Econ2206 notes 1.1 PDF

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Econ2206 notes for lecture 1.1...

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Econ2206: Introductory Econometrics Week 1: Administration and Introduction Assessment dates o 2 Problem sets  Due: 8th October, November 27th o Midterm  28th October o Online Quizzes  Week 2, 4, 7, 10 Check out STATA from MyAccess

Econometrics The use of statistical methods to analyse economic data Typical goals o o o o

Estimating relationships between economic variables Testing economic theories and hypotheses Forecasting economic variables Evaluating and implementing government and business policy

Example: We have data for different countries and years of the unemployment rate and inflation The goals here are:    

Estimating the relationship: When employment is high is inflation also high? Testing an economic theory: High inflation causes employment to go up Forecasting: How high inflation will be in the next years Evaluation: If the AUS Govt increases taxes what happened to unemployment

Economic Data Types of data: 

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Cross-sectional o Sample of individuals, households, firms at a given point of time/period o Observations typically more or less independent  E.g. pure random sampling  Census data is often random sampled and cross sectional Time series o Observations for an individual, firm, country,… collected OVER TIME  E.g Stock prices, exchange rates, consumer price index, GDP

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They are typically serially correlated  Observations between themselves are not always dependent but there is some relationship o Trends, cycles, seasonality o Data Frequency (how often we observe the variable) Pooled cross section o 2 or more cross sections combined in one data set o Used to evaluate policy changes  Effect of change in property tax on house prices  Random sample in 1993 and Random sample in 1995  The 2 samples contain different observation sets (essentially pre VS post) Panel/Longitudinal data o Same cross-sectional units followed over time o Combines cross-sectional and time series dimension o Very useful in modelling  E.g. City crime statistics: each city is observed in 2 years  E.g. effect of police on crime rates may exhibit time lag

Random Variable VS Sample Random variable: Function that describes the possible outcomes and probabilities of a random event. E.G. A coin toss. We always write random variables in capital letters – X, W, or COIN Random sample: The extrapolation of that random variable. I.E actually flipping the coin and recording observations of the first 10 tosses. {w1 , w2 ,… , w 10 } We always write random samples in lower case – w, x, coin...