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## ECON 321 ECONOMETRICS 代写

Essayphd团队有econometrics代写phd，能提供全方位econometrics代写服务。各种高难度学术创作辅导，一应俱全。需要econometrics代写的小伙伴欢迎前来咨询我们。以下是我们为新西兰Auckland大学econometrics代写时候整理出来的课程介绍。

econometrics代写 Course Prescription

Development of the linear regression model, its basis, problems, applications and extensions. Attention is also given to techniques and problems of instrumental variables, simultaneous equations, panel data and time-series analysis.

Prerequisites: ECON 221 Introduction to Econometrics or STATS 207 or STATS 208 or STATS 210 and MATHS 108, 150, 153 This course leads on to the econometrics postgraduate courses ECON 721 (Econometrics I), ECON 723 (Econometrics II) and ECON 726 (Microeconometrics). It also complements ECON 322 Applied Econometrics. Students require ECON 301 (Advanced Microeconomics), ECON 311 (Advanced Macroeconomics) ECON 321 before embarking on postgraduate study in Economics. Students should also note that econometrics at this level requires a reasonable level of mathematical expertise.

econometrics代写 Goals of the Course

The aim of this course is to provide a good understanding of the properties of econometric models and techniques. Econometrics can be described as the science and art of building and using models in economics. More specifically it is concerned with the use of statistical methods to attach numerical values to the parameters of economic models and also with the use of these models for prediction. The techniques of econometrics consist of a blend of economic theory, mathematical modelling and statistical analysis.

econometrics代写 Learning Outcomes

By the end of this course it is expected the student will be able to:

• 1. derive properties of some important estimators such as least squares, maximum likelihood and instrumental variables in a number of specific modelling contexts of a sort which arise frequently in econometric work;
• 2. analyse certain classes of single and multiple equation models, including some time series models;
• 3. demonstrate an ability to explain the essential features of such models by reference to specified definitions and concepts, including notions of identification, specific classes of structural and reduced form estimators, and stationary and non-stationary time series.
• 4. access and manipulate data electronically using a combination of computer packages, including spreadsheets and statistical software;

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