Lecture 3: Multiple Regression
|
|
- Georgina Garrison
- 6 years ago
- Views:
Transcription
1 Lecture 3: Multiple Regression R.G. Pierse 1 The General Linear Model Suppose that we have k explanatory variables Y i = β 1 + β X i + β 3 X 3i + + β k X ki + u i, i = 1,, n (1.1) or Y i = β j X ji + u i, i = 1,, n j=1 where X 1i = 1, i = 1,, n is an intercept. 1.1 Assumptions of the Model E(u i ) = 0, i = 1,, n (A1) E(u i ) = σ, i = 1,, n (A) E(u i u j ) = 0, i, j = 1,, n j i (A3) X values are fixed in repeated sampling (A4) We now need to make an additional assumption about the explanatory variables: The variables X j are not perfectly collinear. (A4 ) Perfect collinearity occurs when there is one or more variables X m such that X mi = c j X ji, i = 1,, n j m where c j are fixed constants. In this case, one of the variables is redundant and should be dropped from the regression.when a set of variables is perfectly 1
2 collinear, dropping any one of them will solve the problem. An example of a perfectly collinear set of variables would be GDP plus all the components of the GDP identity. Y i is a random variable with the following properties: E(Y i ) = β 1 + β X i + β 3 X 3i + + β k X ki, i = 1,, n V ar(y i ) = E(Y i E(Y i )) = E(u i ) = σ, i = 1,, n Interpretation of the OLS Coefficients β j represents the effect of changing the variable X j on the dependent variable Y while holding all other variables constant. In mathematical terms this is the partial derivative of Y with respect to X j β j = Y X j 3 The Ordinary Least Squares Estimator Y i = β j X ji + e i, i = 1,, n (3.1) j=1 The OLS estimator is the estimator that minimises the sum of squared residuals s = n e i. min β 1,, β k s = e i = ( Y i ) β j X ji Differentiating this expression with respect to the k parameters β j gives k first order conditions: s β 1 = s β = j=1 (Y i β 1 β X i β ) k X ki = 0 (3.) (Y i β 1 β X i β k X ki ) X i = 0 (3.3)
3 and so on up to s β k = (Y i β 1 β X i β k X ki ) X ki = 0 (3.4) Note that the first order conditions can be rewritten as e i = 0 and X ji e i = 0, j =,, k (3.5) Rearranging the equations gives the k normal equations in the k unknowns β j Y i = n β 1 + β X i + + β k X ki Y i X i = β 1 and so on up to Y i X ki = β 1 X i + β X ki + β Xi + + β k X i X ki X i X ki + + β k X ki. Solving these k equations defines the OLS estimators. Note that from the first normal equation β 1 = 1 n Y i j= β j 1 n X ji = Y β j X j (3.6) where Y = 1 n n i Y i and X j = 1 n n i X ji are the sample means of Y and X j respectively. 3.1 Special case (k = 3) In the general case, the formulae for the estimators β j are messy in summation notation. Elegant expressions require matrix notation. However, for the case k = 3 we have β = yi x i 3i y i x 3i xi x 3i i 3i ( i x 3i ) j= and β 3 = yi x 3i i y i x i xi x 3i i 3i ( i x 3i ) where, as before, lower case letters denotes deviations from sample means. 3
4 4 Properties of the OLS Estimator The OLS estimator in the general model has the same properties as in the simple regression model. Proofs will not be given here. 4.1 The OLS Estimator β j is Unbiased It can be shown that 4. OLS is BLUE E( β j ) = β j It can be shown that the OLS estimators β j are Best Linear Unbiased Estimators (BLUE) in the general regression model. 4.3 The Variance of OLS Estimators For the general case, again the expressions are messy in summation notation, and elegant expressions require matrix notation. However, for the k = 3 case we have [ V ar( β 1 ) = σ 1 n + X x 3i + X ] 3 x i X X 3 xi x 3i i x 3i ( i x 3i ) V ar( β ) = σ 3i i 3i ( i x 3i ) and V ar( β 3 ) = σ i i 3i ( i x 3i ). We now also have an expression for the covariance between β and β 3 Cov( β, β 3 ) = σ i x 3i i 3i ( i x 3i ). 4.4 The OLS estimator of σ It can be shown that the estimator σ = n e i n k (4.1) is an unbiased estimator of σ. 4
5 5 Matrix Representation Writing out all n observations in the model as a set of equations Y 1 = β 1 X 11 + β X 1 + β 3 X β k X k1 + u 1 Y = β 1 X 1 + β X + β 3 X β k X k + u. Y n = β 1 X 1n + β X n + β 3 X 3n + + β k X kn + u n this can be rewritten in matrix notation as Y 1 X 11 X 1 X k1 Y. = X 1 X X k Y n X 1n X n X kn or y = Xβ + u β 1 β. β k + where y and u are an n 1 column vectors, β is a k 1 column vector and X is an n k matrix. In matrix notation the OLS estimator β is given by the expression β = (X X) 1 X y u 1 u. u n 6 Goodness of Fit and R Recall that Y i = or, taking mean deviations, y i = β j X ji + e i = Ŷi + e i j=1 β j x ji + e i = ŷ i + e i. j= Squaring and summing over all observations gives yi = ŷi + e i + ŷ i e i = ŷi + 5 e i
6 since and ŷ i e i = β j j= x ji e i x ji e i = 0, j from (3.5). Defining n y i = Total Sum of Squares (T SS), n ŷ i = Explained Sum of Squares (ESS), and n e i = Residual Sum of Squares (RSS), we have the identity T SS = ESS + RSS and define the Multiple Coefficient of Determination, R as R = ESS n T SS = ŷ i n y i or, alternatively, R = 1 RSS n T SS = 1 ê i n. y i R measures the proportion of the total variation of Y i about its mean that is explained by all the regressors jointly. This is the goodness-of-fit of the regression. 6.1 Properties of R As long as an intercept is included in the set of regressors then 0 R 1. (If an intercept is not included then it is possible to get a negative value for R ). R will increase as k increases i.e. more regressors are added. In the limit when k = n then R = 1. This suggests that choosing the equation with the highest R will not always be a sensible strategy. The value of R depends on the scale of the dependent variable. R measures cannot be compared between equations with different dependent variables. Example: Consider the equation Subtracting Y t 1 from both sides gives Y t = a + by t 1 + u t. Y t Y t Y t 1 = a + (b 1)Y t 1 + u t 6
