Econometrics II - Introduction to the Course

u n i v e r s i t y o f c o p e n h a g e n d e p a r t m e n t o f e c o n o m i c s

Econometrics II

Introduction to the CourseMorten Nyboe Tabor

Econometrics II


Outline

1 Practical Information

2 The Structure of the Course

3 Learning Outcomes

4 What Can We Use Time Series Econometrics For? And, Why Do We NeedNew Econometric Tools?

Morten Nyboe Tabor — Econometrics II — Introduction to the Course — Slide 2


Outline



3 Learning Outcomes




About Me

Morten Nyboe Tabor.

• PhD in Economics, currently a Postdoc.• Work with time-series econometrics (in particular time-varying parameters

and structural breaks) and Imperfect Knowledge Economics (how to modelrational expectations under imperfect knowledge due to structural breaks).

• Office: CSS 26.2.01.• E-mail: [email protected].• Phone: (+45) 35 33 62 22.• After the lectures on Thursdays, I will stay in the lecture room for up to

an hour. Come to me if you need help or have questions.• Otherwise, use the discussion forum on Absalon for questions (other

students can typically learn from your question!).


mailto:[email protected]


The Teaching Assistants

Class 2 Katrine Bonde Monday, 10-12 CSS 7-0-28

Class 3 Birk Houmark Løfqvist Tuesday, 10-12 CSS 35-0-13

Class 4 Dan Nolsøe Olsen Thursday, 8-10 CSS 4-1-30

Class 6 Katrine Bonde Wednesday, 13-15 CSS 2-1-02

Class 8 Asbjørn Kehlet Berg Thursday, 13-15 CSS 4-1-30

Class 9 Birk Houmark Løfqvist Wednesday, 13-15 CSS 2-0-42

Class 10 Laurits Rømer Hjorth Thursday, 8-10 CSS 4-1-36

Classes start next week!



Prerequisites and Relation to Econometrics I

Econometrics II is part of the compulsory BA-sequenceProb. theory & statistics → EC-I (B) → EC-II (C)

Probability theory Cross-sectional data Estimation principlesIntroductory statistics Linear regression Time series dataMaximum likelihood Instrumental variables

Panel data

No repetition, we build directly on the results from Econometrics I.

Prerequisites (from Econometrics I or similar):• Knowledge of linear regression.

• Properties of OLS in random samples (cross-sectional data).

• Knowledge of instrumental variables (IV) estimation.

• Misspecification: Heteroskedasticity.

• Some matrix algebra.



Outline of Econometrics II

Estimation Principles and Estimation Theory:

1 Linear regression (OLS) for time series data.

2 Maximum Likelihood (ML) estimation.

3 Generalized Method of Moments (GMM) estimation.

Econometrics for Time Series Data:

1 Characteristic features of time series.

2 When can we apply the methods used for cross-sectional data to timeseries data?

3 New tools: Unit roots; cointegration; conditional heteroskedasticity.



Syllabus

Textbook: Marno Verbeek: A Guide to Modern Econometrics, 5th edition.

The book is delayed but will be available mid-September. Chapters 1-6 areavailable on Absalon.

Lecture notes by Heino Bohn Nielsen:

1 Introduction to Time Series.

2 Linear Regression with Time Series Data.

3 Introduction to Vector and Matrix Differentiation*.

4 Dynamic Models for Stationary Time Series.

5 Non-Stationary Time Series and Unit Root Testing.

6 Cointegration and Common Trends.

7 Modeling Volatility in Financial Time Series: An introduction to ARCH.

8 Generalized Method of Moments Estimation.

* Cursory readings.



Outline



3 Learning Outcomes




Five Pedagogical Principles

1 More student activities inside and outside the classroom.

2 More and better focused feedback.

3 Collaboration.

4 Increased student autonomy and shared responsibility for the learningprocess.

5 Extended use of blended learning.



A Combinations of Theory and Practice

Partly theoretical• Deal with the mathematical structure of the models and explore the

properties.• Necessary for understanding the tools – and for being critical towards

them!

Partly empirical• Feeling for real data. Hands-on experience.• Promote an interest for doing empirical analyses.• Different tools apply to different problems.• Introduce practical tools to perform analyses for, e.g., BSc projects or

MSc theses.



