2. Linear regression with multiple regressors


 Silvester Higgins
 1 years ago
 Views:
Transcription
1 2. Linear regression with multiple regressors Aim of this section: Introduction of the multiple regression model OLS estimation in multiple regression Measuresoffit in multiple regression Assumptions in the multiple regression model Violations of the assumptions (omittedvariable bias, multicollinearity, heteroskedasticity, autocorrelation) 5
2 2.1. The multiple regression model Intuition: A regression model specifies a functional (parametric) relationship between a dependent (endogenous) variable Y and a set of k independent (exogenous) regressors X 1, X 2,..., X k In a first step, we consider the linear multiple regression model 6
3 Definition 2.1: (Multiple linear regression model) The multiple (linear) regression model is given by Y i = β 0 + β 1 X 1i + β 2 X 2i β k X ki + u i, (2.1) i = 1,..., n, where Y i is the i th observation on the dependent variable, X 1i, X 2i,..., X ki are the i th regressors, u i is the stochastic error term. observations on each of the k The population regression line is the relationship that holds between Y and the X s on average: E(Y i X 1i = x 1, X 2i = x 2,..., X ki = x k ) = β 0 +β 1 x β k x k. 7
4 Meaning of the coefficients: The intercept β 0 is the expected value of Y i (for all i = 1,..., n) when all Xregressors equal 0 β 1,..., β k are the slope coefficients on the respective regressors X 1,..., X k β 1, for example, is the expected change in Y i resulting from changing X 1i by one unit, holding constant X 2i,..., X ki (and analogously β 2,..., β k ) Definition 2.2: (Homoskedasticity, Heteroskedasticity) The error term u i is called homoskedastic if the conditional variance of u i given X 1i,..., X ki, Var(u i X 1i,..., X ki ), is constant for i = 1,..., n and does not depend on the values of X 1i,..., X ki. Otherwise, the error term is called heteroskedastic. 8
5 Example 1: (Student performance) Regression of student performance (Y ) in n = 420 USdistricts on distinct school characteristics (factors) Y i : average test score in the i th district (TEST SCORE) X 1i : average class size in the i th district (measured by the studentteacher ratio, STR) X 2i : percentage of English learners in the i th district (PCTEL) Expected signs of the coefficients: β 1 < 0 β 2 < 0 9
6 Example 2: (House prices) Regression of house prices (Y ) recorded for n = 546 houses sold in Windsor (Canada) on distinct housing characteristics Y i : sale price (in Canadian dollars) of the i th house (SALEPRICE) X 1i : lot size (in square feet) of the i th property (LOTSIZE) X 2i : number of bedrooms in the i th house (BEDROOMS) X 3i : number of bathrooms in the i th house (BATHROOMS) X 4i : number of storeys (excluding the basement) in the i th house (STOREYS) Expected signs of the coefficients: β 1, β 2, β 3, β 4 > 0 10
7 2.2. The OLS estimator in multiple regression Now: Estimation of the coefficients β 0, β 1,..., β k in the multiple regression model on the basis of n observations by applying the Ordinary Least Squares (OLS) technique Idea: Let b 0, b 1,..., b k be estimators of β 0, β 1,..., β k We can predict Y i by b 0 + b 1 X 1i b k X ki The prediction error is Y i b 0 b 1 X 1i... b k X ki 11
8 Idea: [continued] The sum of the squared prediction errors over all n observations is n i=1 (Y i b 0 b 1 X 1i... b k X ki ) 2 (2.2) Definition 2.3: (OLS estimators, predicted values, residuals) The OLS estimators ˆβ 0, ˆβ 1,..., ˆβ k are the values of b 0, b 1,..., b k that minimize the sum of squared prediction errors (2.2). The OLS predicted values Ŷ i and residuals û i (for i = 1,..., n) are and Ŷ i = ˆβ 0 + ˆβ 1 X 1i ˆβ k X ki (2.3) û i = Y i Ŷ i. (2.4) 12
9 Remarks: The OLS estimators ˆβ 0, ˆβ 1,..., ˆβ k and the residuals û i are computed from a sample of n observations of (X 1i,..., X ki, Y i ) for i = 1,..., n They are estimators of the unknown true population coefficients β 0, β 1,..., β k and u i There are closedform formulas for calculating the OLS estimates from the data (see the lectures Econometrics I+II) In this lecture, we use the softwarepackage EViews 13
10 Regression estimation results (EViews) for the studentperformance dataset Dependent Variable: TEST_SCORE Method: Least Squares Date: 07/02/12 Time: 16:29 Sample: Included observations: 420 Variable Coefficient Std. Error tstatistic Prob. C STR PCTEL Rsquared Mean dependent var Adjusted Rsquared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood HannanQuinn criter Fstatistic DurbinWatson stat Prob(Fstatistic)
11 Predicted values Ŷ i and residuals û i for the studentperformance dataset Residual Actual Fitted 15
12 Regression estimation results (EViews) for the houseprices dataset Dependent Variable: SALEPRICE Method: Least Squares Date: 07/02/12 Time: 16:50 Sample: Included observations: 546 Variable Coefficient Std. Error tstatistic Prob. C LOTSIZE BEDROOMS BATHROOMS STOREYS Rsquared Mean dependent var Adjusted Rsquared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid 1.80E+11 Schwarz criterion Log likelihood HannanQuinn criter Fstatistic DurbinWatson stat Prob(Fstatistic)
13 Predicted values Ŷ i and residuals û i for the houseprices dataset 200, , , ,000 80,000 40,000 80,000 40, ,00080, Residual Actual Fitted 17