7 but does not affect the error term or the coefficients. This is merely a renormalisation of the equation and the RSS of the two will be the same. However, the R statistic will give very different values, purely because of the different scales of the dependent variables. 6. Correcting for degrees of freedom: R R = 1 (1 R ) n 1 n k R makes a correction to R based on the number of parameters in the model k. Note that R < R in general, and that R can be negative. 7 Hypothesis Testing In order to make statistical inferences on the parameter estimates β we must make a further assumption: u i iid N(0, σ ), i = 1,, n (A5) that the errors u i are distributed as independent normal variables. For hypotheses involving a single parameter β j, β j β j V ar( β j ) t n k where t n k is the Student t distribution with n k degrees of freedom. Note that V ar( β j ) is the standard error of the estimator β σ βj j. This is just as in the simple regression case. However, there is now the additional possibility of tests involving more than one parameter, or more than one restriction. 7.1 Testing equality of two coefficients Without loss of generality, let the null hypothesis be H 0 : β = β 3 or, alternatively, H 0 : β β 3 = 0. 7
8 This is a single restriction although it involves two parameters. It can be shown that β β 3 V ar( β β t n k 3 ) where V ar( β β 3 ) = V ar( β ) + V ar( β 3 ) Ĉov( β, β 3 ) and the estimated variances and covariances needed to compute this test are available in any computer regression package. 7. Jointly testing all the coefficients One interesting hypothesis is that or, alternatively, H 0 : β = β 3 = = β k = 0 H 0 : β j = 0, j =,, k so that the model collapses to the intercept term Y i = β 1 + u i, i = 1,, n Note that the hypothesis H 0 involves jointly testing k 1 restrictions. It can be shown that a test of this hypothesis is given by ESS/(k 1) RSS/(n k) = R /(k 1) (1 R )/(n k) F k 1,n k where F k 1,n k is the F distribution with k 1 and n k degrees of freedom. The Student t distribution and the F distribution are related. If x is distributed as t n k then x is distributed as F 1,n k. 7.3 General F Testing More generally, we may often be interested in jointly testing several linear restrictions. Suppose that there are r restrictions. Then the null hypothesis can be written H 0 : a ij β j a i0 = 0, i = 1,, r j=1 where a ij are fixed constants. 8
9 To compute a test of this hypothesis we need to estimate the model both under the null hypothesis (imposing the restrictions) and on the alternative hypothesis (unrestricted). Let RSS U be the sum of squared residuals for the unrestricted model, and RSS R be the sum of squared residuals of the restricted model. Then it can be shown that a test of H 0 is given by 7.4 Testing σ (RSS R RSS U ) /r RSS U /(n k) Finally, as with the simple regression model F r,n k where χ n k (n k) σ σ χ n k (7.1) is the Chi-squared distribution with n k degrees of freedom. 8 Functional Form We require that the OLS model is linear in parameters. It is not necessary that it is linear in variables. It is often possible to tranform variables in order to make an equation linear in parameters. 8.1 Example: Cobb-Douglas Production Function Q = AK γ L δ where γ + δ = 1 implies constant returns to scale. Taking logarithms we get or ln Q = ln A + γ ln K + δ ln L Y = b 0 + b 1 X 1 + b X + u where Y = ln Q, X 1 = ln K, X = ln L, b 0 = ln A, b 1 = γ, and b = δ, which is linear in parameters. Constant return to scale can be tested in this model by the linear hypothesis that b 1 + b = 1. Note that an error term u has been added to the final equation. If we assume that u N(0, σ ) then this implies that the error term on the original equation is e u and is distributed with a log-normal distribution. Specifications cannot always be transformed to make them linear in parameters. Consider the CES production function Q = A[γK ρ + δl ρ ] µ/ρ. 9