The Five Topics

We have structured the content into five major topics:1 The linear regression model for stationary time series.2 Dynamic models (ARMA models).3 Unit roots and cointegration.4 Time-varying volatility (ARCH and GARCH models).5 Generalized Method of Moments (GMM).

For each topic there is an assignment, which guides the teaching.

A set of learning activities take you through the steps you need to carry outthe assignment. These include online videos, review quizzes, and tutorials aswell as Problem Sets with theoretical and empirical exercises.



The Assignments

The five assignments are empirical case studies with open questions.

You must use the econometric theory, estimate the appropriate model(s),present your results in a clear way, and discuss the results and limitations ofyour approach.

You can work with the assignments individually or in groups of up to threestudents. We encourage you to work in groups, and in the exercise classes wecan help you find a group if necessary.

The assignments must be written in English! We encourage you to use Latex.We will discuss the assessment criteria and how to write a good assignment.

The deadline for the first assignment is Friday, September 22.



Peer Feedback on the Assignments

After handing in your assignments, you must provide peer feedback on threeother students’ assignments.

You give feedback on specific criteria focusing on how an assignment can beimproved.

We use the online platform Peergrade, which I will introduce later.• Double-blind feedback process.• You can flag potentially incorrect feedback and the teacher looks at it.• You must rate the quality of the feedback you receive.

You will learn a lot from giving feedback, receiving feedback, and from usingthe feedback to improve your assignments.

Peer feedback on the first assignment must be completed by the evening onWednesday, September 27.


Peergrade


What is Required to Qualify for the Exam?

On four of the five assignments you must:1 Hand in an assignment individually or in groups of up to three students.2 Individually provide peer feedback on three other students’ assignments.3 Rate the quality of the feedback you have received.

(That means you must provide peer feedback on a minimum of 12 assignmentsduring the semester.)



The Exam

Portfolio exam with four parts:1 The first three parts are three of the five assignments you have worked

with during the semester.That gives you the opportunity to use the feedback you receive to improveyour assignments.

2 The fourth part is a new set of theoretical questions.

Formalities:• You must hand in a written report individually or in groups.• You don’t have to do the exam with the group you worked with during the

semester, but if so be very careful with plagiarism!• The exam must be written in English.• The exam is given on Friday, December 15, and the deadline is at 10:00

(at ten in the morning) on Friday, December 22.

The re-exam is an oral exam with preparation. It covers the entire curriculumand is based on the cases in the five assignments, so the best preparation is towork with the assignments. It is in Danish or English.



Interactive Lectures

You must prepare for the lectures!• Readings, short videos, and review quizzes.• You can work with the content at your own pace, whenever and where

ever you want, and as many times as you want!• At the end of the review quizzes, you can add questions and comments on

what you find difficult. Combined with quiz results provides teachers withfeedback on what to focus on.

During lectures you will be activated: We ask you to think, discuss, and applyyour knowledge.

• Short lectures on key topics. Focus on difficult parts and interpretation.• Socrative quizzes to review content and correct common

misunderstandings.• Think-pair-share to apply, discuss, and evaluate key concepts and

interpretation of results.



Exercise Classes

We want you to work on solving exercises on your own!

There will be time for you to work on solving exercises or working onassignments. The teaching assistants can help you and you can use:

• Written solutions available for all theoretical exercises.• Videos show you how to use the OxMetrics software and how to interpret

the results.• Online tutorials take you through the steps of empirical exercises.

The teaching assistants present solutions to selected exercises.• We focus on those exercises you find difficult (tell the TAs!) and those we

find crucial.



The Absalon Site

All relevant course materials can be found on Absalon at

https://absalon.instructure.com/courses/21267


https://absalon.instructure.com/courses/21267


Continuous Evaluation to Improve Your Learning Experience

Primary aim: How can we improve your learning experience?• Which activities help you learn best?• How can we improve the activities, the structure, etc., for you to learn

more?

Secondary aim: Provide opportunities for self-assessment.• You are responsible for your own learning process!• How well do you master the learning outcomes for this module or topic?• Could you study smarter and better?

You will take part in the evaluation design (which questions?) and there will beopenness about results and changes made based on the evaluations.

You are always welcome to comment or provide constructive suggestions onhow to improve the teaching, and we are always happy to discuss how and whywe teach the course in a specific way!



