### Econometrics Notes & R Code (UCSC Econ113)

 Very much in development. Hopefully helpful resources.    Econometrics Topics to Know    Basic Statistics ReviewExpected ValueCentral TendencyMean - equation. outliers problem? videos Mean - Sample v Population MeanMedian - equation. how does this correct for the outlier problem?Mode - what is the mode? wikipediaExample of finding the mean, median and mode by handHistogram - be prepared to draw a very simple histogram.   hist in R - Video Histogram IntroVideos: Population Variance, Sample VarianceStandard Deviation - equation in terms of variance? [Video 13:07]Example of variance and standard deviation calc by handRelatedness of Two Variables Covariance, Rules for Covariance and Variance [Video 15:54] Estimators of variance, covariance, and correlation [Video 4:07] Normal DistributionNormal Distribution Videos: Vid1, Vid2, Vid3(example), Vid4 (more z-table examples), Vid5(abstract example), Vid6.  Econometrics - the goal of causal inference. Using data to test a theory. Data-set Types: Cross Section, Time Series, Panel Data. Simple Regression ModelOrdinary Least Squares (OLS) Estimator. what is the problem OLS solves?Videos: Intro, OLS Proof Pt1, Pt2, Pt3, Pt4 (the Punchline) - Know dependent and independent variables. Know 'u' the error term. - Derivation for \beta^hat_1 & \beta^hat_0Gauss Markov Assumptions required for "\beta\hat\_0" and "\beta\hat_1" to be an unbiased estimator for for the population "\beta_0" & "\beta_1" terms.  "Unbiased Estimate" means...Interpreting Beta - how to interpret your estimate of beta given a level-level, log-level & log-log regression.How to Interpret a Level-Level Regression Model (video 5:00)How to Interpret a Log-Level Regression Model (video 6:40)How to Interpret a Level-Log Regression Model (video 6:50)How to Interpret a Log-Log Regression Model (video 5:30)Goodness of Fit and R-Squared. Homoskedasticity AssumptionHypothesis Testing & P-ValuesIntro to Hypoth Testing VideoVideo: one-sided vs two-sided testNotes:LSE Intro to Econometrics (see email)Intro to applied Stats: R code: http://www.stanford.edu/class/stats191/helpR.html Helpful R Code    Reference Websites for RDownload R Software"Try R" from Code SchoolA quick (and free), two-hours-or-less tutorial introducing the basics of R software. Quick-R (Reference for the basics of R) Dumbed-down R-Software documentation. Check out the right navigation window. Resources for R (More Example R Code)Examples - getting started with RGetting Started with R Software  Too often, the hardest part.Basic Statistics & VisualizationEconometrics with R SoftwareDo a Regression (with R) (video 16:40)Short video that walks through the code to do a linear regression  with R. This works for a univariate and multivariate model. It'll deliver regression coefficient estimates, standard error, t-statistic, P-values, R-squared, adjusted R-squared, omnibus F-test statistic and more.Detailed overview of the lm(..) function for OLS estimation. Simple Regression with R - Ordinary Least Squares solved by hand and in R Software. Introduction to R-regression summary. How to run a regression where data is inputed manually. Multivariate Regression with R - with a walk-through R's OLS Regression Summary Output. lm(...) functionHypothesis Testing, Significance Test - testing whether or not a model's variable is statistically significant. Hypothesis Testing, Other Hypotheses - testing whether or not a model's variable has some specific effect on the dependent variable. Confidence Interval - calculation and interpretation. P-Value - hand calculation. R-software's P-Value. Interpretation of P-Value. Hypothesis Testing - Testing Hypothesis about Linear Combination of Parameters -- testing whether or not two variable have identical effects on the dependent variable. Hypothesis Testing - F-Statistics - Testing General Linear Restrictions. testing whether or not multiple variables are jointly statistically significant determinants of the dependent variable. Hypothesis Testing - F-Statistic - Testing Overall Significance of a Regression. Standardizing Variables in a Regression Model. Dummy variablesInteraction termsDummy variables in interaction termsCategorical variables. - running regression over 'factor' vector, dummy variable trap, interpretation.
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Curtis Kephart,
May 22, 2011, 12:04 PM
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Curtis Kephart,
Apr 11, 2012, 11:10 AM
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Curtis Kephart,
May 22, 2011, 12:04 PM
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Curtis Kephart,
May 22, 2011, 12:04 PM
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Curtis Kephart,
May 22, 2011, 12:04 PM
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Curtis Kephart,
May 22, 2011, 12:04 PM
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Curtis Kephart,
Dec 12, 2012, 11:48 AM