### Materials

Readings: Brooks Ch. 2,-3 (2nd ed.). Revies of Linear Regression Analysis. Book slides available on publisher's page

http://www.cambridge.org/features/economics/brooks/PPT.html

Review of the classical multiple linear regression model (with several independent variables).

Readings: Chapter 3 in Brooks, 2nd ed. See also book slides:

http://www.cambridge.org/features/economics/brooks/downloads/PPT/Ch3_slides.ppt

Regression analysis,:some tests and issues related to time series. Readings: Brooks Ch. 4, see also book's slides

http://www.cambridge.org/features/economics/brooks/downloads/PPT/Ch4_slides.ppt

Regression analysis,:some tests and issues related to time series. Readings: Brooks Ch. 4, see also book's slides.

http://www.cambridge.org/features/economics/brooks/downloads/PPT/Ch4_slides.ppt

Autoregressive processes AR(p). Stationariry condition and alculation of autocovariance and autocorrelation.

**VERY IMPORTANT!**From Brooks, 2nd ed. Study Chapter 5.4.Autoregressive processes p. 215-222,

and the corresponding slides 14-25 from http://www.cambridge.org/features/economics/brooks/downloads/PPT/Ch5_slides.ppt

Watch the following short videos (some of the first ones are revies of what you have already learned):

Time Series Analysis - 2.1.1 - Introduction

Length 1:38

Bob Trenwith

Time Series Analysis - 2.1.2 - Time plots

Length 8:30

Time Series Analysis - 2.1.3 - First Intuitions on Weak Stationarity

Length 2:07

# Time Series Analysis - 2.1.4 Autocovariance function

Length 9:27

Time Series Analysis - 2.1.5 - Autocovariance coefficients

Length 6:05

Time Series Analysis - 2.1.6 - Autocorrelation Function ACF

Length 5:10

Time Series Analysis - 2.2.1 - Random Walk

9:54

Time Series Analysis - 2.2.2 - Introduction to Moving Average Processes

3:00

Time Series Analysis - 2.2.3 - Simulating MA2 process

6:37

Time Series Analysis - 3.1.1 - Stationarity - Intuition and Definition

13:04

Time Series Analysis - 3.1.2 - Stationarity - 1st Examples - White Noise and Random Walks

9:36

Time Series Analysis - 3.1.3 Stationarity - First Examples - ACF of Moving Average

10_04 Quite theoretical

Time Series Analysis - 3.2.1 - Series and Series Representation

8:49 We need just to review the geometric series from this

Time Series Analysis - 3.2.2 - Backward shift operator

5:37 Backward shift operator B is called Lag operator L in our textbook

Time Series Analysis - 3.3.1 - Autoregressive Processes - Definition, Simulation, 1st Examples

9:11

Time Series Analysis - 3.3.2 - Autoregressive Processes - Backshift Operator and the ACF

10:55

More notes on autocorrelation and stationarity, studied already last week. The best way in my opinion is to use the the general rules for variance and covariance, which are added in a separate appendix.

Study the slides on Yule-Walker-equations where these rules are also used. Yule-Walker equations can be used to calculate the autocorrelation and partial autocorrelation functions of AR-processes.

There are also slides that visually demonstrate the idea of partial autocorrelation.

Study also Chapter 5.5. on partial autocorrelation and invertibility condition in Brook's book (p. 222-224).

Video Lectures:

Time Series Analysis - 3.2.3 - Introduction to Invertibility # T

ime Series Analysis - 4.1.1 - Partial Autocorrelation and the PACF - First Examples Time Series Analysis - 4.1.2 - Partial Autocorrelation and the PACF - Concept Development Time Series Analysis - 4.2.1 - Yule Walker Equations in Matrix Form Time Series Analysis - 4.2.2 - Yule Walker Estimation - AR2 Simulation Time Series Analysis - 4.2.3 - Yule Walker Estimation - AR3 Simulation Box-Jenkins modelling. Autocorrelograms for AR, MA, and ARMA-models; ARIMA-models; Information criteria, forecasting with ARMA models.

Read Brooks Chapter 5.6-5.8 and 5.11-5.12

For slides see http://www.cambridge.org/features/economics/brooks/downloads/PPT/Ch5_slides.ppt

Videos:

Time Series Analysis - 5.1.1 - Akaike Information Criterion and Model Quality Time Series Analysis - 5.2.1 - ARMA Models And a Little Theory Time Series Analysis - 5.2.2 - ARMA Properties and Examples Time Series Analysis - 5.3.1 - ARIMA Processes

VSpurious regression, Random Walk models and unit roots; forms of non-stationarity, order of integration

Read Brooks Ch. 7.1.1-7.1.3 (p. 318-327) and corresponding book slides

https://www.cambridge.org/features/economics/brooks/downloads/PPT/Ch7_slides.ppt

Watch the corresponding video by Brooks

: Introductory Econometrics for Finance Lecture 19 Tests for unit root.

Read Brooks Ch. 7.1.4 (p. 327-331) and corresponding book slides

https://www.cambridge.org/features/economics/brooks/downloads/PPT/Ch7_slides.ppt

Watch the vido by Brooks: Introductory Econometrics for Finance Lecture 20