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Course ObjectivesThe aim of the course is to help you develop a working knowledge ofeconometrics and its applications to real-world economic data. The course will cover a range of topics: − Simple Regression − Multiple Regression − estimation, inference − extensions − Learn a specialised econometric software package By the end of session you will be able to: =⇒ read and understand most analyses performed by econometricians =⇒ conduct your own empirical research. () Lecture 1 3 / 31
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Textbooks:The required text is: J.M. Wooldridge (2008) Introductory Econometrics: A Modern Approach. 4th Edition 3rd edition is ﬁne A useful companion book: J.M. Wooldridge (2008) Student Solution Manual for Introductory Econometrics available electronically through the text website. () Lecture 1 4 / 31
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Course Website (Blackboard)This site will contain: Lecture handouts (syllabus, etc) Lecture notes for each class Homework assignments, including data sets Homework solutions Data sets and STATA logs for in class examples Special announcements (sent via email also) () Lecture 1 6 / 31
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Topics covered – rough timeline Topic Classes Approx. Introduction 2 Simple Linear Regression 3 Multiple Linear Regression 5 Small Sample Inference 2 Large Sample Inference 1 Further Issues including Dummies 4 Time Series 2 Panel Data 2 Qualitative Response 2 Endogenous Regressors and IV 2 () Lecture 1 7 / 31
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Software – STATAIt is crucial that you have access to STATA and that you do theempirical exercises Access Options: 1 Timeshare through the web from anywhere 2 Purchase through STATA gradplan – see syllabus – about $100 3 Labs in Burdine? What does STATA look like? Lets see! () Lecture 1 8 / 31
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Lecture 2 - OutlineThe Nature of Econometrics What is Econometrics ? The Structure of Economic Data Causality ‘Ceteris Paribus’ and correlation () Lecture 1 9 / 31
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What is Econometrics ?Econometrics concerns the use of statistical methods in: estimating economic relationships testing economic theory evaluating government and business policy. forecasting and prediction () Lecture 1 10 / 31
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Applications Econometrics has many wider applications, for example 1 the eﬀect of class size or spending on student performance 2 the eﬀect of education on wages 3 testing for discrimination in labour and credit markets 4 the eﬀect of minimum wages on unemployment 5 the eﬀect of CEO compensation on ﬁrm performance 6 the eﬀect of govt policies on inﬂation and economic growth Common feature: Econometrics deals with nonexperimental data drawn from observing economic events (the data are not collected through controlled experiments in a laboratory). () Lecture 1 11 / 31
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Conducting Empirical Economic Analysis Econometrics is used in every branch of applied economics Empirical Analysis uses data to test the predictions of a theory or estimate a relationship. An empirical analysis generally consists of 1 An economic model - which may be formally developed (e.g. derivation of consumer demand equations from a model of utility maximisation) or based on intuitive reasoning. 2 An econometric model - which requires specifying the nature of the relationship between variables () Lecture 1 12 / 31
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Conducting Empirical Economic AnalysisExample of an econometric model – a multiple regression model:wage = β 0 + β 1 .educ + β 2 .exper + υwhere:wage = hourly wage rateeduc = years of educationexper = years of employmentβ 0 , β 1 , β 2 = parameters which describe the direction and strength of therelationship between the wage and the factors which determine itυ = error term (or ‘disturbance term’) which contains unobservable factors(innate ability, job characteristics,...) () Lecture 1 13 / 31
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Conducting Empirical Economic Analysis With this model a range of hypotheses can be stated in terms of the unknown parameters ( β 0 , β 1 , β 2 ). Empirical analysis requires data, and econometric methods are used to estimate the parameters of the model and to formally test hypotheses of interest. The model can also be used to make predictions. Methods need to take into account the structure of the data 4 main data structures: cross-sectional data, time-series data, pooled cross sections, panel data () Lecture 1 14 / 31
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The Structure of Economic DataA. Cross-Sectional Data sample of individuals, households, ﬁrms, countries or other units taken at a point in time (“snapshot”) usually obtained by random sampling from the population (and the sample is “representative”) cross-sectional data are widely used in economics and other social sciences. Very common in applied micro such as labor economics, public economics, industrial organization, health economics =⇒ this is the main data structure we will focus on if randomly sampled, order of observations is unimportant () Lecture 1 15 / 31
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Cross-Sectional DataExamples of a cross-sectional data set:(a) Data set on wages and other personal characteristics () Lecture 1 16 / 31
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Cross-Sectional Data(b) Data set on economic growth and country characteristics () Lecture 1 17 / 31
