How to use EViews to implement the econometric techniques we have discussed in the module.

Assignment Question

Module of this assignment is Quantitative Techniques. This is an important problem set I am working on. The final product has to be of an academic level (final academic year, so it is very important that the answers are accurate and correct), and it has to be a single PDF document (a word document as well to support it would be helpful, but not mandatory). Calculus and answers must have step by step processes, to understand how we came to that conclusion. Also, where needed, citations must be included at the end of the document. Moreover, pages must have numbers and the font has to be Times New Roman 11 (except for calculus, formulas, and maths related characters). To be able to do this assignment, it is fundamental that you look at the attached Excel file. If you need the Gretl file to work on the assignment (which should not be necessary since you have all the data on the Excel file) please let me know and I will send it either via email or as you prefer. All the data will be uploaded, but if you feel something is missing, please reach out to me. To help you answer the questions, I will point out all the topics that have been covered during the lectures, and that could be helpful to understand where to gather some more information from: – The simple linear regression model and the principle of ordinary least squares (OLS) estimation. – The basic ideas and assumptions of the multiple linear regression model; the OLS approach to estimating the parameters of the k-variable linear regression model; some basic facts about random vectors, mean vectors and covariance matrices. – The statistical properties of the OLS estimator in the classical multiple linear regression model (unbiasedness, consistency, efficiency); the ideas behind various goodness-of-fit measures; the problem of multi collinearity. – The basic principles of hypothesis testing and how to carry out statistical tests in the classical linear regression model; the implications of specification errors. – Heterosexuality, its implications, and inference based on generalized/weighted least squares. – Auto correlation, its implications, and inference based on generalized least squares. – The basic principles of maximum likelihood estimation. – Auto regressive distributed lag models; how to use EViews for regression analysis. – How to use EViews to implement the econometric techniques we have discussed in the module.