10 This is intrinsically nonlinear in parameters. Similarly, the equation Y = β 0 + β 1 X 1 + β 0 β 1 X + u is linear in variables but nonlinear in parameters. 8. Log-linear Specifications The Cobb-Douglas example is one in which all the variables in the econometric equation are in logarithms. Such specifications are called log-linear or log-log and are very common in economics. Compare a linear and a log-linear specification and Y = a + bx + u ln Y = c + d ln X + v. In the linear specification b = Y/ X is the marginal response of Y to a change in X. Note that the effect of doubling X on Y depends on the level of X, because the elasticity is not constant. In the log-linear specification d = ln Y/ ln X which is the elasticity of Y with respect to X. Log-linear models impose the assumption that elasticities are constant. In log-linear models the slope parameters are independent of the scale of measurement of the variables. Suppose we rescale X by multiplying by a factor k, denoting the new variable kx as X. In the linear model the effect is that the parameter b becomes b = b/k. In the log-linear model we have ln X = ln k+ln X so that the rescaling factor is constant and changes the intercept c but not the elasticity d. Many economic variables represent money flows or stocks or prices that logically cannot ever be negative. In principle, a linear specification can give a negative value for the dependent variable if u is large and negative enough. In the log-linear specification, the dependent variable can never become negative since Y = e ln Y Linearity as an Approximation Consider a general function of a single variable Y = g(x). Weierstrass s theorem states that, under certain very general conditions, Y a 0 + a 1 X + a X + a 3 X
11 so that the function g(x) can be approximated, to any desired degree of precision, by a polynomial function in X. This polynomial function is linear in parameters so it can be estimated by OLS, treating X, X, X 3 etc. as separate regressors. Note that X, X, X 3, although they are exact functions of each other, are not linearly dependent so that there is no perfect collinearity here. 11
Lecture 5: Omitted Variables, Dummy Variables and Multicollinearity
Lecture 5: Omitted Variables, Dummy Variables and Multicollinearity R.G. Pierse 1 Omitted Variables Suppose that the true model is Y i β 1 + β X i + β 3 X 3i + u i, i 1,, n (1.1) where β 3 0 but that the
More informationThe regression model with one fixed regressor cont d
The regression model with one fixed regressor cont d 3150/4150 Lecture 4 Ragnar Nymoen 27 January 2012 The model with transformed variables Regression with transformed variables I References HGL Ch 2.8
More informationReview of Econometrics
Review of Econometrics Zheng Tian June 5th, 2017 1 The Essence of the OLS Estimation Multiple regression model involves the models as follows Y i = β 0 + β 1 X 1i + β 2 X 2i + + β k X ki + u i, i = 1,...,
More informationMultiple Regression Analysis
Chapter 4 Multiple Regression Analysis The simple linear regression covered in Chapter 2 can be generalized to include more than one variable. Multiple regression analysis is an extension of the simple
More informationLECTURE 5 HYPOTHESIS TESTING
October 25, 2016 LECTURE 5 HYPOTHESIS TESTING Basic concepts In this lecture we continue to discuss the normal classical linear regression defined by Assumptions A1-A5. Let θ Θ R d be a parameter of interest.
More informationLecture 6: Dynamic Models
Lecture 6: Dynamic Models R.G. Pierse 1 Introduction Up until now we have maintained the assumption that X values are fixed in repeated sampling (A4) In this lecture we look at dynamic models, where the
More informationCh 2: Simple Linear Regression
Ch 2: Simple Linear Regression 1. Simple Linear Regression Model A simple regression model with a single regressor x is y = β 0 + β 1 x + ɛ, where we assume that the error ɛ is independent random component
More informationEconometrics Multiple Regression Analysis: Heteroskedasticity
Econometrics Multiple Regression Analysis: João Valle e Azevedo Faculdade de Economia Universidade Nova de Lisboa Spring Semester João Valle e Azevedo (FEUNL) Econometrics Lisbon, April 2011 1 / 19 Properties
More informationSimple Linear Regression
Simple Linear Regression Christopher Ting Christopher Ting : christophert@smu.edu.sg : 688 0364 : LKCSB 5036 January 7, 017 Web Site: http://www.mysmu.edu/faculty/christophert/ Christopher Ting QF 30 Week
More informationSo far our focus has been on estimation of the parameter vector β in the. y = Xβ + u
Interval estimation and hypothesis tests So far our focus has been on estimation of the parameter vector β in the linear model y i = β 1 x 1i + β 2 x 2i +... + β K x Ki + u i = x iβ + u i for i = 1, 2,...,
More informationMFin Econometrics I Session 4: t-distribution, Simple Linear Regression, OLS assumptions and properties of OLS estimators
MFin Econometrics I Session 4: t-distribution, Simple Linear Regression, OLS assumptions and properties of OLS estimators Thilo Klein University of Cambridge Judge Business School Session 4: Linear regression,
More information1. The OLS Estimator. 1.1 Population model and notation
1. The OLS Estimator OLS stands for Ordinary Least Squares. There are 6 assumptions ordinarily made, and the method of fitting a line through data is by least-squares. OLS is a common estimation methodology
More informationLECTURE 2 LINEAR REGRESSION MODEL AND OLS
SEPTEMBER 29, 2014 LECTURE 2 LINEAR REGRESSION MODEL AND OLS Definitions A common question in econometrics is to study the effect of one group of variables X i, usually called the regressors, on another
More informationApplied Econometrics (QEM)
Applied Econometrics (QEM) based on Prinicples of Econometrics Jakub Mućk Department of Quantitative Economics Jakub Mućk Applied Econometrics (QEM) Meeting #3 1 / 42 Outline 1 2 3 t-test P-value Linear
More informationContest Quiz 3. Question Sheet. In this quiz we will review concepts of linear regression covered in lecture 2.
