Introductory Econometrics
Multiple Regression: Inference
Farshid Vahid
2016
Recap
I
In the multiple regression model
y = X + u
if we add the assumption u | X is Normally distributed to the
Gauss-Markov assumptions, we obtain
b | X N(, 2 (X0 X)1 )
I
This i

Appendix G
TABLE (5.1 Cumulative Areas under the Standard Normal Distribution
2 0 1 ' 2 3 4 5 6
(continued)
743 744 Appendices
TABLE G.1 (Continued)
2 O 1
Examples: IfZ ~ Normal(0, 1), then P(Z S 1.32) = .0934 and P(Z S 1.84) = .9671.
Source: This tab

Introductory Econometrics
Probability and Statistics Refresher
Farshid Vahid
2016
Outline
I
Random variables (discrete, continuous) and their probability
distribution
I
Mean, variance, standard deviation
I
Properties of expectation
I
Properties of conditi

ETC1000/ETW1000/ETX9000 Business and Economic Statistics
LECTURE NOTES
Topic 2: Understanding What is Happening
1.
Relating Variables Together
In the diabetes example in Topic 1 we had a contingency table that suggested not
doing enough exercise was relat

Introductory Econometrics
Incorporating Qualitative Information in a Model
Farshid Vahid
2016
Recap
I
We have studied the multiple regression model and learnt:
1. to express it for a single observation, and, using matrix form,
for n observations
2. the OL

Topic 1: Introduction to Time Series Regression and
Forecasting
()
1 / 138
Table of Contents I
1 Introduction
1.1
1.2
1.3
1.4
Time series data
Properties of time series data
Notational conventions
Two examples of economic time series
Textbook reference: 1

Introductory Econometrics
Multiple Regression: Inference
Farshid Vahid
2016
Recap
I
In the multiple regression model
yi = 0 + 1 xi1 + . + k xik + ui , i = 1, 2, . . . , n
under the classical linear model (CLM) assumptions, we learned how
to:
I
I
I
I
test

Introductory Econometrics
Regression
Interpretation, functional form, scaling
Farshid Vahid
2016
Recap
I
The regression model relates the dependent variable to k
explanatory variables
y = 0 + 1 x1 + 2 x2 + + k xk + u
I
When we have a sample of n observati

Introductory Econometrics
Further Issues: Large Sample Properties of OLS
Farshid Vahid
2016
Recap
I
We have studied the multiple regression model and learnt that when:
1. model is linear in parameters: y = X + u
2. conditional mean of errors is zero: E (u

Introductory Econometrics
Multiple Regression: Inference
Farshid Vahid
2016
Recap
I
In the multiple regression model
y = X + u
if we add the assumption u | X is Normally distributed to the
Gauss-Markov assumptions, we obtain
b | X N(, 2 (X0 X)1 )
I
This i