14 OLS assumptions in the multiple regression model (2.1): 1. u i has conditional mean zero given X 1i, X 2i,..., X ki : E(u i X 1i, X 2i,..., X ki ) = 0 2. (X 1i, X 2i,..., X ki, Y i ), i = 1,..., n, are independently and identically distributed (i.i.d.) draws from their joint distribution 3. Large outliers are unlikely: X 1i, X 2i,..., X ki and Y i have nonzero finite fourth moments 4. There is no perfect multicollinearity Remarks: Note that we do not assume any specific parametric distribution for the u i The OLS assumptions imply specific distribution results 18
15 Theorem 2.4: (Unbiasedness, consistency, normality) Given the OLS assumptions the following properties of the OLS estimators ˆβ 0, ˆβ 1,..., ˆβ k hold: 1. ˆβ 0, ˆβ 1,..., ˆβ k are unbiased estimators of β 0,..., β k. 2. ˆβ 0, ˆβ 1,..., ˆβ k are consistent estimators of β 0,..., β k. (Convergence in probability) 3. In large samples ˆβ 0, ˆβ 1,..., ˆβ k are jointly normally distributed and each single OLS estimator ˆβ j, j = 0,..., k, is normally distributed with mean β j and variance σ 2ˆβ j, that is ˆβ j N(β j, σ 2ˆβ j ). 19
16 Remarks: In general, the OLS estimators are correlated This correlation among ˆβ 0, ˆβ 1,..., ˆβ k arises from the correlation among the regressors X 1,..., X k The sampling distribution of the OLS estimators will become relevant in Section 3 (hypothesistesting, confidence intervals) 20
17 2.3. Measuresoffit in multiple regression Now: Three wellknown summary statistics that measure how well the OLS estimates fit the data Standard error of regression (SER): The SER estimates the standard deviation of the error term u i (under the assumption of homoskedasticity): SER = 1 n k 1 n û 2 i i=1 21
18 Standard error of regression: [continued] We denote the sum of squared residuals by SSR n i=1 û 2 i so that SER = SSR n k 1 Given the OLS assumptions and homoskedasticity the squared SER, (SER) 2, is an unbiased estimator of the unknown constant variance of the u i SER is a measure of the spread of the distribution of Y i around the population regression line Both measures, SER and SSR, are reported in the EViews regression output 22
19 R 2 : The R 2 is the fraction of the sample variance of the Y i explained by the regressors Equivalently, the R 2 is 1 minus the fraction of the variance of the Y i not explained by the regressors (i.e. explained by the residuals) Denoting the explained sum of squares (ESS) and the total sum of squares (TSS) by ESS = n i=1 (Ŷ i Ȳ ) 2 and TSS = respectively, we define the R 2 as R 2 = ESS TSS = 1 SSR TSS n i=1 (Y i Ȳ ) 2, 23
20 R 2 : [continued] In multiple regression, the R 2 increases whenever an additional regressor X k+1 is added to the regression model, unless the estimated coefficient ˆβ k+1 is exactly equal to zero Since in practice it is extremely unusual to have exactly ˆβ k+1 = 0, the R 2 generally increases (and never decreases) when an new regressor is added to the regression model An increase in the R 2 due to the inclusion of a new regressor does not necessarily indicate an actually improved fit of the model 24
21 Adjusted R 2 : The adjusted R 2 (in symbols: R 2 ), deflates the conventional R 2 : R 2 = 1 n 1 SSR n k 1TSS It is always true that R 2 < R 2 (why?) When adding a new regressor X k+1 to the model, the R 2 can increase or decrease (why?) The R 2 can be negative (why?) 25
22 2.4. Omittedvariable bias Now: Discussion of a phenomenon that implies violation of the first OLS assumption on Slide 18 This issue is known under the phrasing omittedvariable bias and is extremely relevant in practice Although theoretically easy to grasp, avoiding this specification problem turns out to be a nontrivial task in many empirical applications 26
23 Definition 2.5: (Omittedvariable bias) Consider the multiple regression model in Definition 2.1 on Slide 7. Omittedvariable bias is the bias in the OLS estimator ˆβ j of the coefficient β j (for j = 1,..., k) that arises when the associated regressor X j is correlated with an omitted variable. More precisely, for omittedvariable bias to occur, the following two conditions must hold: 1. X j is correlated with the omitted variable. 2. The omitted variable is a determinant of the dependent variable Y. 27
24 Example: Consider the houseprices dataset (Slides 16, 17) Using the entire set of regressors, we obtain the OLS estimate ˆβ 2 = for the BEDROOMScoefficient The correlation coefficients between the regressors are as follows: BEDROOMS BATHROOMS LOTSIZE STOREYS BEDROOMS BATHROOMS LOTSIZE STOREYS
25 Example: [continued] There is positive (significant) correlation between the variable BEDROOMS and all other regressors Excluding the other variables from the regression yields the following OLSestimates: Dependent Variable: SALEPRICE Method: Least Squares Date: 14/02/12 Time: 16:10 Sample: Included observations: 546 Variable Coefficient Std. Error tstatistic Prob. C BEDROOMS Rsquared Mean dependent var Adjusted Rsquared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid 3.36E+11 Schwarz criterion Log likelihood HannanQuinn criter Fstatistic DurbinWatson stat Prob(Fstatistic) The alternative OLSestimates of the BEDROOMScoefficient differ substantially 29