Updated: November 17, 2011 Lecturer: Thilo Klein Contact: tk375@cam.ac.uk Contest Quiz 3 Question Sheet In this quiz we will review concepts of linear regression covered in lecture 2. NOTE: Please round
More informationHomoskedasticity. Var (u X) = σ 2. (23)
Homoskedasticity How big is the difference between the OLS estimator and the true parameter? To answer this question, we make an additional assumption called homoskedasticity: Var (u X) = σ 2. (23) This
More informationLecture 14 Simple Linear Regression
Lecture 4 Simple Linear Regression Ordinary Least Squares (OLS) Consider the following simple linear regression model where, for each unit i, Y i is the dependent variable (response). X i is the independent
More informationLECTURE 6. Introduction to Econometrics. Hypothesis testing & Goodness of fit
LECTURE 6 Introduction to Econometrics Hypothesis testing & Goodness of fit October 25, 2016 1 / 23 ON TODAY S LECTURE We will explain how multiple hypotheses are tested in a regression model We will define
More informationECON The Simple Regression Model
ECON 351 - The Simple Regression Model Maggie Jones 1 / 41 The Simple Regression Model Our starting point will be the simple regression model where we look at the relationship between two variables In
More informationRecent Advances in the Field of Trade Theory and Policy Analysis Using Micro-Level Data
Recent Advances in the Field of Trade Theory and Policy Analysis Using Micro-Level Data July 2012 Bangkok, Thailand Cosimo Beverelli (World Trade Organization) 1 Content a) Classical regression model b)
More informationLectures 5 & 6: Hypothesis Testing
Lectures 5 & 6: Hypothesis Testing in which you learn to apply the concept of statistical significance to OLS estimates, learn the concept of t values, how to use them in regression work and come across
More informationGreene, Econometric Analysis (7th ed, 2012)
EC771: Econometrics, Spring 2012 Greene, Econometric Analysis (7th ed, 2012) Chapters 2 3: Classical Linear Regression The classical linear regression model is the single most useful tool in econometrics.
More information2 Regression Analysis
FORK 1002 Preparatory Course in Statistics: 2 Regression Analysis Genaro Sucarrat (BI) http://www.sucarrat.net/ Contents: 1 Bivariate Correlation Analysis 2 Simple Regression 3 Estimation and Fit 4 T -Test:
More informationEC212: Introduction to Econometrics Review Materials (Wooldridge, Appendix)
1 EC212: Introduction to Econometrics Review Materials (Wooldridge, Appendix) Taisuke Otsu London School of Economics Summer 2018 A.1. Summation operator (Wooldridge, App. A.1) 2 3 Summation operator For
More information1. The Multivariate Classical Linear Regression Model
Business School, Brunel University MSc. EC550/5509 Modelling Financial Decisions and Markets/Introduction to Quantitative Methods Prof. Menelaos Karanasos (Room SS69, Tel. 08956584) Lecture Notes 5. The
More informationEconometrics Summary Algebraic and Statistical Preliminaries
Econometrics Summary Algebraic and Statistical Preliminaries Elasticity: The point elasticity of Y with respect to L is given by α = ( Y/ L)/(Y/L). The arc elasticity is given by ( Y/ L)/(Y/L), when L
More informationMFin Econometrics I Session 5: F-tests for goodness of fit, Non-linearity and Model Transformations, Dummy variables
MFin Econometrics I Session 5: F-tests for goodness of fit, Non-linearity and Model Transformations, Dummy variables Thilo Klein University of Cambridge Judge Business School Session 5: Non-linearity,
More informationRegression #8: Loose Ends
Regression #8: Loose Ends Econ 671 Purdue University Justin L. Tobias (Purdue) Regression #8 1 / 30 In this lecture we investigate a variety of topics that you are probably familiar with, but need to touch
More informationEconometrics of Panel Data
Econometrics of Panel Data Jakub Mućk Meeting # 2 Jakub Mućk Econometrics of Panel Data Meeting # 2 1 / 26 Outline 1 Fixed effects model The Least Squares Dummy Variable Estimator The Fixed Effect (Within
More informationBusiness Economics BUSINESS ECONOMICS. PAPER No. : 8, FUNDAMENTALS OF ECONOMETRICS MODULE No. : 3, GAUSS MARKOV THEOREM
Subject Business Economics Paper No and Title Module No and Title Module Tag 8, Fundamentals of Econometrics 3, The gauss Markov theorem BSE_P8_M3 1 TABLE OF CONTENTS 1. INTRODUCTION 2. ASSUMPTIONS OF
More informationWISE International Masters
WISE International Masters ECONOMETRICS Instructor: Brett Graham INSTRUCTIONS TO STUDENTS 1 The time allowed for this examination paper is 2 hours. 2 This examination paper contains 32 questions. You are
More informationMultiple Regression Analysis: Estimation. Simple linear regression model: an intercept and one explanatory variable (regressor)
1 Multiple Regression Analysis: Estimation Simple linear regression model: an intercept and one explanatory variable (regressor) Y i = β 0 + β 1 X i + u i, i = 1,2,, n Multiple linear regression model:
More informationNonlinear Regression Functions
Nonlinear Regression Functions (SW Chapter 8) Outline 1. Nonlinear regression functions general comments 2. Nonlinear functions of one variable 3. Nonlinear functions of two variables: interactions 4.
