CHAPTER 1

1. CHAPTER 1 THE LINEAR REGRESSION MODEL: AN OVERVIEW Damodar Gujarati Econometrics by Example

2. THE LINEAR REGRESSION MODEL (LPM) • The general form of the LPM model is: • Yi = B1 + B2X2i + B3X3i + … + BkXki + ui • Or, as written in short form: • Yi =BX + ui • Y is the regressand, X is a vector of regressors, and u is an error term. Damodar Gujarati Econometrics by Example

3. POPULATION (TRUE) MODEL • This equation is known as the population or true model. • It consists of two components: • (1) A deterministic component, BX (the conditional mean of Y, or E(Y|X)). • (2) A nonsystematic, or random component, ui. Yi = B1 + B2X2i + B3X3i + … + BkXki + ui Damodar Gujarati Econometrics by Example

4. REGRESSION COEFFICIENTS • B1 is the intercept • B2 to Bk are the slope coefficients • Collectively, they are the regression coefficients or regression parameters • Each slope coefficient measures the (partial) rate of change in the meanvalue of Y for a unit change in the value of a regressor, ceteris paribus Damodar Gujarati Econometrics by Example

5. SAMPLE REGRESSION FUNCTION • The sample counterpart is: • Yi = b1 + b2X2i + b3X3i + … + bkXki + ei • Or, as written in short form: • Yi =bX + ei where e is a residual. • The deterministic component is written as: Damodar Gujarati Econometrics by Example

6. THE NATURE OF THE Y VARIABLE • Ratio Scale: • Ratio of two variables, distance between two variables, and ordering of variables are meaningful • Interval Scale: • Distance and ordering between two variables meaningful, but not ratio • Ordinal Scale: • Ordering of two variables meaningful (not ratio or distance) • Nominal Scale: • Categorical or dummy variables, qualitative in nature Damodar Gujarati Econometrics by Example

7. THE NATURE OF DATA • Time Series Data • A set of observations that a variable takes at different times, such as daily (e.g., stock prices), weekly (e.g., money supply), monthly (e.g., the unemployment rate), quarterly (e.g., GDP), annually (e.g., government budgets), quinquenially or every five years (e.g., the census of manufactures), or decennially or every ten years (e.g., the census of population). Damodar Gujarati Econometrics by Example

8. THE NATURE OF DATA • Cross-Section Data • Data on one or more variables collected at the same point in time. • Examples are the census of population conducted by the Census Bureau every 10 years, opinion polls conducted by various polling organizations, and temperature at a given time in several places. Damodar Gujarati Econometrics by Example

9. THE NATURE OF DATA • Panel, Longitudinal or Micro-panel Data • Combines features of both cross-section and time series data • Same cross-sectional units are followed over time • Panel data represents a special type of pooled data (simply time series, cross-sectional, where the same cross-sectional units are not necessarily followed over time) Damodar Gujarati Econometrics by Example

10. METHOD OF ORDINARY LEAST SQUARES • Method of Ordinary Least Squares (OLS) does not minimize the sum of the error term, but minimizes error sum of squares (ESS): • To obtain values of the regression coefficients, derivatives are taken with respect to the regression coefficients and set equal to zero. Damodar Gujarati Econometrics by Example

11. CLASSICAL LINEAR REGRESSION MODEL • Assumptions of the Classical Linear Regression Model (CLRM): • A-1: Model is linear in the parameters. • A-2: Regressors are fixed or nonstochastic. • A-3: Given X, the expected value of the error term is zero, or E(ui |X) = 0. Damodar Gujarati Econometrics by Example

12. CLASSICAL LINEAR REGRESSION MODEL • Assumptions of the Classical Linear Regression Model (CLRM): • A-4: Homoscedastic, or constant, variance of ui, or var(ui|X) = σ2. • A-5: No autocorrelation, or cov(ui,uj|X) = 0, i ≠ j. • A-6: No multicollinearity, or no perfect linear relationships among the X variables. • A-7: No specification bias. Damodar Gujarati Econometrics by Example

13. GAUSS-MARKOV THEOREM • On the basis of assumptions A-1 to A-7, the OLS method gives best linear unbiased estimators (BLUE): • (1) Estimators are linear functions of the dependent variable Y. • (2) The estimators are unbiased; in repeated applications of the method, the estimators approach their true values. • (3) In the class of linear estimators, OLS estimators have minimum variance; i.e., they are efficient, or the “best” estimators. Damodar Gujarati Econometrics by Example

14. HYPOTHESIS TESTING: t TEST • To test the following hypothesis: • H0: Bk = 0 • H1: Bk ≠ 0 • we calculate the following and use the t table to obtain the critical t value with n-k degrees of freedom for a given level of significance (or α, equal to 10%, 5%, or 1%): • If this value is greater than the critical t value, we can reject H0. Damodar Gujarati Econometrics by Example

15. HYPOTHESIS TESTING: t TEST • An alternative method is seeing whether zero lies within the confidence interval: • If zero lies in this interval, we cannot reject H0. • The p-value gives the exact level of significance, or the lowest level of significance at which we can reject H0. Damodar Gujarati Econometrics by Example

16. GOODNESS OF FIT, R2 • R2, the coefficient of determination, is an overall measure of goodness of fit of the estimated regression line. • Gives the percentage of the total variation in the dependent variable that is explained by the regressors. • It is a value between 0 (no fit) and 1 (perfect fit). • Let: • Then: Damodar Gujarati Econometrics by Example

17. HYPOTHESIS TESTING: F TEST • Testing the following hypothesis is equivalent to testing the hypothesis that all the slope coefficients are 0: • H0: R2 = 0 • H1: R2 ≠ 0 • Calculate the following and use the F table to obtain the critical F value with k-1 degrees of freedom in the numerator and n-k degrees of freedom in the denominator for a given level of significance: • If this value is greater than the critical F value, reject H0. Damodar Gujarati Econometrics by Example