Preview

Introductory Econometrics

Powerful Essays
Open Document
Open Document
14263 Words
Grammar
Grammar
Plagiarism
Plagiarism
Writing
Writing
Score
Score
Introductory Econometrics
Classical Linear Regression Models and Relaxing their Assumptions

Seid Nuru seidnali@yahoo.com
August 2012

>

The Classical Linear Regression Models

Introduction The Simple Regression Model The Multiple Linear Regression Models Violations of the Assumptions of CLRMs

Definition


Econometrics is the application of statistical, and mathematical techniques to the analysis of economic data with a purpose of verifying or refuting economic theories.
Theory Mathematical Model Econometric Model

As income increases, consumption also increases, but not as much as income.

yi = f ( xi ) = β0 + β1xi

y i = f ( x i ) = β0 + β1x i + εi

2

>

The Classical Linear Regression Models

Introduction The Simple Regression Model The Multiple Linear Regression Models Violations of the Assumptions of CLRMs

Definition


Why do we need to include the stochastic (random) component, for example in the consumption function? function?
— Omission of variables leads to misspecification problem. For example, income is not the only determinants of consumption. — There may be measurement error in collecting data. — We may use poor proxy variables. — The functional form may not be correct.

3

>

The Classical Linear Regression Models

Introduction The Simple Regression Model The Multiple Linear Regression Models Violations of the Assumptions of CLRMs

Some Concepts: Regression, Causation, and Correlation


• • •

Regression is estimation or prediction of the average value of a dependent variable on the basis of the fixed values of other variables. Causation comes from theory rather than statistics. Thus, regression does not necessarily imply causation. Correlation measures the strength of linear association between variables. In regression, we have stochastic dependent variable and nonnon-stochastic independent variable (fixed) while in correlation, variables involved are stochastic.

4

>

The Classical Linear Regression Models

You May Also Find These Documents Helpful

  • Satisfactory Essays

    Acc/531 Week 4

    • 646 Words
    • 3 Pages

    was selected to estimate the multiple regression model, where y is the number of hours of television watched last week, x1 is the age (in years), x2 is the number of years of education, and x3 is income (in $1,000). The regression equation…

    • 646 Words
    • 3 Pages
    Satisfactory Essays
  • Good Essays

    Econ2206 Assignment

    • 457 Words
    • 2 Pages

    to find the answers to questions (iii)-(vii), where s are parameters to be estimated. In addition to answering the questions (i)-(vii), you are encouraged to comment on the adequacy of this model for analyzing the questions. You have access to a data set from a recent national health survey of Luckland, which can be regarded as a random sample. The data description is in the file “NHS.des” and data are in the file “NHS.raw”. Read “NHS.des” carefully and make sure that you understand the meaning of each variable in…

    • 457 Words
    • 2 Pages
    Good Essays
  • Good Essays

    This write-up provides a general overview of the most common data assumptions which the researcher will encounter in statistical research.…

    • 802 Words
    • 4 Pages
    Good Essays
  • Good Essays

    The four assumptions of the simple linear regression model are (1) linearity – the mean of E(Y|X) is a straight line function of X; (2) constant variance – the standard deviation of Y|X is the same for all X; (3) normality – the distribution of Y|X is normal; (4) independence – the observations are all independent.…

    • 958 Words
    • 4 Pages
    Good Essays
  • Good Essays

    Let the measurement error term of the dependant variable be defined as e(0)=y(true)-y(observe). Under the assumption that MLR 1-4 holds for the original model, we can substitute in to the regression model to produce equation respond(true)= β(0)+ β(1)resplast+ β(2)avggift+ β(3)propresp+ β(4)mailyear+u+e(0). Given that there are no correlation with the explanatory variables, OLS estimators βs will remain constant and unbiased. This is an extension of MLR 3(zero conditional mean) which suggests that the error term u has no correlation to the βs and hence can infer that likewise ‘e’ has zero conditional mean making the OLS estimators consistent and unbiased. If we consider that e(0) does not satisfy zero conditional mean then only β0 will be biased.…

    • 940 Words
    • 3 Pages
    Good Essays
  • Satisfactory Essays

    a) draw a scatter diagram of number of sales calls and number of units sold…

    • 384 Words
    • 2 Pages
    Satisfactory Essays
  • Powerful Essays

    Cultural Competence

    • 1572 Words
    • 7 Pages

    Holland, K., Jenkins, J., Solomon, J. and Whittem, S., (2003). Applying the Ropper Logan. Tierney Model in Practice. Churchill Livingstone.…

    • 1572 Words
    • 7 Pages
    Powerful Essays
  • Good Essays

    Decision Making Problem

    • 1292 Words
    • 6 Pages

    of quantitative models. The technical appendix should include a formulation of a linear model, as we…

    • 1292 Words
    • 6 Pages
    Good Essays
  • Powerful Essays

    Linear Regression

    • 2726 Words
    • 11 Pages

    Linear regression provides a means to estimate or predict the value of a dependent variable based on the value of one or more independent variables. The regression equation is a mathematical expression of a causal proposition emerging from a theoretical framework. The linkage between the theoretical statement and the equation is made prior to data collection and analysis. Linear regression is a statistical method of estimating the expected value of one variable, y, given the value of another variable, x. The term simple linear regression refers to the use of one independent variable, x, to predict one dependent variable, y.…