More informationSimple Linear Regression
Simple Linear Regression ST 430/514 Recall: A regression model describes how a dependent variable (or response) Y is affected, on average, by one or more independent variables (or factors, or covariates)
More informationCorrelation Analysis
Simple Regression Correlation Analysis Correlation analysis is used to measure strength of the association (linear relationship) between two variables Correlation is only concerned with strength of the
More informationHypothesis testing Goodness of fit Multicollinearity Prediction. Applied Statistics. Lecturer: Serena Arima
Applied Statistics Lecturer: Serena Arima Hypothesis testing for the linear model Under the Gauss-Markov assumptions and the normality of the error terms, we saw that β N(β, σ 2 (X X ) 1 ) and hence s
More informationEconometrics of Panel Data
Econometrics of Panel Data Jakub Mućk Meeting # 3 Jakub Mućk Econometrics of Panel Data Meeting # 3 1 / 21 Outline 1 Fixed or Random Hausman Test 2 Between Estimator 3 Coefficient of determination (R 2
More informationECON Introductory Econometrics. Lecture 7: OLS with Multiple Regressors Hypotheses tests
ECON4150 - Introductory Econometrics Lecture 7: OLS with Multiple Regressors Hypotheses tests Monique de Haan (moniqued@econ.uio.no) Stock and Watson Chapter 7 Lecture outline 2 Hypothesis test for single
More informationREVIEW (MULTIVARIATE LINEAR REGRESSION) Explain/Obtain the LS estimator () of the vector of coe cients (b)
REVIEW (MULTIVARIATE LINEAR REGRESSION) Explain/Obtain the LS estimator () of the vector of coe cients (b) Explain/Obtain the variance-covariance matrix of Both in the bivariate case (two regressors) and
More informationWooldridge, Introductory Econometrics, 4th ed. Chapter 2: The simple regression model
Wooldridge, Introductory Econometrics, 4th ed. Chapter 2: The simple regression model Most of this course will be concerned with use of a regression model: a structure in which one or more explanatory
More informationECON 5350 Class Notes Functional Form and Structural Change
ECON 5350 Class Notes Functional Form and Structural Change 1 Introduction Although OLS is considered a linear estimator, it does not mean that the relationship between Y and X needs to be linear. In this
More informationEconometrics I KS. Module 2: Multivariate Linear Regression. Alexander Ahammer. This version: April 16, 2018
Econometrics I KS Module 2: Multivariate Linear Regression Alexander Ahammer Department of Economics Johannes Kepler University of Linz This version: April 16, 2018 Alexander Ahammer (JKU) Module 2: Multivariate
More informationApplied Statistics and Econometrics
Applied Statistics and Econometrics Lecture 6 Saul Lach September 2017 Saul Lach () Applied Statistics and Econometrics September 2017 1 / 53 Outline of Lecture 6 1 Omitted variable bias (SW 6.1) 2 Multiple
More informationQuantitative Analysis of Financial Markets. Summary of Part II. Key Concepts & Formulas. Christopher Ting. November 11, 2017
Summary of Part II Key Concepts & Formulas Christopher Ting November 11, 2017 christopherting@smu.edu.sg http://www.mysmu.edu/faculty/christophert/ Christopher Ting 1 of 16 Why Regression Analysis? Understand
More informationSimple Linear Regression Model & Introduction to. OLS Estimation
Inside ECOOMICS Introduction to Econometrics Simple Linear Regression Model & Introduction to Introduction OLS Estimation We are interested in a model that explains a variable y in terms of other variables
More informationFinancial Econometrics Lecture 6: Testing the CAPM model
Financial Econometrics Lecture 6: Testing the CAPM model Richard G. Pierse 1 Introduction The capital asset pricing model has some strong implications which are testable. The restrictions that can be tested
More information14 Multiple Linear Regression
B.Sc./Cert./M.Sc. Qualif. - Statistics: Theory and Practice 14 Multiple Linear Regression 14.1 The multiple linear regression model In simple linear regression, the response variable y is expressed in
More informationTesting Linear Restrictions: cont.