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Time Series DataB. Time Series Data observations on a variable (or set of variables) over time E.g. stock prices, cpi, gdp, crime rates. The chronological ordering of observations is important =⇒ observations cannot be assumed to be independent over time, most economic time series are (strongly) related to their recent histories =⇒ econometric model needs to take this into account Data frequency is important, due to seasonal patterns (e.g. daily, weekly, monthly, quarterly, annual) () Lecture 1 18 / 31
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Time Series DataExample: data on minimum wages () Lecture 1 19 / 31
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Pooled Cross SectionsC. Pooled Cross Sections some data sets have both cross-sectional and time series properties. E.g. 2 cross-sectional family surveys in US - one in 2000 recording income, expenditure, family size,... - a new random sample in 2005, with same questions =⇒ pool them to increase sample size =⇒ no family is in the sample for the 2 years () Lecture 1 20 / 31
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Pooled Cross Sections Pooled cross-sections can be an eﬀective way to analyse govt policies (e.g. look at economic relationships before and after the policy was introduced) Pooled cross sections are also very useful for studying group dynamics over time (e.g. how are average wages evolving for the group who entered the labour market during the last recession; what determines changes in median house prices in speciﬁc areas of US) Can analyse like a standard cross-section, though need to allow for changes in variables over time () Lecture 1 21 / 31
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Pooled Cross SectionsExample: Two years of house prices () Lecture 1 22 / 31
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Panel DataD. Panel (or Longitudinal) Data Consists of a time series for each cross-sectional unit =⇒ follow the same individuals / ﬁrms etc. over time Example: crime statistics at the city level – to study things like eﬀect of law enforcement or economic conditions on crime () Lecture 1 23 / 31
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Panel Data panel data has some important advantages over other data structures (we can control for certain types of unobserved characteristics, and can study lags in behaviour). some important questions cannot be answered without panel data =⇒ e.g. studying dynamics behaviour of individual units brieﬂy consider simple panel data methods () Lecture 1 24 / 31
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Causal Eﬀects, Ceteris Paribus and CorrelationCausality and Ceteris Paribus in Econometric Analysis In most tests of economic theory, and for evaluating policy, the goal is to infer a causal eﬀect of one variable on another Most propositions in economics are ‘ceteris paribus’ by nature Example: the responsiveness of the demand for coﬀee to price - holding all other factors constant (such as income, prices of other goods). If these other factors are not constant, we cannot determine the casual eﬀect of a price change on quantity demanded not feasible to literally hold ‘all else equal’ .... but have enough other factors been held constant to infer causality ? properly applied, econometric methods can simulate a ceteris paribus experiment(⇒) economic theory and econometrics together can help us uncovercausal eﬀects. () Lecture 1 25 / 31
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Causality and Correlation We may recall (hopefully!) from Prob/Stats ECO329 the concept of correlation and covariance Measures of linear association between 2 variables Example: Education and Wages. Do people with higher levels of education tend to have higher wages? Do people with higher wages have more education? Correlation is a measure of this assocation Let r be the correlation between wage for person i - say yi and education xi () Lecture 1 26 / 31
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Recall (?????) that, ∑n=1 (xi − x )(yi − y ) i ¯ ¯ r= n n ∑i =1 (xi − x )2 ∑i =1 (yi − y )2 ¯ ¯ r > 0 means that large y are associated with large x r < 0 means that large y are associated with small x r = 0 means no linear associationcould be nonlinearcould be no association at all () Lecture 1 27 / 31
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Keep in Mind One cannot conclude causation by simply looking at correlation Note r is symmetric in x and y so: does x cause y does y cause x Even if one thought it went a particular direction there may be other mitigating factors that need to be taken into account () Lecture 1 28 / 31
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Examples:Wages and Education are correlated (as we will see) Which direction is plausible and why? Other factors? () Lecture 1 29 / 31
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Examples:SportsIn watching football I often hear/see statements that are of the form:“When team x (insert your favorite) runs the ball more than 30 times theywin 80% of the games but when they run less than 30 times they only win 30% of the games.” What the heck does this mean? Clearly it looks like there is a correlation between number of running plays and the chance of winning. But is it a causal eﬀect? If it were causal then it would mean the coach could just make sure he runs the ball at least 30 times (regardless) and will win more often Is this how it works? () Lecture 1 30 / 31
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Examples:In the popular press there are many instances of people trying to infer acausal relationship between variables based simply on correlations betweentwo variables. Try and listen for examples of this. Now lets play with some data! 1 Wage data – relation between education and wages 2 Test score data – relation between class size and standardized test scores () Lecture 1 31 / 31
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