Econometrics II (Time Series Analysis)
Introduction
Syllabus
Computer Setup
Lectures
Introduction
1. Time series vs cross-section
2. Time Series concepts and models
Multivariate normal distribution
1. Multivariate normal distribution
Review of maximum likelihood estimation
1. Maximum likelihood estimation
2. Maximum likelihood estimation of Gaussian models
ARMA models
1. Introduction to AR and MA models
2. Algebra of AR and MA models
3. ARMA models
3. Forecasting with ARMA models
Non-stationarity
1. Non-stationarity and unit root processes
Conditional Heteroskedasticity
1. ARCH processes
Multivariate models
1. VARs
2. State space models
3. Applications of state space models
Other topics
1. Regime-switching
2. Granger Causality
3. Bayesian approach
Assignments
Homework 0
README HW0
Homework 1
README HW1
HW1-empty
Homework 2
README HW2
HW 2 Part 1
HW 2 Part 2
HW 2 Part 3
Homework 3
Readme HW3
HW 3 Part 2-2
Homework 4
README HW4
Part 1
Part 2
Homework 5
README HW5
HW 5 part 1
Homework 6
Homework 7
Homework 8
HW8 Part 2
Homework 9
Homework 10
Projects
Midterm project
Final project
Tutorials
1. Python
Intro
Functions
Modules
Paths
2. Numpy
Numpy exampes I
Numpy exercises I
Multivariate Normal in Numpy
3. Pandas
Intro
Pandas for time series data
4. Matplotlib
Other tools
Command line
Git and GitHub
Conda
Data
1. APIs
Download data from ECB’s SDW
Download data from EUROSTAT
Download data from FRED
.md
.pdf
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Homework 0
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README HW0
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Bayesian approach
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