Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
LEARNING OUTCOMES
The main objective of the course is to obtain a basic understanding of the econometric methodology. The aim is to motivate the students to examine causal relationships between economic phenomena by using a linear regression model. The course focuses on least squares estimation of the model and related statistical inferences. The assumptions of least squares estimation will be critically investigated. We examine the violations of these assumptions and the possible ways to alleviate the assumptions. The emphasis of the course is in the empirical application of the least squares method and its extensions. The economic interpretation of the estimated parameters of regression model and their statistical significance is given a special focus. After the course, students should have the skills to conduct basic empirical econometric analysis.
Credits: 6
Schedule: 11.01.2021 - 26.02.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Timo Kuosmanen
Teacher in charge (applies in this implementation): Timo Kuosmanen
Contact information for the course (valid 08.12.2020-21.12.2112):
Professor Timo Kuosmanen
E-mail: timo.kuosmanen@aalto.fi
Office hours: by appointment
Teaching assistant: Sheng Dai
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
Valid 01.08.2020-31.07.2022:
Econometrics is a branch of economics that aims to give empirical content to economic theory by applying statistical methods to real world data. This course focuses on the application of linear regression to economic data, its assumptions, and statistical significance tests of parameters and linear restrictions. We also extend the basic linear regression for modeling endogeneity, heteroskedasticity and autocorrelation. Time series and panel data models are considered towards the end of the course. All topics are examined by means of economic examples with actual empirical data.
Applies in this implementation:
Due to the Covid-19 pandemic, this year the course is organized fully in
online format. The lectures are organized as video lectures, which are
available through the course website in MyCourses.The video lessons will be added to the course website as they are
recorded so that students can view them in their own pace.To facilitate interaction, students are encouraged to submit a question
to the professor as a part of the weekly assignments. Valid questions will
contribute to the grade through the assignments. The answers to the most
interesting questions (anonymized) will be posted to the course website on
weekly basis.Weekly homework assignments
include both theoretical and empirical problems, and a question for the
professor. The problems are mainly based on the lectures, but it may be useful
to consult the textbooks indicated below and/or other (online) resources. Students
may collaborate to solve homework assignments, but everyone needs to submit
independently their own solutions for grading. The deadline for submitting the
solutions for grading is 15:00 every Tuesday (before the start of the first
exercise session). Solutions submitted after the deadline will not be graded. Detailed
instructions for how to submit the solutions to the course assistant will be
provided in the problem sets (to be published on the course website).During the live exercise
sessions organized through Zoom (Tue, Wed), the teaching assistant Sheng Dai will
present the example solutions to assignments, discuss possible alternative ways
of approaching the problem, and provide tips to solving the next problem sets.Before the final exam,
there will be an extra problem set that can be submitted for grading. Points
earned from the extra problem set can be used to compensate any missing points
from weekly homework assignments.To solve the empirical problems, students are free to
use any software preferred. See Lesson 1d) for the discussion of the main
alternatives and their relative advantages and disadvantages.
Assessment Methods and Criteria
Valid 01.08.2020-31.07.2022:
70 % exam
30 % assignmentsApplies in this implementation:
The exam and the homework assignments will be based on the lectures and
the course textbook.The online exam will be organized through the course website The exam
includes both theoretical and empirical questions. To eliminate the possibility
of cheating, in the empirical questions each student will be analyzing unique
randomly generated data that are personalized using the student number.
Workload
Valid 01.08.2020-31.07.2022:
Lectures 36 h
Exercise sessions 10 h
Self-study and other independent work 74 h
Exam preparation 37 h
Exam 3 h
Total 160 h (6 ECTS)
DETAILS
Study Material
Valid 01.08.2020-31.07.2022:
Lecture notes and additional material are provided on the course website.
The following textbooks may be used as supplementary study material:
Wooldridge, J.M. (2015) Introductory econometrics: A modern approach. (or any newer edition)
Dougherty, Christopher (2016) Introduction to econometrics. (or any newer edition)
Prerequisites
Valid 01.08.2020-31.07.2022:
Preferably "Statistics 2 with R" or equivalent, at minimum "Tilastotieteen perusteet" or an equivalent introductory statistics course.
SDG: Sustainable Development Goals
7 Affordable and Clean Energy
8 Decent Work and Economic Growth
12 Responsible Production and Consumption
FURTHER INFORMATION
Details on the schedule
Applies in this implementation:
Video lectures are pre-recorded, and can be watched in your own pace. There are two weekly exercise sessions organized live in Zoom.
- Lärare: Dai Sheng
- Lärare: Kuosmanen Timo