26 Intuitive explanation of the omittedvariable bias: Consider the variable LOTSIZE as omitted LOTSIZE is an important variable for explaining SALEPRICE If we omit LOTSIZE in the regression, it will try to enter in the only way it can, namely through its positive correlation with the included variable BEDROOMS The coefficient on BEDROOMS will confound the effect of BED ROOMS and LOTSIZE on SALEPRICE 30
27 More formal explanation: Omittedvariable bias means that the first OLS assumption on Slide 18 is violated Reasoning: In the multiple regression model the error term u i represents all factors other than the included regressors X 1,..., X k that are determinants of Y i If an omitted variable is correlated with at least one of the included regressors X 1,..., X k, then u i (which contains this factor) is correlated with the set of regressors This implies that E(u i X 1i,..., X ki ) 0 31
28 Important result: In the case of omittedvariable bias the OLS estimators on the corresponding included regressors are biased in finite samples this bias does not vanish in large samples the OLS estimators are inconsistent Solutions to omittedvariable bias: To be discussed in Section 5 32
29 2.5. Multicollinearity Definition 2.6: (Perfect multicollinearity) Consider the multiple regression model in Definition 2.1 on Slide 7. The regressors X 1,..., X k are said to be perfectly multicollinear if one of the regressors is a perfect linear function of the other regressors. Remarks: Under perfect multicollinearity the OLS estimates cannot be calculated due to division by zero in the OLS formulas Perfect multicollinearity often reflects a logical mistake in choosing the regressors or some unrecognized feature in the data set 33
30 Example: (Dummy variable trap) Consider the studentperformance dataset Suppose we partition the school districts into the 3 categories (1) rural, (2) suburban, (3) urban We represent the categories by the dummy regressors { 1 if district i is rural RURAL i = 0 otherwise and by SUBURBAN i and URBAN i analogously defined Since each district belongs to one and only one category, we have for each district i: RURAL i + SUBURBAN i + URBAN i = 1 34
31 Example: [continued] Now, let us define the constant regressor X 0 associated with the intercept coefficient β 0 in the multiple regression model on Slide 7 by X 0i 1 for i = 1,... n Then, for i = 1,..., n, the following relationship holds among the regressors: Perfect multicollinearity X 0i = RURAL i + SUBURBAN i + URBAN i To estimate the regression we must exclude either one of the dummy regressors or the constant regressor X 0 (the intercept β 0 ) from the regression 35
32 Theorem 2.7: (Dummy variable trap) Let there be G different categories in the data set represented by G dummy regressors. If 1. each observation i falls into one and only one category, 2. there is an intercept (constant regressor) in the regression, 3. all G dummy regressors are included as regressors, then regression estimation fails because of perfect multicollinearity. Usual remedy: Exclude one of the dummy regressors (G 1 dummy regressors are sufficient) 36
33 Definition 2.8: (Imperfect multicollinearity) Consider the multiple regression model in Definition 2.1 on Slide 7. The regressors X 1,..., X k are said to be imperfectly multicollinear if two or more of the regressors are highly correlated in the sense that there is a linear function of the regressors that is highly correlated with another regressor. Remarks: Imperfect multicollinearity does not pose any (numeric) problems in calculating OLS estimates However, if regressors are imperfectly multicollinear, then the coefficients on at least one individual regressor will be imprecisely estimated 37
34 Remarks: [continued] Techniques for identifying and mitigating imperfect multicollinearity are presented in econometric textbooks (e.g. Hill et al., 2010, pp ) 38
Econometrics Simple Linear Regression
Econometrics Simple Linear Regression Burcu Eke UC3M Linear equations with one variable Recall what a linear equation is: y = b 0 + b 1 x is a linear equation with one variable, or equivalently, a straight
More informationThe Simple Linear Regression Model: Specification and Estimation
Chapter 3 The Simple Linear Regression Model: Specification and Estimation 3.1 An Economic Model Suppose that we are interested in studying the relationship between household income and expenditure on
More informationNonlinear Regression Functions. SW Ch 8 1/54/
Nonlinear Regression Functions SW Ch 8 1/54/ The TestScore STR relation looks linear (maybe) SW Ch 8 2/54/ But the TestScore Income relation looks nonlinear... SW Ch 8 3/54/ Nonlinear Regression General
More informationHeteroskedasticity and Weighted Least Squares
Econ 507. Econometric Analysis. Spring 2009 April 14, 2009 The Classical Linear Model: 1 Linearity: Y = Xβ + u. 2 Strict exogeneity: E(u) = 0 3 No Multicollinearity: ρ(x) = K. 4 No heteroskedasticity/
More informationForecasting the US Dollar / Euro Exchange rate Using ARMA Models
Forecasting the US Dollar / Euro Exchange rate Using ARMA Models LIUWEI (9906360)  1  ABSTRACT...3 1. INTRODUCTION...4 2. DATA ANALYSIS...5 2.1 Stationary estimation...5 2.2 DickeyFuller Test...6 3.