Testing Linear Restrictions: cont. The F-statistic is closely connected with the R of the regression. In fact, if we are testing q linear restriction, can write the F-stastic as F = (R u R r)=q ( R u)=(n
More informationIntermediate Econometrics
Intermediate Econometrics Markus Haas LMU München Summer term 2011 15. Mai 2011 The Simple Linear Regression Model Considering variables x and y in a specific population (e.g., years of education and wage
More informationLecture 1: OLS derivations and inference
Lecture 1: OLS derivations and inference Econometric Methods Warsaw School of Economics (1) OLS 1 / 43 Outline 1 Introduction Course information Econometrics: a reminder Preliminary data exploration 2
More informationCHAPTER 2: Assumptions and Properties of Ordinary Least Squares, and Inference in the Linear Regression Model
CHAPTER 2: Assumptions and Properties of Ordinary Least Squares, and Inference in the Linear Regression Model Prof. Alan Wan 1 / 57 Table of contents 1. Assumptions in the Linear Regression Model 2 / 57
More informationPractical Econometrics. for. Finance and Economics. (Econometrics 2)
Practical Econometrics for Finance and Economics (Econometrics 2) Seppo Pynnönen and Bernd Pape Department of Mathematics and Statistics, University of Vaasa 1. Introduction 1.1 Econometrics Econometrics
More informationIntermediate Econometrics
Intermediate Econometrics Heteroskedasticity Text: Wooldridge, 8 July 17, 2011 Heteroskedasticity Assumption of homoskedasticity, Var(u i x i1,..., x ik ) = E(u 2 i x i1,..., x ik ) = σ 2. That is, the
More informationThe multiple regression model; Indicator variables as regressors
The multiple regression model; Indicator variables as regressors Ragnar Nymoen University of Oslo 28 February 2013 1 / 21 This lecture (#12): Based on the econometric model specification from Lecture 9
More informationBusiness Statistics. Lecture 10: Correlation and Linear Regression
Business Statistics Lecture 10: Correlation and Linear Regression Scatterplot A scatterplot shows the relationship between two quantitative variables measured on the same individuals. It displays the Form
More informationModels, Testing, and Correction of Heteroskedasticity. James L. Powell Department of Economics University of California, Berkeley
Models, Testing, and Correction of Heteroskedasticity James L. Powell Department of Economics University of California, Berkeley Aitken s GLS and Weighted LS The Generalized Classical Regression Model
More information11 Hypothesis Testing
28 11 Hypothesis Testing 111 Introduction Suppose we want to test the hypothesis: H : A q p β p 1 q 1 In terms of the rows of A this can be written as a 1 a q β, ie a i β for each row of A (here a i denotes
More informationInference for the Regression Coefficient
Inference for the Regression Coefficient Recall, b 0 and b 1 are the estimates of the slope β 1 and intercept β 0 of population regression line. We can shows that b 0 and b 1 are the unbiased estimates
More informationEconometrics II - EXAM Answer each question in separate sheets in three hours
Econometrics II - EXAM Answer each question in separate sheets in three hours. Let u and u be jointly Gaussian and independent of z in all the equations. a Investigate the identification of the following
More informationEconometrics I Lecture 3: The Simple Linear Regression Model
Econometrics I Lecture 3: The Simple Linear Regression Model Mohammad Vesal Graduate School of Management and Economics Sharif University of Technology 44716 Fall 1397 1 / 32 Outline Introduction Estimating
More informationEconometrics Lecture 5: Limited Dependent Variable Models: Logit and Probit
Econometrics Lecture 5: Limited Dependent Variable Models: Logit and Probit R. G. Pierse 1 Introduction In lecture 5 of last semester s course, we looked at the reasons for including dichotomous variables
More informationECON Introductory Econometrics. Lecture 6: OLS with Multiple Regressors
ECON4150 - Introductory Econometrics Lecture 6: OLS with Multiple Regressors Monique de Haan (moniqued@econ.uio.no) Stock and Watson Chapter 6 Lecture outline 2 Violation of first Least Squares assumption
More informationLinear Models and Estimation by Least Squares
Linear Models and Estimation by Least Squares Jin-Lung Lin 1 Introduction Causal relation investigation lies in the heart of economics. Effect (Dependent variable) cause (Independent variable) Example:
More informationSimple Linear Regression: The Model
Simple Linear Regression: The Model task: quantifying the effect of change X in X on Y, with some constant β 1 : Y = β 1 X, linear relationship between X and Y, however, relationship subject to a random
More informationTopic 4: Model Specifications
Topic 4: Model Specifications Advanced Econometrics (I) Dong Chen School of Economics, Peking University 1 Functional Forms 1.1 Redefining Variables Change the unit of measurement of the variables will
More information405 ECONOMETRICS Chapter # 11: MULTICOLLINEARITY: WHAT HAPPENS IF THE REGRESSORS ARE CORRELATED? Domodar N. Gujarati
405 ECONOMETRICS Chapter # 11: MULTICOLLINEARITY: WHAT HAPPENS IF THE REGRESSORS ARE CORRELATED? Domodar N. Gujarati Prof. M. El-Sakka Dept of Economics Kuwait University In this chapter we take a critical
More informationMultiple Linear Regression
Multiple Linear Regression Asymptotics Asymptotics Multiple Linear Regression: Assumptions Assumption MLR. (Linearity in parameters) Assumption MLR. (Random Sampling from the population) We have a random
More informationFöreläsning /31
1/31 Föreläsning 10 090420 Chapter 13 Econometric Modeling: Model Speci cation and Diagnostic testing 2/31 Types of speci cation errors Consider the following models: Y i = β 1 + β 2 X i + β 3 X 2 i +
More information2.1. Consider the following production function, known in the literature as the transcendental production function (TPF).
