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ECON 30130 Applied Econometrics 1 Vincent Hogan. Introduction & Outline Introduce me Lectures and Labs Course material Course outline and objectives.

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Presentation on theme: "ECON 30130 Applied Econometrics 1 Vincent Hogan. Introduction & Outline Introduce me Lectures and Labs Course material Course outline and objectives."— Presentation transcript:

1 ECON 30130 Applied Econometrics 1 Vincent Hogan

2 Introduction & Outline Introduce me Lectures and Labs Course material Course outline and objectives

3 Me Vincent Hogan vincent.hogan@ucd.ie  (716) 8300 Room D205, Newman Building Office Hours: Mon 10pm-noon or by appointment

4 Lectures and Labs Lectures: Tues & Thurs 10-12 Theatre Q. Labs – Several locations and times – Tues 17.00-1900 D114 & D115 HEA – Wed 17.00-19.00 G5 DAE – Thurs 18.00-20.00 G5 & G6 DAE Labs start in week 2

5 Role of Labs Labs are NOT tutorials in the traditional sense. The scheduled labs are merely times where the computer labs are reserved for your use exclusively AND where there will be some support for using the course software provided by Phd Students You are not obliged to attend labs but Econometrics is a practical subject so you need to practice either in the supervised labs or on your own

6 Course Software Econometrics is a practical subject that involves the analysis of real world data We will use software called stata which can be accessed via NAL on the ucd network – You cannot access it at home There is freeware at gretl.com which will do most of the analysis we need on this course – Neither I nor UCD will offer support for gretl

7 Using Stata Vital to get comfortable with basics in stata Manuals on the course website Lots of online help for stata Practice its use in the labs where the grad students can help you Stata will be needed for assignments Understanding the output will be key for class and the final exam

8 Course Website The course material will be available at www.vincenthogan.ie www.vincenthogan.ie Material will be posted in blog form – Course notes – Example data – Stata comand files – Software manuals – Sample exams The blackboard site for this course contains the lecture material from previous years. – Not of much relevance

9 Course Material You are strongly advised to bring a printed copy of these notes to the lecture to enable you to follow the material. – These notes are not designed to be sufficient on their own. The recommended text book is – Introductory Econometrics by Jeffrey Wooldridge Introductory Econometrics by Jeffrey Wooldridge – Should be in the campus bookshop – Second hand copies should be fine

10 Alternative Texts All of these texts should be fine and maybe available second hand – “Basic Econometrics”, Damodar Gujurati, McGraw-Hill – Introduction to Econometrics” by James Stock and Mark Watson, – Modern Econometrics: an introduction”, R L Thomas, Addison-Wesley, – “Introduction to Econometrics”, G.S Maddala, Prentice Hall, – “A Guide to Econometrics”, Peter Kennedy, Blackwell, – “Learning and Practising Econometrics”, Griffith Hill and Judge, John Wiley.

11 Assessment Assessment will be based on – 2 pieces of assessed work each worth 10% of the course grade – An end of year exam worth 80% The projects will involve applying the methods of class to real world data which I will provide. – due at end of week 8 and week 10 – In addition there will be an assignment every week for practice i.e. not for grade The final exam will be slightly more theoretical but will still have a large practical component

12 Introduction to Econometrics What is econometrics? Learning objectives Method of teaching.

13 What is Econometrics In a nutshell Econometrics is statistics applied to economic relationships – quantify economic relationships A simple example: – Keynesian Consumption function –  income today,  consumption today or  savings today – C=a+b*Y Another Example Return to Education: – What is  in income from holding a degree

14 Demand for fuel: – response of consumer demand to change in excise tax? – How much matters to the government

15 Key Issue: Managing Uncertainty Economic Theory defines a relationship between variables Agents require the size of the effects e.g. MPC, elasticity of demands. Key issue: – Whole population never observed only sample – Creates uncertainty Managing uncertainty is the key point of statistics

16 Steps in the Analysis 1.Economic Model: state theory or hypothesis e.g. Keynesian model 2.Specify a mathematical model: single equation or several. – e.g. C=a+b*Y – Note: 1 & 2 from your other courses 3.Specify statistical model: how deal with “errors” caused by sampling – This is what makes statistics 4.Get data: I provide for this course – But for your own project you will need to get data

17 Steps in the Analysis 5.Estimate the parameters of the model that best fit to the data. e.g. what “b” gives the best fit 6.Reject the Model? – Test hypotheses regarding the parameters. e.g. is “b” = 0.8? – Not trivial because of sample 7.Prediction: “What if” – implicit in everything

18 Learning Objectives 1.Understand how to perform linear regression analysis of economic data to derives estimates of parameters defined by economic theory 2.Understand how to perform hypothesis tests on the regression results in order to reject (or not) alternative economic theories 3.Use the results of the analyses to describe the effects of alternative economic policies and actions

19 Teaching This Course Practical approach Each section will be motivated by a case study – We will analyse real data in class – Address theoretical issues as they arise in each case study – Apply to some other cases – Further applications as homework You should repeat the data analysis in your own time and do the assignments for practice – Remember only 2 are for grade

20 Teaching This Course Failure to actually use data yourself will inhibit your learning Remember 40% of final exam is based on practical interpretation of stata output

21 Cases & Topics 1.Are women paid less than men? – Intro to statistics 2.What is the MPC? – Simple regression 3.How low will house prices fall? – Multivariate regression 4.Big is beautiful: Asymptotic theory 5.Mid way review & several examples

22 Cases & Topics 6.What is the return to education? – Omitted variables and errors in variables 7.What is the elasticity of demand for fuel? – Functional form 8.Review some of the cases for statistical problems of heteroscedasticity and autocorrelation


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