ho: c1= c2 = c3 =0 H1: c1=0 , c2 =0 or c3 =0 No -Misspecification Misspecification E(et)=0 E(et) =0 1. 3. Then test via F-test using these results : propapility =0.0000 , r=3 restrictions , k=5 prameters and T= 262 observation : F=(SSER- SSEU )/3 SSEU /(262-5) Note that : SSER is the residual sum of squares in the equation (yt=b1+b2 xt+et ) = e2t SSEU is the residual sum of squares in the equation (yt=b1+b2 xt+c1 2t +c23t + c3 4t +ut) =u2t Since the probability of the F-test = 0.0000 is lower than the significant level (?=0.05).) then we reject null hypothesis at that significant level . Then we accept H1 of misspecification which indicate that the estimated parameter is not unbiased (biased) . So if the null hypnosis is rejected we need to find remedy to solve the problem . Ramsey RESET Test Equation: UNTITLED Specification: Y C X Omitted Variables: Squares of fitted values Value df Probability t-statistic 17.81245 259 0.0000 F-statistic 317.2834 (1, 259) 0.0000 Likelihood ratio 209.5401 1 0.0000 F-test summary: Sum of Sq. df Mean Squares Test SSR 72334997 1 72334997 Restricted SSR 1.31E+08 260 505317.0 Unrestricted SSR 59047420 259 227982.3 Unrestricted SSR 59047420 259 227982.3 LR test summary: Value df Restricted LogL -2091.173 260 Unrestricted LogL -1986.403 259 Unrestricted Test Equation: Dependent Variable: Y Method: Least Squares Date: 01/01/12 Time: 18:42 Sample: 1/01/2008 12/31/2008 Included observations: 262 Variable Coefficient Std. ...read more.

Conclusion

Error t-Statistic Prob. C 119004.3 77166.64 1.542173 0.1242 X 192991.8 37428.75 5.156246 0.0000 R-squared 0.092771 Mean dependent var 501459.6 Adjusted R-squared 0.089281 S.D. dependent var 361010.5 S.E. of regression 344518.0 Akaike info criterion 28.34528 Sum squared resid 3.09E+13 Schwarz criterion 28.37252 Log likelihood -3711.232 Hannan-Quinn criter. 28.35623 F-statistic 26.58688 Durbin-Watson stat 0.229846 Prob(F-statistic) 0.000000 Remedy : One of the way to make the model sufficient is to make use of one of the solution and these are : robust stander errors and weighted last squares in the estimations . When we used the E-views to repeat the steps we used weighted last squares to have new t-test and to make the model more efficient . And the probability of the t-test = 0.0988 which is higher than l the significant level (?=0.05).).. which means there is Homoscedasticity. Dependent Variable: Y Method: Least Squares Date: 01/01/12 Time: 18:58 Sample (adjusted): 1/02/2008 12/31/2008 Included observations: 261 after adjustments White heteroskedasticity-consistent standard errors & covariance Variable Coefficient Std. Error t-Statistic Prob. C -28.70704 17.32658 -1.656820 0.0988 X 904.6026 145.5312 6.215866 0.0000 X(-1) -896.7796 143.8650 -6.233478 0.0000 Y(-1) 1.000002 0.007342 136.2078 0.0000 R-squared 0.996173 Mean dependent var 4681.528 Adjusted R-squared 0.996129 S.D. dependent var 1405.186 S.E. of regression 87.43054 Akaike info criterion 11.79477 Sum squared resid 1964534. Schwarz criterion 11.84940 Log likelihood -1535.218 Hannan-Quinn criter. 11.81673 F-statistic 22301.20 Durbin-Watson stat 2.197746 Prob(F-statistic) 0.000000 Fourth :Test For Normality : This assumption is important because the distribution that are used to test different null hypothesis are based on this assumption . One testing we can use for this assumption is Jarque-Bera test . Therefore , the null hypothesis of normality against the alternative hypothesis of non normality is : Ho: S=(K-3)= 0 H1: S= 0 or (K-3) = 0 Normality Non-Normality The null hypothesis of normality is rejected if the probability of JB -test is lower than the significant level . the best solution for the problem is using logarithm if possible , remove outlines via dummy variables , allowing parameters instability or using alternative methods like boot simulation . ...read more.

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