CHAPTER Functional Forms of Regression Models.1. Consider the following production function, known in the literature as the transcendental production function (TPF). Q i B 1 L B i K i B 3 e B L B K 4 i
More informationMultiple Regression Analysis
Multiple Regression Analysis y = β 0 + β 1 x 1 + β 2 x 2 +... β k x k + u 2. Inference 0 Assumptions of the Classical Linear Model (CLM)! So far, we know: 1. The mean and variance of the OLS estimators
More informationFinal Exam - Solutions
Ecn 102 - Analysis of Economic Data University of California - Davis March 19, 2010 Instructor: John Parman Final Exam - Solutions You have until 5:30pm to complete this exam. Please remember to put your
More informationECONOMETRIC THEORY. MODULE VI Lecture 19 Regression Analysis Under Linear Restrictions
ECONOMETRIC THEORY MODULE VI Lecture 9 Regression Analysis Under Linear Restrictions Dr Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur One of the basic objectives
More informationEconometrics. Week 4. Fall Institute of Economic Studies Faculty of Social Sciences Charles University in Prague
Econometrics Week 4 Institute of Economic Studies Faculty of Social Sciences Charles University in Prague Fall 2012 1 / 23 Recommended Reading For the today Serial correlation and heteroskedasticity in
More informationAnalisi Statistica per le Imprese
, Analisi Statistica per le Imprese Dip. di Economia Politica e Statistica 4.3. 1 / 33 You should be able to:, Underst model building using multiple regression analysis Apply multiple regression analysis
More informationWISE MA/PhD Programs Econometrics Instructor: Brett Graham Spring Semester, Academic Year Exam Version: A
WISE MA/PhD Programs Econometrics Instructor: Brett Graham Spring Semester, 2015-16 Academic Year Exam Version: A INSTRUCTIONS TO STUDENTS 1 The time allowed for this examination paper is 2 hours. 2 This
More informationLinear models. Linear models are computationally convenient and remain widely used in. applied econometric research
Linear models Linear models are computationally convenient and remain widely used in applied econometric research Our main focus in these lectures will be on single equation linear models of the form y
More informationEmpirical Market Microstructure Analysis (EMMA)
Empirical Market Microstructure Analysis (EMMA) Lecture 3: Statistical Building Blocks and Econometric Basics Prof. Dr. Michael Stein michael.stein@vwl.uni-freiburg.de Albert-Ludwigs-University of Freiburg
More informationFormulary Applied Econometrics
Department of Economics Formulary Applied Econometrics c c Seminar of Statistics University of Fribourg Formulary Applied Econometrics 1 Rescaling With y = cy we have: ˆβ = cˆβ With x = Cx we have: ˆβ
More informationEconometrics Review questions for exam
Econometrics Review questions for exam Nathaniel Higgins nhiggins@jhu.edu, 1. Suppose you have a model: y = β 0 x 1 + u You propose the model above and then estimate the model using OLS to obtain: ŷ =
More informationINTRODUCTORY ECONOMETRICS
INTRODUCTORY ECONOMETRICS Lesson 2b Dr Javier Fernández etpfemaj@ehu.es Dpt. of Econometrics & Statistics UPV EHU c J Fernández (EA3-UPV/EHU), February 21, 2009 Introductory Econometrics - p. 1/192 GLRM:
More informationMa 3/103: Lecture 25 Linear Regression II: Hypothesis Testing and ANOVA
Ma 3/103: Lecture 25 Linear Regression II: Hypothesis Testing and ANOVA March 6, 2017 KC Border Linear Regression II March 6, 2017 1 / 44 1 OLS estimator 2 Restricted regression 3 Errors in variables 4
More informationthe error term could vary over the observations, in ways that are related
Heteroskedasticity We now consider the implications of relaxing the assumption that the conditional variance Var(u i x i ) = σ 2 is common to all observations i = 1,..., n In many applications, we may
More informationEconometrics Honor s Exam Review Session. Spring 2012 Eunice Han
Econometrics Honor s Exam Review Session Spring 2012 Eunice Han Topics 1. OLS The Assumptions Omitted Variable Bias Conditional Mean Independence Hypothesis Testing and Confidence Intervals Homoskedasticity
More information2. Linear regression with multiple regressors
2. Linear regression with multiple regressors Aim of this section: Introduction of the multiple regression model OLS estimation in multiple regression Measures-of-fit in multiple regression Assumptions
More informationIntroduction to Estimation Methods for Time Series models. Lecture 1
Introduction to Estimation Methods for Time Series models Lecture 1 Fulvio Corsi SNS Pisa Fulvio Corsi Introduction to Estimation () Methods for Time Series models Lecture 1 SNS Pisa 1 / 19 Estimation