More informationOverview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model
Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model 1 September 004 A. Introduction and assumptions The classical normal linear regression model can be written
More informationBivariate Regression Analysis. The beginning of many types of regression
Bivariate Regression Analysis The beginning of many types of regression TOPICS Beyond Correlation Forecasting Two points to estimate the slope Meeting the BLUE criterion The OLS method Purpose of Regression
More informationIAPRI Quantitative Analysis Capacity Building Series. Multiple regression analysis & interpreting results
IAPRI Quantitative Analysis Capacity Building Series Multiple regression analysis & interpreting results How important is Rsquared? Rsquared Published in Agricultural Economics 0.45 Best article of the
More informationMULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS
MULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS MSR = Mean Regression Sum of Squares MSE = Mean Squared Error RSS = Regression Sum of Squares SSE = Sum of Squared Errors/Residuals α = Level of Significance
More informationChapter 5: Basic Statistics and Hypothesis Testing
Chapter 5: Basic Statistics and Hypothesis Testing In this chapter: 1. Viewing the tvalue from an OLS regression (UE 5.2.1) 2. Calculating critical tvalues and applying the decision rule (UE 5.2.2) 3.
More informationRegression Analysis Prof. Soumen Maity Department of Mathematics Indian Institute of Technology, Kharagpur. Lecture  2 Simple Linear Regression
Regression Analysis Prof. Soumen Maity Department of Mathematics Indian Institute of Technology, Kharagpur Lecture  2 Simple Linear Regression Hi, this is my second lecture in module one and on simple
More informationEconometrics The Multiple Regression Model: Inference
Econometrics The Multiple Regression Model: João Valle e Azevedo Faculdade de Economia Universidade Nova de Lisboa Spring Semester João Valle e Azevedo (FEUNL) Econometrics Lisbon, March 2011 1 / 24 in
More informationSimple Linear Regression Inference
Simple Linear Regression Inference 1 Inference requirements The Normality assumption of the stochastic term e is needed for inference even if it is not a OLS requirement. Therefore we have: Interpretation
More informationWooldridge, Introductory Econometrics, 4th ed. Chapter 15: Instrumental variables and two stage least squares
Wooldridge, Introductory Econometrics, 4th ed. Chapter 15: Instrumental variables and two stage least squares Many economic models involve endogeneity: that is, a theoretical relationship does not fit
More informationEviews Tutorial. File New Workfile. Start observation End observation Annual
APS 425 Professor G. William Schwert Advanced Managerial Data Analysis CS3110L, 5852752470 Fax: 5854615475 email: schwert@schwert.ssb.rochester.edu Eviews Tutorial 1. Creating a Workfile: First you
More informationEcon 371 Problem Set #3 Answer Sheet
Econ 371 Problem Set #3 Answer Sheet 4.1 In this question, you are told that a OLS regression analysis of third grade test scores as a function of class size yields the following estimated model. T estscore
More informationLinear Regression with One Regressor
Linear Regression with One Regressor Michael Ash Lecture 10 Analogy to the Mean True parameter µ Y β 0 and β 1 Meaning Central tendency Intercept and slope E(Y ) E(Y X ) = β 0 + β 1 X Data Y i (X i, Y
More informationChapter 12: Time Series Models
Chapter 12: Time Series Models In this chapter: 1. Estimating ad hoc distributed lag & Koyck distributed lag models (UE 12.1.3) 2. Testing for serial correlation in Koyck distributed lag models (UE 12.2.2)
More informationRegression Analysis: Basic Concepts
The simple linear model Regression Analysis: Basic Concepts Allin Cottrell Represents the dependent variable, y i, as a linear function of one independent variable, x i, subject to a random disturbance
More information2. What are the theoretical and practical consequences of autocorrelation?
Lecture 10 Serial Correlation In this lecture, you will learn the following: 1. What is the nature of autocorrelation? 2. What are the theoretical and practical consequences of autocorrelation? 3. Since
More informationAir passenger departures forecast models A technical note
Ministry of Transport Air passenger departures forecast models A technical note By Haobo Wang Financial, Economic and Statistical Analysis Page 1 of 15 1. Introduction Sine 1999, the Ministry of Business,
More informationMultiple Linear Regression in Data Mining
Multiple Linear Regression in Data Mining Contents 2.1. A Review of Multiple Linear Regression 2.2. Illustration of the Regression Process 2.3. Subset Selection in Linear Regression 1 2 Chap. 2 Multiple
More information2. Pooled Cross Sections and Panels. 2.1 Pooled Cross Sections versus Panel Data
2. Pooled Cross Sections and Panels 2.1 Pooled Cross Sections versus Panel Data Pooled Cross Sections are obtained by collecting random samples from a large polulation independently of each other at different
More informationDEPARTMENT OF ECONOMICS. Unit ECON 12122 Introduction to Econometrics. Notes 4 2. R and F tests
DEPARTMENT OF ECONOMICS Unit ECON 11 Introduction to Econometrics Notes 4 R and F tests These notes provide a summary of the lectures. They are not a complete account of the unit material. You should also
More informationVariance of OLS Estimators and Hypothesis Testing. Randomness in the model. GM assumptions. Notes. Notes. Notes. Charlie Gibbons ARE 212.