Outline



3 Learning Outcomes




The Main Learning Outcomes

After completing the course, students should be able to...Identify the characteristic properties of a given data set,suggest and construct relevant statistical models,analyze to what extend a statistical model is congruent with the data,estimate and interpret the parameters of the model,formulate economic questions as hypotheses on the parameters of the model,and test these hypotheses.

After completing the course, students should be able to...Present a statistical model and empirical results in a clear and concise way.This includes using statistic and econometric terms in a correct way, givingstatistically sound and economically relevant interpretations of statisticalresults, and presenting results in a way so that they can be reproduced byothers.

These are the main learning outcomes you are assessed on in the exam!



Breakdown of Learning Outcomes


What are the properties of the data?





Which statistical model is relevant?





Is the statistical model a valid representation of the data?





How do we estimate and interpret the parameters of the model?





How do we formulate economic hypotheses as hypotheses we can test in theestimated model? And, how do we perform the statistical test of thehypothesis?




After completing the course, students should be able to...Present a statistical model and empirical results in a clear and concise way.This includes using statistic and econometric terms in a correct way, givingstatistically sound and economically relevant interpretations of statisticalresults, and presenting results in a way so that they can be reproduced byothers.

Requires that you spend energy on writing your assignmments!

We will discuss how to write a good assignment and what the exactassessment criteria are.



Outline



3 Learning Outcomes




What can we use time series econometrics for?

Think-Pair-Share1 Individually, think about the question for one minute without talking.2 In groups of 2-4 students, discuss for a few minutes and list at least three

reasons why we need econometrics.3 We all discuss and summarize.



“Danish private consumption is determined by thenumber of breeding cormorants (in Danish: skarv)”

Outline how you would empirically test the statement above graphically andusing a linear regression model based on time series data for the sample from1982 to 2001. Be as specific as possible.

Think-Pair-Share1 Individually, think about the question for one minute without talking.2 In groups of 2-4 students, discuss the question for a few minutes.3 We all discuss and summarize.



Illustration: Why Do We Need New Tools?

• We have yearly data for the sample 1982 − 2001.

CONS Danish private consumption in 1995 prices.BIRD Number of breeding cormorants (skarv) in Denmark.

We estimate a simple linear regression model,

log(CONSt) = β0 + β1 · log(BIRDt) + ut .

We would expect (or hope) to get β̂1 ≈ 0 and R2 ≈ 0.• Applying OLS to yearly data 1982 − 2001 gives the result:

log(CONSt) = 12.145(80.90)

+ 0.095(6.30)

· log(BIRDt) + ut ,

with R2 = 0.688.• What can we conclude?

It looks like a reasonable model.But it is complete nonsense: spurious regression.We need more econometrics to understand this (and to avoid it inpractice).

• See more examples of spurious correlations: http://tylervigen.com.


http://tylervigen.com


Illustration: Why Do We Need New Tools?

• We have yearly data for the sample 1982 − 2001.

CONS Danish private consumption in 1995 prices.BIRD Number of breeding cormorants (skarv) in Denmark.

We estimate a simple linear regression model,

log(CONSt) = β0 + β1 · log(BIRDt) + ut .

We would expect (or hope) to get β̂1 ≈ 0 and R2 ≈ 0.• Applying OLS to yearly data 1982 − 2001 gives the result:

log(CONSt) = 12.145(80.90)

+ 0.095(6.30)

· log(BIRDt) + ut ,

with R2 = 0.688.• What can we conclude? It looks like a reasonable model.

But it is complete nonsense: spurious regression.We need more econometrics to understand this (and to avoid it inpractice).

• See more examples of spurious correlations: http://tylervigen.com.


http://tylervigen.com


What’s Next?

What is a time series and why do we care if it is stationary?

Preparations for Thursday:

1 Read Lecture Note 1, “Introduction to Economic Time Series” (13 pages).2 Watch the video “An Introduction to Time Series and Stationarity” (10

minutes).3 Complete Review Quiz 1-01 (8 multiple choice questions).

Think about these two questions:1 What is the key difference between cross-sectional data and time-series

data?2 What is the definition of stationary and weakly dependent time series, and

what is the important implication of these assumptions?

See you on Thursday at 10:15!


Econometrics II - Introduction to the Course

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Transcript of Econometrics II - Introduction to the Course