Econometrics Week 1 Class Notes (January 11-January 15)
Econometrics Week 1 Class Notes (January 11-January 15) ECON 40313
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This 3 page Class Notes was uploaded by Kaley Hicks on Monday January 18, 2016. The Class Notes belongs to ECON 40313 at Texas Christian University taught by Dr. Nikaj in Spring 2016. Since its upload, it has received 69 views. For similar materials see Econometrics in Economcs at Texas Christian University.
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Date Created: 01/18/16
January 12, 2016 Class Notes: Econometrics: Use of statistical methods and data to test economic relationships Law of Demand: Price Increases, Quantity Demanded decreases Ex: What is the effect of P gas on Qd? o Qd: Function (Pg) Factors that are determinants of Quantity Demanded: Function (Pg) o Fuel Efficiency o Income o Commuting Time o Walking/Public Transit Example: Potato Famine in Ireland: Price Increased & Quantity Demanded Increased o This is a GIFFEN GOOD o Why does this happen? People are worried the price will rise even more Expectations of the future are determining demand Expectations of a price increase will lead you to demand more now. Causes this relationship of Price increasing and Quantity demanded increasing. What caused the Great Recession? o Banks: [idea of free money and loose lending] o Secuitization: [securities became toxic] o Mortgage Standardization o Borrowers o Adjustable o Low Interest Rates January 14, 2016 Class Notes: Goals of Econometrics o Test Hypotheses/theories o Estimate relationships between economic variables o Forecasting time series econometrics: predictive o Evaluate government policy CBO/UI/MATH ACA -Econ Model Law of Demand: Price Increases, Quantity Demanded Decreases Determinants: Income, price of substitutes and complements, number of buyers, preferences, expectations Gasoline market Econometric Model: Qd~ gallons/barrels Pg~ ($) Yearly Household Income ~ ($), Public transport, price of electrics, ubers (average price of uber ride in your town) o Dummy (binary) variable): example is public transport : yes you live in it or no you don’t o Population, geographic information Generate binary location variables o Age is also becoming a determinant for use of gasoline -Sources of Data 3 Main: (1) Observational, (2) Survey, (3) Experimental Observational: GPS, transit, shop, NSA, IRS, health care o Two groups: #1 gets free insurance and care, #2 gets free insurance but not care o Who utilizes care? Group 1 utilizes about 40% more care Free: quantity demanded went up People who get free insurance utilize it Experimental Methods can go bad: concentration camps, Tuskeege experiments (African American men with syphallis penicillin exists but this experiment continues on once it is released and they withhold care for them) Other issues: running experiments on a non-random population -4 Types of Data 4 Types of Data: (1) Cross-Sectional, (2) Pooled-cross sections, (3) Panel- longitudinal, (4) Time Series Cross Sectional: People, institutions, point in time Pooled-Cross Sections: Several cross-sections, different subjects, overtime Panel/Longitudinal: same people over time o Ex: Texas star program [track education/learning] Time Series: overtime, statistics, very macro (the other three are very micro) -Correlation vs. Causation Two variables A and B move together A B or BA o A Increases, B Increases OR A Increases, B decreases o B Increases, A Increases OR B increases, A decreases Example: Ice Cream consumption increases ; Shark Attacks Increase o Result of Hot Weather o Education, wealth, but what about ability? o Point: there could be a third factor working/going on -Appendix A – Statistics Sample: [68, 72, 32, 84, 93, 100] What is the min of this sample? 32 Max? 100 Average Mean: A measure of central tendency Median: middle observation of your data informative when your sample has a lot of outliers Range: spread/variation n ∑ xi Summation Notation: i=1 “sum over all your samples” Properties: n c=nc o (1) Let C be a constant: i=1 n n c∗xi c x o (2) C is a constani=1 = i=1
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