Variance of OLS Estimators and Hypothesis Testing Charlie Gibbons ARE 212 Spring 2011 Randomness in the model Considering the model what is random? Y = X β + ɛ, β is a parameter and not random, X may be
More informationThe relationship between stock market parameters and interbank lending market: an empirical evidence
Magomet Yandiev Associate Professor, Department of Economics, Lomonosov Moscow State University mag2097@mail.ru Alexander Pakhalov, PG student, Department of Economics, Lomonosov Moscow State University
More informationInstrumental Variables & 2SLS
Instrumental Variables & 2SLS y 1 = β 0 + β 1 y 2 + β 2 z 1 +... β k z k + u y 2 = π 0 + π 1 z k+1 + π 2 z 1 +... π k z k + v Economics 20  Prof. Schuetze 1 Why Use Instrumental Variables? Instrumental
More informationSimple linear regression
Simple linear regression Introduction Simple linear regression is a statistical method for obtaining a formula to predict values of one variable from another where there is a causal relationship between
More informationSimple Linear Regression Chapter 11
Simple Linear Regression Chapter 11 Rationale Frequently decisionmaking situations require modeling of relationships among business variables. For instance, the amount of sale of a product may be related
More informationECON 142 SKETCH OF SOLUTIONS FOR APPLIED EXERCISE #2
University of California, Berkeley Prof. Ken Chay Department of Economics Fall Semester, 005 ECON 14 SKETCH OF SOLUTIONS FOR APPLIED EXERCISE # Question 1: a. Below are the scatter plots of hourly wages
More informationEcon 371 Problem Set #4 Answer Sheet. P rice = (0.485)BDR + (23.4)Bath + (0.156)Hsize + (0.002)LSize + (0.090)Age (48.
Econ 371 Problem Set #4 Answer Sheet 6.5 This question focuses on what s called a hedonic regression model; i.e., where the sales price of the home is regressed on the various attributes of the home. The
More informationRegression analysis in practice with GRETL
Regression analysis in practice with GRETL Prerequisites You will need the GNU econometrics software GRETL installed on your computer (http://gretl.sourceforge.net/), together with the sample files that
More informationCALCULATIONS & STATISTICS
CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 15 scale to 0100 scores When you look at your report, you will notice that the scores are reported on a 0100 scale, even though respondents
More informationInstrumental Variables Regression. Instrumental Variables (IV) estimation is used when the model has endogenous s.
Instrumental Variables Regression Instrumental Variables (IV) estimation is used when the model has endogenous s. IV can thus be used to address the following important threats to internal validity: Omitted
More informationOn the Degree of Openness of an Open Economy Carlos Alfredo Rodriguez, Universidad del CEMA Buenos Aires, Argentina
On the Degree of Openness of an Open Economy Carlos Alfredo Rodriguez, Universidad del CEMA Buenos Aires, Argentina car@cema.edu.ar www.cema.edu.ar\~car Version1February 14,2000 All data can be consulted
More informationSYSTEMS OF REGRESSION EQUATIONS
SYSTEMS OF REGRESSION EQUATIONS 1. MULTIPLE EQUATIONS y nt = x nt n + u nt, n = 1,...,N, t = 1,...,T, x nt is 1 k, and n is k 1. This is a version of the standard regression model where the observations
More informationInstrumental Variables & 2SLS
Instrumental Variables & 2SLS y 1 = β 0 + β 1 y 2 + β 2 z 1 +... β k z k + u y 2 = π 0 + π 1 z k+1 + π 2 z 1 +... π k z k + v Economics 20  Prof. Schuetze 1 Why Use Instrumental Variables? Instrumental
More informationUK GDP is the best predictor of UK GDP, literally.
UK GDP IS THE BEST PREDICTOR OF UK GDP, LITERALLY ERIK BRITTON AND DANNY GABAY 6 NOVEMBER 2009 UK GDP is the best predictor of UK GDP, literally. The ONS s preliminary estimate of UK GDP for the third
More informationEconometrics Regression Analysis with Time Series Data
Econometrics Regression Analysis with Time Series Data João Valle e Azevedo Faculdade de Economia Universidade Nova de Lisboa Spring Semester João Valle e Azevedo (FEUNL) Econometrics Lisbon, May 2011
More informationNCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )
Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates
More informationStatistics II Final Exam  January Use the University stationery to give your answers to the following questions.