More informationProbability and Statistics Notes
Probability and Statistics Notes Chapter Seven Jesse Crawford Department of Mathematics Tarleton State University Spring 2011 (Tarleton State University) Chapter Seven Notes Spring 2011 1 / 42 Outline
More informationWISE MA/PhD Programs Econometrics Instructor: Brett Graham Spring Semester, Academic Year Exam Version: A
WISE MA/PhD Programs Econometrics Instructor: Brett Graham Spring Semester, 2016-17 Academic Year Exam Version: A INSTRUCTIONS TO STUDENTS 1 The time allowed for this examination paper is 2 hours. 2 This
More informationMultiple Regression Analysis: The Problem of Inference
Multiple Regression Analysis: The Problem of Inference Jamie Monogan University of Georgia Intermediate Political Methodology Jamie Monogan (UGA) Multiple Regression Analysis: Inference POLS 7014 1 / 10
More informationECON 4230 Intermediate Econometric Theory Exam
ECON 4230 Intermediate Econometric Theory Exam Multiple Choice (20 pts). Circle the best answer. 1. The Classical assumption of mean zero errors is satisfied if the regression model a) is linear in the
More informationStatistics II. Management Degree Management Statistics IIDegree. Statistics II. 2 nd Sem. 2013/2014. Management Degree. Simple Linear Regression
Model 1 2 Ordinary Least Squares 3 4 Non-linearities 5 of the coefficients and their to the model We saw that econometrics studies E (Y x). More generally, we shall study regression analysis. : The regression
More informationChapter 14 Student Lecture Notes 14-1
Chapter 14 Student Lecture Notes 14-1 Business Statistics: A Decision-Making Approach 6 th Edition Chapter 14 Multiple Regression Analysis and Model Building Chap 14-1 Chapter Goals After completing this
More informationProblems. Suppose both models are fitted to the same data. Show that SS Res, A SS Res, B
Simple Linear Regression 35 Problems 1 Consider a set of data (x i, y i ), i =1, 2,,n, and the following two regression models: y i = β 0 + β 1 x i + ε, (i =1, 2,,n), Model A y i = γ 0 + γ 1 x i + γ 2
More informationSummer School in Statistics for Astronomers V June 1 - June 6, Regression. Mosuk Chow Statistics Department Penn State University.
Summer School in Statistics for Astronomers V June 1 - June 6, 2009 Regression Mosuk Chow Statistics Department Penn State University. Adapted from notes prepared by RL Karandikar Mean and variance Recall
More informationThe OLS Estimation of a basic gravity model. Dr. Selim Raihan Executive Director, SANEM Professor, Department of Economics, University of Dhaka
The OLS Estimation of a basic gravity model Dr. Selim Raihan Executive Director, SANEM Professor, Department of Economics, University of Dhaka Contents I. Regression Analysis II. Ordinary Least Square
More informationRegression Analysis II
Regression Analysis II Measures of Goodness of fit Two measures of Goodness of fit Measure of the absolute fit of the sample points to the sample regression line Standard error of the estimate An index
More informationMultiple Regression Analysis. Part III. Multiple Regression Analysis
Part III Multiple Regression Analysis As of Sep 26, 2017 1 Multiple Regression Analysis Estimation Matrix form Goodness-of-Fit R-square Adjusted R-square Expected values of the OLS estimators Irrelevant
More informationLecture 3: Linear Models. Bruce Walsh lecture notes Uppsala EQG course version 28 Jan 2012
Lecture 3: Linear Models Bruce Walsh lecture notes Uppsala EQG course version 28 Jan 2012 1 Quick Review of the Major Points The general linear model can be written as y = X! + e y = vector of observed
More informationWEIGHTED LEAST SQUARES. Model Assumptions for Weighted Least Squares: Recall: We can fit least squares estimates just assuming a linear mean function.
1 2 WEIGHTED LEAST SQUARES Recall: We can fit least squares estimates just assuming a linear mean function. Without the constant variance assumption, we can still conclude that the coefficient estimators
More informationLecture 4: Heteroskedasticity
Lecture 4: Heteroskedasticity Econometric Methods Warsaw School of Economics (4) Heteroskedasticity 1 / 24 Outline 1 What is heteroskedasticity? 2 Testing for heteroskedasticity White Goldfeld-Quandt Breusch-Pagan
More informationstatistical sense, from the distributions of the xs. The model may now be generalized to the case of k regressors:
Wooldridge, Introductory Econometrics, d ed. Chapter 3: Multiple regression analysis: Estimation In multiple regression analysis, we extend the simple (two-variable) regression model to consider the possibility
More information