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ECON 526 Exam 1 Study Guide

by: Mike Meng

ECON 526 Exam 1 Study Guide Econ 526

Marketplace > Kansas > Economcs > Econ 526 > ECON 526 Exam 1 Study Guide
Mike Meng
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The first exam is on next Thursday. Here is the study guide ready for you! Make sure you go over it and you will do very well!
Intro to Econometrics
Zach Raff
Study Guide
ECON 526
50 ?




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This 2 page Study Guide was uploaded by Mike Meng on Sunday February 14, 2016. The Study Guide belongs to Econ 526 at Kansas taught by Zach Raff in Winter 2016. Since its upload, it has received 339 views. For similar materials see Intro to Econometrics in Economcs at Kansas.


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Date Created: 02/14/16
University of Kansas ECON 526: Introduction to Econometrics Exam #1 Study Guide Exam Info: Next Thursday, February 25, during class. The exam will cover S & W Chapters 1- 5, and any other information covered during lecture. The items listed below are for those specific items themselves. In addition, know how to explain the mathematical concepts in words; because this is an economics and not a math course, I want you to be able to tell me what is going on from the math! The following is a list of broad topics. Make sure you know how to do problems similar to the examples covered in lecture and in the homework for each topic (e.g., calculating probabilities and estimates given summary statistics or data). Finally, as I mentioned in class, you are allowed one page (front and back) of notes; this page must be written (not computer printed). You may include anything you like on this page (e.g., formulas, example problems, etc.), and you will turn this page in with your exam. Items to Know for the Exam: Ch. 1 – Economic Questions and Data 1. What kinds of questions do we care about in economics? 2. Quantitative v. qualitative data (and how to transform qualitative data into quantitative) 3. Experimental v. observational data 4. Types of data (i.e., cross-sectional v. time-series v. panel) Ch. 2 – Review of Probability 1. Terms of probability (outcomes, sample space, etc.) 2. Discrete v. continuous random variables 3. Probability distribution function v. cumulative distribution function 4. Expected values, means, standard deviations, variance 5. Joint, marginal, conditional distributions 6. Covariance, correlation 7. Properties of the normal distribution and how to standardize variables  also how to read a standard normal table 8. Random sampling 8.1. I.I.D 8.2 Various estimators (sample average, variance) 9. Central limit theorem, law of large numbers Ch. 3 – Review of Statistics 1. Properties of estimators (e.g., unbiased, consistent) 1 2. Hypothesis tests for the population mean (H v H0)  waat do these mean, and when can we reject/fail to reject H 0 3. Sample variance, standard deviation, and SE (for mean) 4. p-value (what it is and how to calculate) 5. t-statistics (what it is and how to calculate) 6. 1-sided v. 2-sided hypothesis tests 7. Critical values for a number of significance levels (e.g., 90, 95, 99 % levels) 8. Constructing and interpreting confidence intervals 9. Hypothesis tests and confidence intervals for differences of means of 2 populations 10. Reading scatterplots 11. Sample covariance and correlation Ch. 4 – Linear Regression with One Regressor 1. Know the terminology (e.g., which are dependent/independent variables, etc.) 2. Interpreting coefficients/being able to know what we are doing with regression 3. Calculate and interpret coefficients, predicted values, and errors 4. Various measures of the regr2ssion (ESS, TSS, SSR) 5. Various measures of fit (R , SER) 6. Least squares assumptions  why do we need them? what happens if they fail? 7. Sampling distribution of OLS Ch. 5 – Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals 1. 1-sided and 2-sided hypothesis tests for regression coefficients (perform and know what the results mean!) 2. Confidence intervals for regression coefficients 3. Regression for binary independent variables (interpret them) 4. Heteroskedasticity, homoskedasticity  what are they, and what do they mean? 5. BLUE estimators Stata 1. Commands for basic estimators, regressions, plots (e.g., reg y x, corr y x, etc.) 2. Know all parts of the regression output that we discussed (e.g., R , TSS, ESS, etc.)  how to calculate some parts given others 2


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