A Practical Approach:
1.
2.
3.
4.
5.
6.
Plotting the data and choosing economically-plausible models
Testing hypotheses concerning the parameters
Performing residual analysis
Assessing forecasting performance
Measuring goodness-of fit (R2)
Using the princ
San Jos State University
ENVS/ECON 107, Environmental Economics and Policy
Fall 2016
Instructor:
Duran Fiack
Office Location:
WSQ 111B
Email:
duran.fiack@sjsu.edu
Office Hours:
Tues. & Thurs. 1:00-2:00 pm, Wed. 3:30-4:30 pm or by appointment
Class Days/Ti
San Jos State University, Department of Economics
Economics 108, Cost-Benefit Analysis, Fall 2016
Course and Contact Information
Instructor:
Mike Jerbic
Office Location:
140 Clark Hall
Telephone:
(408) 373-7416
Email:
Stephen.jerbic@sjsu.edu
Office Hours:
Estimating the variance of the Error term:
The variance of the random error ei is:
var(ei ) 2 Eei E(ei ) E (ei 0)2 E(ei )
2
2
Assuming that the mean error = 0 assumption is correct.
The unbiased estimator of variance is:
2
e
2
i
N 2
with
E( 2 ) 2
Interv
5. Measuring goodness-of-fit: With different dependent variables:
We cant compare R2 as each has
different dependent variable.
Goodness of fit with different dependent variables:
The R2 from a linear model, measures how well the linear model explains the
Infinite Distributed Lag:
The main problem with this type of model is that the number of lags q must be chosen
empirically using selection criteria such as AIC and SBC.
This is viewed as too data driven you might fit the data perfectly but youre actually
Please note that LIFT does not warrant the correctness of the materials contained within the notes. Additionally, in some cases, these
notes were created for previous semesters and years. Courses are subject to change over time, both in content and scope
FAMINC = the combined income of husband and wife
Omitted variables:
It is possible that a chosen model may have important variables omitted. Our economic principles may have
overlooked a variable, or lack of data may lead us to drop a variable even when i
The Chi-Square Distribution:
The Chi-square random variables arise when standard normal random variables are squared. If Z1,
Z2, ., Zm denote m independent N(0,1) random variables, then
V Z Z Z
2
1
2
2
2
m
~
2
( m)
m
Z
i 1
2
i
~ (2m)
2
The notation V ~ X
Spurious Regressions:
The main reason why it is important to know whether a time series is stationary or non-stationary before
one embarks on a regression analysis is that there is a danger of obtaining apparently significant
regression results from unrel
Please note that LIFT does not warrant the correctness of the materials contained within the notes. Additionally, in some cases, these
notes were created for previous semesters and years. Courses are subject to change over time, both in content and scope