Foreign Trade University Faculty of Finance and Banking ********* ECONOMETRICS ASSIGNMENT Topic: “The impact of lover on study results of Foreign Trade University students” Contents I Introduction II Data description .3 1.Scope .3 2.Sources of data 3.Investigated factors and expectations .3 III Empirical Results .5 1.Building Regression Model .5 a.Model .5 b.Model .6 2.Assumption Tests .8 a.Multicollinearity b.Heteroskedasticity c.Autocorrelation .10 d.Normality 11 IV Conclusion .13 1.Interpretion 13 2.Suggestions 14 3.Limitations .14 4.Final words .15 Part I: Introduction Love is inherently a part of human life, particularly with the young Entering college marks the stage of adult From then on, you have the right to have a boy/girl friend and you also have more opportunities to expand your relationship than high school period Percentage of college students who are in love is great Besides issues such as part time jobs, school work, or social activities, we cannot deny that love is an important part of university student life However, the last question is whether university students should love or not Love has positive or negative impacts on academic performance Indeed, it depends on the way you love and people you choose Therefore, our group decided to choose the topic “The impact of lover on study results of Foreign Trade University students” We hope we can bring a more fully comprehensive view of students’ love, which suggests a reasonable and helpful advice for you to balance between love and learning Part II: Data description Scope: Data collected from students in Foreign Trade University who already have a lover by November 2012 Sources of data: We have conducted survey on totally 150 students, through both online-form and offline-form, to run the model Of all the 150 answer sheets, we have had 111 acceptable results The rest cannot be used because the respondents omitted some questions, or had some unrealistic answers Investigated factors and expectations: Dependent variable Variable Description GPA The GPA at the nearest semester of a student Independent variable – Quantitative variable Expec Variable Description - Note tation The gap between the Age Finance The “Age” variable can have age of the respondent +/- positive or negative impact and her/his lover on the study results The finance condition of The “Finance” variable can the respondent’s +/- boy/girl friend have positive or negative impact on the study results The “Time” variable can have negative impact on the The average number of Time study results If students hours per week the - respondent spends with spend more time on love, they have to cut back the his/her lover time spending on studying Qualitative variable Variable Description Denote Expec1 tation Note The “Gender” variable can be Gender Gender of the respondent Femal Male e +/- positive/ or negative impact on the study results Appearance The Averag above appearance of e Above +/- The “Appearance” variable can be the positive/ or respondent’ negative impact on boy/girl friend the study results is above average level The The “Appearance” appearance of Appearance below the respondent’s boy/girl friend variable can be Averag Below e +/- positive/ or negative impact on the study results is below average level The capacity The higher of study of the Capacity respondent’s Averag above boy/girl friend e capacity of boy/girl Above + level of motivation is above the for respondent average level The capacity The lower capacity of study of the Capacity respondent’s Averag below boy/girl friend e of boy/girl friend, Below - the lower level of motivation for is below the respondent average level Distance friend, the higher The geography The “Distance” distance variable can have between the respondent’s Close Far +/- positive or negative impact on the study results place and his/her lover’s Extra activities High concentrate Extra activities Taking part in extra implies part activities may have time jobs or no yes +/- positive or other social negative impact on activities the study result The higher Respondent concentrate no yes well on study + concentration on study, the higher the study result The worse Respondents Low concentrate concentrate no yes - concentration on badly on study study, the lower the study result Part III: Empirical Results Building regression model: a Model 1: Y =  +  1*Gender +  2*Age +  3*Appearance_above +  4*Appearance_below +  5*Capacity_above +  6*Capacity_below +  7*Distance +  8*Finance +  9*Dedicated_time +  10*Low_concentrate +  11*High_concentrate +  12*Extra_activities + ui By using Gretl following OLS method, we have the result below:  R-squared = 0,511498, which means all the independent variables explain  about 51,15% of the real outcome There are only variables which have statistical significance (p-values ≤ 0.1) These are: Age, Dedicated_time, Low_concentrate, High_concentrate, Extra_Activities The signs of the coefficients of these variable are followed our expectation Other variables not have statistical significance, so we will omit them from the model and run another regression model b Model 2: After omitting insignificant variables, we run the model with the other variables: Y =  +  1*Age +  2*Dedicated_time +  3*Low_concentrate +  4*High_concentrate +  5*Extra_activities + ui The R-squared now is 0,475943, which is smaller than the old R-squared Thus, we run the Ramsey RESET test to see if there is mispecification in our model or not As we can see, all the p-values are larger than specification is adequate α = 0,05 So we conclude that The signs of the variables follow our expectations: The variables of age, dedicated time and low concentration have negative signs, means that they have negative relationship with the increase of GPA On the other hand, the variables of high concentration and extra activities have positive signs, indicating that they have positive relationship with the increase of GPA Our SRF now is: Y = 7,76941 -0,0326185*Age – 0,0129144*Dedicated_time – 0,556837*Low_concentrate + 0,655956*High_concentrate + 0,3368*Extra_activities + ui Assumption Tests a Multicollinearity: At first, we use a correlation matrix to detect the presence of multicollinearity By using gretl, we have the following correlation matrix: We can see from the matrix that all the correlation coefficients between the variables have small absolute value Thus, we can not conclude that variables are strongly associated with each other But