    • 2726 Words
    • 11 Pages
    Powerful Essays
  • Satisfactory Essays

    Excel

    • 273 Words
    • 2 Pages

    Statistics and parameters. Properties for a statistic. Central Limit Theorem. Distribution of the sample mean, difference in means and the proportion. Point and interval estimates for the mean, difference in means, and proportion. Hypotheses testing and types of errors. Significance levels and p values. Small sample testing: Chi square, t and F distributions and their properties. Applications of chi square and t distributions to interval estimates and tests. UNIT 2 : CLASSICAL TWO VARIABLE LINEAR REGRESSION MODEL Types of Data : Time Series, Cross Section and Panel Data. Concept of PRF and SRF. Estimation of the SRF using OLS. Analysis of variance and R squared. Understanding the residuals/error term. Assumptions of the model. Expectation and standard errors of the regression coefficients and the error term. Gauss Markov Theorem. Confidence intervals and tests on population regression coefficients, variance of population disturbance term, and forecasts. Testing the significance of the model as a whole. Testing the normality assumption. UNIT 3 : MULTIPLE REGRESSION MODEL The three variable case. Derivation of the coefficients. Correlation. Additional assumptions. Adjusted R square. Confidence intervals and testing of the regression coefficients. F and t tests for structural stability, contribution and justification of an explanatory variable. UNIT 4 : OTHER FUNCTIONAL FORMS Regressions in deviation form and through the origin. The log-log, log-lin, lin-log, reciprocal, log-reciprocal models with application. UNIT 5 : DUMMY VARIABLES Intercept, Slope Dummy variables. Interaction between qualitative variables. Interaction between quantitative and qualitative variables. Dummies for testing for the presences of Seasonal Trends. Main Readings 1. Christopher Dougherty, Introductory Econometrics 3rd Edition Oxford University Press (2007) 2. Gujarati , Damodar : Basic Econometrics , 3rd edition Mc.Graw Hill,…

    • 273 Words
    • 2 Pages
    Satisfactory Essays
  • Good Essays

    Multicollinearity

    • 561 Words
    • 3 Pages

    One problem that can arise in multiple regression analysis is multicollinearity. Multicollinearity is when two or more of the independent variables of a multiple regression model are highly correlated. Technically, if two of the independent variables are correlated, we have collinearity; when three or more independent variables are correlated, we have multicollinearity. However, the two terms are frequently used interchangeably. The reality of business research is that most of the time some correlation between predictors (independent variables) will be present. The problem of multicollinearity arises when the inter-correlation between predictor variables is high. This relationship causes several other problems, particularly in the interpretation of the analysis.…

    • 561 Words
    • 3 Pages
    Good Essays
  • Powerful Essays

    extra

    • 6263 Words
    • 31 Pages

    Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation…

    • 6263 Words
    • 31 Pages
    Powerful Essays
  • Powerful Essays

    Chapter 1 Introduction 1 Chapter 2 The Classical Multiple Linear Regression Model 2 Chapter 3 Least Squares 3 Chapter 4 Finite-Sample Properties of the Least Squares Estimator 7 Chapter 5 Large-Sample Properties of the Least Squares and Instrumental Variables Estimators 14 Chapter 6 Inference and Prediction 19 Chapter 7 Functional Form and Structural Change 23 Chapter 8 Specification Analysis and Model Selection 30 Chapter 9 Nonlinear Regression Models 32 Chapter 10 Nonspherical Disturbances - The Generalized Regression Model 37 Chapter 11 Heteroscedasticity 41 Chapter 12 Serial Correlation 49 Chapter 13 Models for Panel Data 53 Chapter 14 Systems of Regression Equations 63 Chapter 15 Simultaneous Equations Models 72 Chapter 16 Estimation Frameworks in Econometrics 78 Chapter 17 Maximum Likelihood Estimation 84 Chapter 18 The Generalized Method of Moments 93 Chapter 19 Models with Lagged Variables 97 Chapter 20 Time Series Models 101 Chapter 21 Models for Discrete Choice 1106 Chapter 22 Limited Dependent Variable and Duration Models 112 Appendix A Matrix Algebra 115 Appendix B Probability and Distribution Theory 123 Appendix C Estimation and Inference 134 Appendix D Large Sample Distribution Theory 145 Appendix E Computation and Optimization 146 In the solutions, we denote: • scalar values with italic, lower case letters, as in a or α • column vectors with boldface lower case letters, as in b, • row vectors as transposed column vectors, as in b′, • single population parameters with greek letters, as in β, • sample estimates of parameters with English letters, as in b as an estimate of β, ˆ • sample estimates of population parameters with a caret, as in α • matrices with boldface upper case letters, as in M or Σ, • cross section observations with subscript i, time series observations with…

    • 75524 Words
    • 303 Pages
    Powerful Essays
  • Good Essays

    Econometrics – literally ,,economic measurement” is the quantitative measurement and analysis of actual economic and business phenomena.…

    • 516 Words
    • 3 Pages
    Good Essays
  • Powerful Essays

    Computer and Grades

    • 3921 Words
    • 14 Pages

    This chapter presents the background of the study, problem and its significance, and the scope and limitation of the study.…

    • 3921 Words
    • 14 Pages
    Powerful Essays