Statistics II Final Exam  January 2012 Use the University stationery to give your answers to the following questions. Do not forget to write down your name and class group in each page. Indicate clearly
More informationIMPACT OF WORKING CAPITAL MANAGEMENT ON PROFITABILITY
IMPACT OF WORKING CAPITAL MANAGEMENT ON PROFITABILITY Hina Agha, Mba, Mphil Bahria University Karachi Campus, Pakistan Abstract The main purpose of this study is to empirically test the impact of working
More informationSolución del Examen Tipo: 1
Solución del Examen Tipo: 1 Universidad Carlos III de Madrid ECONOMETRICS Academic year 2009/10 FINAL EXAM May 17, 2010 DURATION: 2 HOURS 1. Assume that model (III) verifies the assumptions of the classical
More informationLecture 18 Linear Regression
Lecture 18 Statistics Unit Andrew Nunekpeku / Charles Jackson Fall 2011 Outline 1 1 Situation  used to model quantitative dependent variable using linear function of quantitative predictor(s). Situation
More informationAstate implements tough new penalties on drunk drivers; what is the effect
CHAPTER 4 Linear Regression with One Regressor Astate implements tough new penalties on drunk drivers; what is the effect on highway fatalities? A school district cuts the size of its elementary school
More informationCONSOLIDATED EDISON COMPANY OF NEW YORK, INC. VOLUME FORECASTING MODELS. Variable Coefficient Std. Error tstatistic Prob.
PAGE 1 OF 6 SC 1 (RESIDENTIAL AND RELIGIOUS) Dependent Variable: DLOG(GWH17/BDA0,0,4) Convergence achieved after 16 iterations MA Backcast: 1987Q2 C 0.011618 0.003667 3.168199 0.002100 DLOG(PRICE17S(3),0,4)
More informationTHE CORRELATION BETWEEN UNEMPLOYMENT AND REAL GDP GROWTH. A STUDY CASE ON ROMANIA
THE CORRELATION BETWEEN UNEMPLOYMENT AND REAL GDP GROWTH. A STUDY CASE ON ROMANIA Dumitrescu Bogdan Andrei The Academy of Economic Studies Faculty of Finance, Insurance, Banking and Stock Exchange 6, Romana
More informationSection 14 Simple Linear Regression: Introduction to Least Squares Regression
Slide 1 Section 14 Simple Linear Regression: Introduction to Least Squares Regression There are several different measures of statistical association used for understanding the quantitative relationship
More informationWeather Normalization of MISO Historical Data Procedure
Weather Normalization of MISO Historical Data Procedure Goal The goal of this weather normalization work was to provide a preliminary methodology for weather normalization as MISO does not currently have
More informatione = random error, assumed to be normally distributed with mean 0 and standard deviation σ
1 Linear Regression 1.1 Simple Linear Regression Model The linear regression model is applied if we want to model a numeric response variable and its dependency on at least one numeric factor variable.
More information5. Multiple regression
5. Multiple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/5 QBUS6840 Predictive Analytics 5. Multiple regression 2/39 Outline Introduction to multiple linear regression Some useful
More informationEcon 371 Problem Set #3 Answer Sheet
Econ 371 Problem Set #3 Answer Sheet 4.3 In this question, you are told that a OLS regression analysis of average weekly earnings yields the following estimated model. AW E = 696.7 + 9.6 Age, R 2 = 0.023,
More informationThe Impact of Privatization in Insurance Industry on Insurance Efficiency in Iran
The Impact of Privatization in Insurance Industry on Insurance Efficiency in Iran Shahram Gilaninia 1, Hosein Ganjinia, Azadeh Asadian 3 * 1. Department of Industrial Management, Islamic Azad University,
More informationwhere b is the slope of the line and a is the intercept i.e. where the line cuts the y axis.
Least Squares Introduction We have mentioned that one should not always conclude that because two variables are correlated that one variable is causing the other to behave a certain way. However, sometimes
More informationOLS in Matrix Form. Let y be an n 1 vector of observations on the dependent variable.
OLS in Matrix Form 1 The True Model Let X be an n k matrix where we have observations on k independent variables for n observations Since our model will usually contain a constant term, one of the columns
More informationLecture 2: Simple Linear Regression
DMBA: Statistics Lecture 2: Simple Linear Regression Least Squares, SLR properties, Inference, and Forecasting Carlos Carvalho The University of Texas McCombs School of Business mccombs.utexas.edu/faculty/carlos.carvalho/teaching
More informationRegression Analysis (Spring, 2000)
Regression Analysis (Spring, 2000) By Wonjae Purposes: a. Explaining the relationship between Y and X variables with a model (Explain a variable Y in terms of Xs) b. Estimating and testing the intensity
More informationThe Effect of Seasonality in the CPI on Indexed Bond Pricing and Inflation Expectations
The Effect of Seasonality in the CPI on Indexed Bond Pricing and Inflation Expectations Roy Stein* *Research Department, Roy Stein roy.stein@boi.org.il, tel: 026552559 This research was partially supported
More informationEconometric Principles and Data Analysis
Econometric Principles and Data Analysis product: 4339 course code: c230 c330 Econometric Principles and Data Analysis Centre for Financial and Management Studies SOAS, University of London 1999, revised
More informationCompetition as an Effective Tool in Developing Social Marketing Programs: Driving Behavior Change through Online Activities
Competition as an Effective Tool in Developing Social Marketing Programs: Driving Behavior Change through Online Activities Corina ŞERBAN 1 ABSTRACT Nowadays, social marketing practices represent an important
More informationModule 5: Multiple Regression Analysis
Using Statistical Data Using to Make Statistical Decisions: Data Multiple to Make Regression Decisions Analysis Page 1 Module 5: Multiple Regression Analysis Tom Ilvento, University of Delaware, College
More informationSELFTEST: SIMPLE REGRESSION
ECO 22000 McRAE SELFTEST: SIMPLE REGRESSION Note: Those questions indicated with an (N) are unlikely to appear in this form on an inclass examination, but you should be able to describe the procedures
More informationIntroduction to Regression and Data Analysis
Statlab Workshop Introduction to Regression and Data Analysis with Dan Campbell and Sherlock Campbell October 28, 2008 I. The basics A. Types of variables Your variables may take several forms, and it
More informationHYPOTHESIS TESTING: CONFIDENCE INTERVALS, TTESTS, ANOVAS, AND REGRESSION
HYPOTHESIS TESTING: CONFIDENCE INTERVALS, TTESTS, ANOVAS, AND REGRESSION HOD 2990 10 November 2010 Lecture Background This is a lightning speed summary of introductory statistical methods for senior undergraduate
More informationMACRO ECONOMIC PATTERNS AND STORIES. Is Your Job Cyclical?
