ProblemSet1 Answers - Professor Mumford mumford@purdue.edu...

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon
Professor Mumford Econ 360 - Fall 2010 mumford@purdue.edu Problem Set 1 Answers True/False (20 points) 1. TRUE If { a 1 ,a 2 ,...,a n } are constants and { X 1 ,X 2 ,...,X n } are random variables then: E n X i =1 a i X i ! = n X i =1 a i E ( X i ) 2. FALSE For a random variable X , let μ = E ( X ). The variance of X can be expressed as: V ar ( X ) = E ( X 2 ) - μ 2 3. TRUE An estimator, W , of θ is an unbiased estimator if E ( W ) = θ for all possible values of θ . 4. FALSE The central limit theorem states that the average from a random sample for any population (with finite variance) when it is standardized, by subtracting the mean and then dividing by the standard deviation, has an asymptotic standard normal distribution. 5. FALSE Inferring causality is sometimes possible without a designed experiment. Using observational data to infer causality is what econometrics is all about. 1
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Multiple Choice Questions (20 points) 6. The idea of holding “all else equal” is known as (a) ceteris paribus (b) correlation (c) causal effect (d) independence 7. If our dataset has one observation for every state for the year 2000, then our dataset is (a) cross-sectional data (b) pooled cross-sectional data
Background image of page 2
Image of page 3
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 02/06/2012 for the course ECON 360 taught by Professor Na during the Spring '10 term at Purdue University-West Lafayette.

Page1 / 4

ProblemSet1 Answers - Professor Mumford mumford@purdue.edu...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online