Hi All,
Since I didnt follow the book sequentially for the regression chapters, I wanted to clarify
which topics are included and what topics are not. Hope it helps, please let me know if
you have more questions.
Chapter 3: Simple Linear Regression
We cov
Ch. 2 Mathematical and statistical foundations
The ultimate objective of econometrics is usually to build a model, which maybe thought of as a
simplified version of the true relationship between two or more variables that can be described as
a function. A
Ch. 3 Book Notes  be able to use the hypothesis tests (test of significance and confidence interval)
3.1 What is a regression model?
Regression in concerned with describing and evaluating the relationship between a given variable
and one or more other v
Ch. 1
What is econometrics?
Measurement in economics
Financial econometrics = The application of statistical techniques to problems in finance
Useful for testing theories in finance, determining asset prices or returns, testing hypotheses
concerning th
Ch. 4 Book Notes (be able to perform a ftest)
4.1 Generalizing the simple model to multiple linear regression
Yt = a + Bxt + ut; where there are variables for a set of k1 explanatory variables which are thought
to influence y and the coefficient estima
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How does it benefit the company
and the Demand Planning &
Business Forecasting
Economics 309
Project Proposal
Due by Midnight 2/8/2013
The project proposal is important since it will serve as a guide to future forecast work in this
class. Beginning with a good proposal is required to do well in the course. This is worth five
points
Economics 533
Applied Economic and Financial Forecasting
Spring 2014
Project due by midnight April 28
Professor:
Email:
Phone:
Fax:
Stanley Holmes, Ph.D.
[email protected]
(903) 4686029 or (903) 3657190
(903) 8865601
Semester Project
For
Multiplier (LM) test.
Also uses the OLS residuals e,. The first step
of the test is a linear regression with
dependent variable e, and independent
variables Xt2,.,xtx e t  ~
.
Compute the ' of
R
this regrission. The test statistic is
9
.
I
Note that we u


~ e ~ t a r * ~ i n ~ h a p t e r ~ i . ~ i n i b e n ~ ~ r
~dtkcflfgtordethtd~aswcIrjbulh~rpe
To p o h this tab note that hpaion (95) esrn k rarsvritm as
'.
K=81+&Xd+*
.
 +B&+p*L+e,h
~hctatfmp=Oe~nP~rebcrr~a~s~muttipUer
the additionof the wuk&It 1
ASSUMPTIONS O F THE MWZTIFLE WEGRBSSION MODEL
MR1. y t = P1 +&XB
h/IR2. E(yr) = + b
+
* = *
t 2
+~
I
+
K X ~ K
er3
? =1,
*.,
,T
+ ' ' + P c r ~ E(et) = 0.
MR3. var(y;)=var(et)=cr 2
MR4. cuv(yt, y,) = cov(et, e,) = 0
and are not exact: +.
d.
. linear func
l 80 CHAPTER 5 Time Series and Their Components
Bell and Hillmer concluded that seasonal adjustment is done to simplify data so that
they may be more easily interpreted by statistically unsophisticated users without a
nificant loss of information (p. 301
Hi All,
I have uploaded the data ser PR611 Dataset for Regression Analysis under DocSharing.
Please follow the steps for this data set and when you are done, do it again for your project
data. This is an example of Simple Regression where you have only o
Applied Business Forecasting and
Planning
MOVING AVERAGES AND
EXPONENTIAL SMOOTHING
Introduction
This chapter introduces models applicable to time series
data with seasonal, trend, or both seasonal and trend
component and stationary data.
Forecasting meth