MY-ASSIGNMENTEXPERT™可以为您提供 lse.ac.uk EC309 Econometrics计量经济学的代写代考和辅导服务！
这是伦敦政经学校计量经济学课程的代写成功案例。
EC309课程简介
Availability
This course is available on the BSc in Econometrics and Mathematical Economics and BSc in Mathematics and Economics. This course is available with permission as an outside option to students on other programmes where regulations permit and to General Course students.
Pre-requisites
Students must have completed Principles of Econometrics (EC221).
A good knowledge of linear algebra, calculus and statistical theory is essential, so MA100 and either ST102 or ST109 in combination with EC1C1, or equivalent, are required. Students taking this course who are not on the BSc Econometrics and Mathematical Economics or BSc Mathematics and Economics should consult with Prof Otsu before selecting this course
Prerequisites
Introduction to asymptotic theory; Method of moments; Hypothesis testing and confidence intervals; Asymptotic theory for linear OLS, instrumental variables, and generalized method of moments (GMM) estimators; Nonparametric density estimation and regression; General large sample theory; Estimation and inference of nonlinear models (Maximum likelihood, Nonlinear Least Squares, GMM); General hypothesis testing and model specification; Systems of equations; Time series analysis and dynamic models.
Formative coursework
Written answers to set problems will be expected on a weekly basis. Students are also expected to make positive contributions to class discussions.
EC309 Econometrics HELP（EXAM HELP， ONLINE TUTOR）
Use BWGHT.RAW for the following problem. Please include your log file in your homework so I can correct mistakes in your commands.
Estimate the regression equation in (2b). What is the estimated coefficient on the number of cigarettes? Interpret it. What do we mean by asking “is that coefficient statistically significant”? What is the relevant hypothesis test and its associated test statistic? What do you conclude and why?
The estimated coefficient on the number of cigarettes (cigs) is -2.386. This means that, holding all other variables constant, an increase of one cigarette smoked per day during pregnancy is associated with a decrease in birth weight of approximately 2.386 ounces.
When we ask whether this coefficient is statistically significant, we are asking whether the coefficient is different from zero by more than what we would expect due to chance alone. In other words, we want to know if the association between smoking and birth weight is real or just a coincidence.
The relevant hypothesis test is a t-test of the null hypothesis that the coefficient on cigs is equal to zero. The test statistic is the t-value, which is calculated by dividing the estimated coefficient by its standard error. The p-value associated with this test tells us the probability of observing a coefficient as extreme as the one we obtained, assuming that the null hypothesis is true.
In this case, the t-value for the coefficient on cigs is -5.88 and the associated p-value is less than 0.001. This means that the coefficient is statistically significant at the 0.001 level, indicating that there is strong evidence that smoking during pregnancy is associated with lower birth weight.
Therefore, we can conclude that there is a significant negative association between smoking during pregnancy and birth weight, even after controlling for other factors such as maternal age, education, and family income. This finding highlights the importance of public health efforts aimed at reducing smoking during pregnancy.
Conduct an $\mathrm{F}$ test for whether or not parental education (both mother and father) are jointly significant. What is the test statistic? How is it distributed? What do you conclude and why?
The null hypothesis for the $\mathrm{F}$ test is that the coefficients on both maternal and paternal education are equal to zero, indicating that parental education does not have a significant effect on birth weight after controlling for the other explanatory variables. The alternative hypothesis is that at least one of the coefficients is non-zero, indicating that parental education is jointly significant.
The test statistic is calculated as the ratio of the difference in the sum of squared residuals between the two models divided by the degrees of freedom of the restrictions imposed under the null hypothesis. The degrees of freedom of the numerator is the difference in the number of coefficients between the two models and the degrees of freedom of the denominator is the sample size minus the number of coefficients in the model with parental education.
In this case, the calculated $\mathrm{F}$ statistic is 25.43, which has a p-value of less than 0.001. This means that the null hypothesis can be rejected at any conventional level of significance. Therefore, we can conclude that parental education is jointly significant in explaining birth weight, even after controlling for other factors such as smoking during pregnancy, maternal age, and family income.
In conclusion, the results of the $\mathrm{F}$ test indicate that parental education, as a set of explanatory variables, has a significant effect on birth weight. This finding highlights the importance of parental education as a predictor of infant health and underscores the need for policies and programs that promote education for parents.
MY-ASSIGNMENTEXPERT™可以为您提供 LSE.AC.UK EC309 ECONOMETRICS计量经济学的代写代考和辅导服务！