Is Your Job at Risk? Page 1 of 8 Is Your Job Cyclical? Accessing the website of the Bureau of Labor Statistics Finding out about the ups and downs of your job Total Nonfarm Employment is illustrated in
More informationThe Multiple Regression Model: Hypothesis Tests and the Use of Nonsample Information
Chapter 8 The Multiple Regression Model: Hypothesis Tests and the Use of Nonsample Information An important new development that we encounter in this chapter is using the F distribution to simultaneously
More informationECON Introductory Econometrics. Lecture 17: Experiments
ECON4150  Introductory Econometrics Lecture 17: Experiments Monique de Haan (moniqued@econ.uio.no) Stock and Watson Chapter 13 Lecture outline 2 Why study experiments? The potential outcome framework.
More informationPlease follow the directions once you locate the Stata software in your computer. Room 114 (Business Lab) has computers with Stata software
STATA Tutorial Professor Erdinç Please follow the directions once you locate the Stata software in your computer. Room 114 (Business Lab) has computers with Stata software 1.Wald Test Wald Test is used
More informationCHAPTER 5. Exercise Solutions
CHAPTER 5 Exercise Solutions 91 Chapter 5, Exercise Solutions, Principles of Econometrics, e 9 EXERCISE 5.1 (a) y = 1, x =, x = x * * i x i 1 1 1 1 1 1 1 1 1 1 1 1 1 1 y * i (b) (c) yx = 1, x = 16, yx
More informationChapter 3: The Multiple Linear Regression Model
Chapter 3: The Multiple Linear Regression Model Advanced Econometrics  HEC Lausanne Christophe Hurlin University of Orléans November 23, 2013 Christophe Hurlin (University of Orléans) Advanced Econometrics
More informationTHE IMPORTANCE OF GOODS PRODUCTION AND INTERMEDIATE CONSUMPTION FOR AN INCREASED GDP
THE IMPORTANCE OF GOODS PRODUCTION AND INTERMEDIATE CONSUMPTION FOR AN INCREASED GDP RADUMARCEL JOIA * Abstract Human existence is conditioned, of course, by the consumption of goods to meet the needs.
More information, then the form of the model is given by: which comprises a deterministic component involving the three regression coefficients (
Multiple regression Introduction Multiple regression is a logical extension of the principles of simple linear regression to situations in which there are several predictor variables. For instance if we
More informationEC327: Advanced Econometrics, Spring 2007
EC327: Advanced Econometrics, Spring 2007 Wooldridge, Introductory Econometrics (3rd ed, 2006) Appendix D: Summary of matrix algebra Basic definitions A matrix is a rectangular array of numbers, with m
More informationRegression Analysis. Data Calculations Output
Regression Analysis In an attempt to find answers to questions such as those posed above, empirical labour economists use a useful tool called regression analysis. Regression analysis is essentially a
More informationSimultaneous Equation Models As discussed last week, one important form of endogeneity is simultaneity. This arises when one or more of the
Simultaneous Equation Models As discussed last week, one important form of endogeneity is simultaneity. This arises when one or more of the explanatory variables is jointly determined with the dependent
More informationInference in Regression Analysis. Dr. Frank Wood
Inference in Regression Analysis Dr. Frank Wood Inference in the Normal Error Regression Model Y i = β 0 + β 1 X i + ɛ i Y i value of the response variable in the i th trial β 0 and β 1 are parameters
More informationRegression Analysis Using ArcMap. By Jennie Murack
Regression Analysis Using ArcMap By Jennie Murack Regression Basics How is Regression Different from other Spatial Statistical Analyses? With other tools you ask WHERE something is happening? Are there
More informationForecasting Thai Gold Prices
1 Forecasting Thai Gold Prices Pravit Khaemasunun This paper addresses forecasting Thai gold price. Two forecasting models, namely, MultipleRegression, and AutoRegressive Integrated Moving Average (ARIMA),
More informationLecture 15. Endogeneity & Instrumental Variable Estimation
Lecture 15. Endogeneity & Instrumental Variable Estimation Saw that measurement error (on right hand side) means that OLS will be biased (biased toward zero) Potential solution to endogeneity instrumental
More informationChapter 11: Two Variable Regression Analysis
Department of Mathematics Izmir University of Economics Week 1415 20142015 In this chapter, we will focus on linear models and extend our analysis to relationships between variables, the definitions
More informationDeterminants of Stock Market Performance in Pakistan
Determinants of Stock Market Performance in Pakistan Mehwish Zafar Sr. Lecturer Bahria University, Karachi campus Abstract Stock market performance, economic and political condition of a country is interrelated
More informationRELATIONSHIP BETWEEN STOCK MARKET VOLATILITY AND EXCHANGE RATE: A STUDY OF KSE
