Sample undergrad thesis proposal

For your reference and guide, please download here a model undergraduate thesis proposal written by Garcia et al. (2008).

Econ 21 Syllabus 2010

Economics 21 students may download the syllabus here.

Econ 22 Syllabus 2010

Economics 22 students may download the syllabus here.

Econ 1N Syllabus 2010

Economics 1N students may download the syllabus here.

Econ 299 Orientation

Econ 299 students may download the orientation notes here.

Econ 1N market experiment info for 330 class

Econ 1N students will need to make use of the summary of the distribution of seller and buyer types for the market experiment (reported below) to complete their experiment report which is due on Dec. 9 for MWF classes and Dec. 10 for TTH class.

    MWF 330-430 (PE 11): Session 1

Type Number Cost Value
Low Cost Seller 10 Php 10 N/A
High Cost Seller 4 Php 30 N/A
High Value Buyer 5 N/A Php 40
Low Value Buyer 11 N/A Php 20

    MWF 330-430 (PE 11): Session 2

Type Number Cost Value
Low Cost Seller 6 Php 10 N/A
High Cost Seller 11 Php 30 N/A
High Value Buyer 11 N/A Php 40
Low Value Buyer 5 N/A Php 20

Econ 232: Monte Carlo Exercise 1

Guide Questions:

1. Does the simulation exercise support the algebraic results on the expected values of muhat and mutilde?
2. Which of the two is more asymptotically efficient?

Below is the sample R script for the first part of the Monte Carlo exercise to investigate the properties of two competing estimators of the population mean:

# Monte Carlo Exercise 1
# The purpose of this exercise is to learn 
# the properties of the sampling distribution
# of muhat (arithmetic mean) and mutilde (min plus max divided by two) 
# when the parent distribution is Normal

rm()
nobs = 30
nsim = 100

# muhat when R.V. is from Normal

mu.bar.vals = rep(0,nsim)
set.seed(223)
for (i in 1:nsim) {
	z = rnorm(nobs)
	mu.bar.vals[i] = mean(z)
}

mubar.mean = mean(mu.bar.vals)
mubar.mean
x.vals1 = seq(from=min(mu.bar.vals), to=max(mu.bar.vals), length=300)
y.vals1 = dnorm(x.vals1, mean=0, sd = 1/sqrt(nobs))
hist(mu.bar.vals, probability=T, main="Distribution of Mubar and Normal Approximation", sub="N(0,1), T=30")
points(x.vals1, y.vals1, type="l")

# mutilde when R.V. is from Normal

mu.tilde.vals = rep(0,nsim)
set.seed(223)
for (i in 1:nsim) {
	w = rnorm(nobs)
	mu.tilde.vals[i] = (min(w)+max(w))/2
}

mutilde.mean = mean(mu.tilde.vals)
mutilde.mean
x.vals2 = seq(from=min(mu.tilde.vals), to=max(mu.tilde.vals), length=300)
y.vals2 = dnorm(x.vals2, mean=0, sd = sqrt(1/2))
hist(mu.tilde.vals, probability=T, main="Distribution of Mutilde and Normal Approximation", sub="N(0,1), T=30")
points(x.vals2, y.vals2, type="l")