we also can not conclude that multicollinearity does not appear That is why we have to run another test to see if there is multicollinearity in the model According to this test, all the variables have Variance Inflation Factors (VIF) smaller than 10 Therefore, we can conclude that the model does not have the problem of multicollinearity b Heteroskedasticity: We try to see if there are any signs of heteroskedasticity by creating a scatter plot of the model: 10 There is no sign of heteroskedasticity, so we move on to run the White Test And here is the result from gretl: The p-value here is 0,505189 > α = 0,05; so we can come to the conclusion that there is no heteroskedasticity in this model 11 c Autocorrelation: Because we use a cross-sectional data, we cannot just run the autocorrelation test In this case, we change our data to time-series and have the result of the BG test as below: In this case, all the p-values are larger than α = 0,05; so we can come to the conclusion that the model does not face autocorrelation d Normality This part is to find out whether the error term ui in the model has normal distribution After running the Test statistic for Normality, here is our result: 12 The p-value in this case is 0,0153 < α = 0,05; so we conclude that the error term is not normally distributed To fix this, we add the variable of l_age to the current model: 13 We run the test again to see if the problem has been cured: 14 The p-value is 0,6013 > α = 0,05; so we can conclude that the error term is now normally distributed Part III: Conclusion Interpretation: After running regression and testing all the assumptions for multiple regressions, we have the final regression function with R-squared = 41, 3070% This is not a high value, but still can be accepted It implies that our model could explain about 41,3% of the outcome Our final regression model is: 15 Y = 7,85175 – 0,0154247*Dedicated_time – 0,481006*Low_concentrate + 0,517323*High_concentrate + 0,367198*Extra_activities – 0,162235*l_age + ui From our final regression model, we can conclude that among variables: Dedicated–time, low–concentrate, high-concentrate, extra-activities and l_age, there are variables having negative impacts on GPA and variables having positive ones Their influence mostly follow our first expectations   = -0, 0154247 < 0: means that one hour increased in time for love leads to 0,0154247 unit less in Y if other factors remain unchanged   = -0, 481006 < 0: means that low concentration leads to 0,481006 unit decreased in Y if other factors remain unchanged   3= 0,517323 >0: means that high concentration leads to 0,517323 unit more in Y if other factors remain unchanged  β4 = 0,367198 > 0: means that taking part in extra-activities leads to 0,367198 unit increase in GPA  β5 = –0,162235 < 0: means that a year increased in age gap leads to -0,162235 unit decrease in GPA Suggestions: Below are some suggestions for Foreign Trade University’s students that we conclude from our analysis result:  Gender, the capacity/appearance/finance condition of the lover and distance not have affect on study result Thus, we are free to love who we want redardless of these factors  As students, we should spend less time for love as studying is the most important thing at this time It seems like an opportunity cost if we dedicate too much to dating Love should be the motivation to archive higher marks, not a reason for going backward 16  Practicing self-concentration is the most important factor on study performance We should try to manage time effectively as well as identify our goals clearly; by doing this, not only studying but other issues will get better  Extra-activities are very essential, especially with students, it not only help us develop social skills but also have positive effects on studying result However, we should balance between time for studying and time for these activities Limitations: From our regression model, we can conclude that among 13 variables, there are some variables that follow our expectation but some not This indicates the gap between theory and reality, which can be unpredictable and impossible to fulfill without the help of subjects like Econometrics Besides, during the process of preparing this report, we have to face some problems The most challenged problem arising from the subject econometric itself Econometrics is a difficult subject which requires good nationality, diligence and time for research as well as analysis However, because we had to complete assignments of different subjects simultaneously and we received the announcement in hurry, we had to worked in a rush and did not have enough time to proofread this paper Furthermore, our knowledge of the subject still has limitations, which led us to choosing unsuitable variables In other words, the model which was run by us still had unavoidable mistakes Besides, our topic is about “Impacts of lovers on study results of FTU's students” – a sensitive one, so very few people can provide exact data leading to limited number of observations: we received only 150 surveys, and luckily 110 ones are accepted Moreover, many different groups are conducting surveys at this time, which makes students get bored of filling surveys Last but not least, it is also not easy to draw the right and meaningful conclusion from the result of the research, which is an inevitably important step in the process of research However during the time of doing this exercise we had chance to practicing team-working and understanding more about the econometrics and its application in life Final words 17 Our group, thanks to the instructions of Dr Tu Thuy Anh and lecturer Thai Long, has made great effort in collecting data and implementing the model Though the result did not turn out to be as well as we had expected, we have gained a lot experiences in building the regression model 18 ... or other social negative impact on activities the study result The higher Respondent concentrate no yes well on study + concentration on study, the higher the study result The worse Respondents... Description - Note tation The gap between the Age Finance The “Age” variable can have age of the respondent +/- positive or negative impact and her/his lover on the study results The finance condition... choose Therefore, our group decided to choose the topic The impact of lover on study results of Foreign Trade University students We hope we can bring a more fully comprehensive view of students