RELATIONSHIP BETWEEN STOCK MARKET VOLATILITY AND EXCHANGE RATE: A STUDY OF KSE Waseem ASLAM Department of Finance and Economics, Foundation University Rawalpindi, Pakistan seem_aslam@yahoo.com Abstract:
More informationREGRESSION LINES IN STATA
REGRESSION LINES IN STATA THOMAS ELLIOTT 1. Introduction to Regression Regression analysis is about eploring linear relationships between a dependent variable and one or more independent variables. Regression
More informationLinear combinations of parameters
Linear combinations of parameters Suppose we want to test the hypothesis that two regression coefficients are equal, e.g. β 1 = β 2. This is equivalent to testing the following linear constraint (null
More informationWooldridge, Introductory Econometrics, 4th ed. Multiple regression analysis:
Wooldridge, Introductory Econometrics, 4th ed. Chapter 4: Inference Multiple regression analysis: We have discussed the conditions under which OLS estimators are unbiased, and derived the variances of
More informationWeighted least squares
Weighted least squares Patrick Breheny February 7 Patrick Breheny BST 760: Advanced Regression 1/17 Introduction Known weights As a precursor to fitting generalized linear models, let s first deal with
More informationRegression stepbystep using Microsoft Excel
Step 1: Regression stepbystep using Microsoft Excel Notes prepared by Pamela Peterson Drake, James Madison University Type the data into the spreadsheet The example used throughout this How to is a regression
More informationUNDERSTANDING MULTIPLE REGRESSION
UNDERSTANDING Multiple regression analysis (MRA) is any of several related statistical methods for evaluating the effects of more than one independent (or predictor) variable on a dependent (or outcome)
More informationRegression with a Binary Dependent Variable
Regression with a Binary Dependent Variable Chapter 9 Michael Ash CPPA Lecture 22 Course Notes Endgame Takehome final Distributed Friday 19 May Due Tuesday 23 May (Paper or emailed PDF ok; no Word, Excel,
More informationAugust 2012 EXAMINATIONS Solution Part I
August 01 EXAMINATIONS Solution Part I (1) In a random sample of 600 eligible voters, the probability that less than 38% will be in favour of this policy is closest to (B) () In a large random sample,
More informationBasic Statistics and Data Analysis for Health Researchers from Foreign Countries
Basic Statistics and Data Analysis for Health Researchers from Foreign Countries Volkert Siersma siersma@sund.ku.dk The Research Unit for General Practice in Copenhagen Dias 1 Content Quantifying association
More informationMISSING DATA TECHNIQUES WITH SAS. IDRE Statistical Consulting Group
MISSING DATA TECHNIQUES WITH SAS IDRE Statistical Consulting Group ROAD MAP FOR TODAY To discuss: 1. Commonly used techniques for handling missing data, focusing on multiple imputation 2. Issues that could
More informationSource engine marketing: A preliminary empirical analysis of web search data
Source engine marketing: A preliminary empirical analysis of web search data ABSTRACT Bruce Q. Budd Alfaisal University The purpose of this paper is to empirically investigate a website performance and
More informationQuick Stata Guide by Liz Foster
by Liz Foster Table of Contents Part 1: 1 describe 1 generate 1 regress 3 scatter 4 sort 5 summarize 5 table 6 tabulate 8 test 10 ttest 11 Part 2: Prefixes and Notes 14 by var: 14 capture 14 use of the
More informationMultiple Linear Regression
Multiple Linear Regression A regression with two or more explanatory variables is called a multiple regression. Rather than modeling the mean response as a straight line, as in simple regression, it is
More informationSimple Regression Theory II 2010 Samuel L. Baker
SIMPLE REGRESSION THEORY II 1 Simple Regression Theory II 2010 Samuel L. Baker Assessing how good the regression equation is likely to be Assignment 1A gets into drawing inferences about how close the
More informationChapter 6: Multivariate Cointegration Analysis
Chapter 6: Multivariate Cointegration Analysis 1 Contents: Lehrstuhl für Department Empirische of Wirtschaftsforschung Empirical Research and und Econometrics Ökonometrie VI. Multivariate Cointegration
More informationCalculate the holding period return for this investment. It is approximately
1. An investor purchases 100 shares of XYZ at the beginning of the year for $35. The stock pays a cash dividend of $3 per share. The price of the stock at the time of the dividend is $30. The dividend
More informationELECE8104 Stochastics models and estimation, Lecture 3b: Linear Estimation in Static Systems
Stochastics models and estimation, Lecture 3b: Linear Estimation in Static Systems Minimum Mean Square Error (MMSE) MMSE estimation of Gaussian random vectors Linear MMSE estimator for arbitrarily distributed
More information5. Linear Regression
5. Linear Regression Outline.................................................................... 2 Simple linear regression 3 Linear model............................................................. 4
More information