Econometrics Lecture 2 Introduction to Linear Regression Part

Econometrics - Lecture 2 Introduction to Linear Regression – Part 2

Contents n n n Goodness-of-Fit Hypothesis Testing Asymptotic Properties of the OLS estimator Multicollinearity Prediction Oct 1, 2010 Hackl, Econometrics, Lecture 2 2

Goodness-of-fit R² The quality of the model yi = xi' + εi can be measured by R 2, the goodness-of-fit (Go. F) statistic n R 2 is the portion of the variance in y that can be explained by the linear regression with regressors xk, k=1, …, K n If the model contains an intercept (as usual): with Ṽ{ei} = (Σi ei²)/(N-1) n Alternatively, R 2 can be calculated as Oct 1, 2010 Hackl, Econometrics, Lecture 2 3

Properties of R 2 n n n 0 R 2 1, if the model contains an intercept R 2 = 1: all residuals are zero R 2 = 0: for all regressors, bk = 0; the model explains nothing Comparisons of R 2 for two models makes no sense if the explained variables are different R 2 cannot decrease if a variable is added Oct 1, 2010 Hackl, Econometrics, Lecture 2 4

Example: Individ. Wages, cont’d OLS estimated wage equation (Table 2. 1, Verbeek) only 3, 17% of the variation of individual wages p. h. is due to the gender Oct 1, 2010 Hackl, Econometrics, Lecture 2 5

Other Go. F Measures n n n For the case of no intercept: Uncentered R 2; cannot become negative Uncentered R 2 = 1 – Σi ei²/ Σi yi² For comparing models: adjusted R 2; compensated for added regressor, penalty for increasing K for a given model, adj R 2 is smaller than R 2 For other than OLS-estimated models coincides with R 2 for OLS-estimated models Oct 1, 2010 Hackl, Econometrics, Lecture 2 6

Contents n n n Goodness-of-Fit Hypothesis Testing Asymptotic Properties of the OLS estimator Multicollinearity Prediction Oct 1, 2010 Hackl, Econometrics, Lecture 2 7

Individual Wages OLS estimated wage equation (Table 2. 1, Verbeek) b 1 = 5, 147, se(b 1) = 0, 081: mean wage p. h. for females: 5, 15$, with std. error of 0, 08$ b 2 = 1, 166, se(b 2) = 0, 112 95% confidence interval for β 1: 4, 988 β 1 5, 306 Oct 1, 2010 Hackl, Econometrics, Lecture 2 8

OLS-Estimator: Distributional Properties Under the assumptions (A 1) to (A 5): n The OLS estimator b = (X’X)-1 X’y is normally distributed with mean β and covariance matrix V{b} = σ2(X‘X)-1 n b ~ N(β, σ2(X’X)-1), bk ~ N(βk, σ2 ckk), k=1, …, K The statistic n follows the standard normal distribution N(0, 1) The statistic follows the t-distribution with N-K degrees of freedom (df) Oct 1, 2010 Hackl, Econometrics, Lecture 2 9

Testing a Regression Coefficient: t-Test For testing a restriction wrt a single regression coefficient k: n Null hypothesis H 0: k = q n n Alternative HA: k > q Test statistic: (computed from the sample with known distribution under the null hypothesis) tk is a realization of the random variable t. N-K, which follows the tdistribution with N-K degrees of freedom (df = N-K) q under H 0 and q given the Gauss-Markov assumptions and normality of the errors Reject H 0, if the p-value P{t. N-K > tk | H 0} is small (tk-value is large) Oct 1, 2010 Hackl, Econometrics, Lecture 2 10

Normal and t-Distribution Standard normal distribution: Z ~ N(0, 1) n Distribution function F(z) = P{Z ≤ z} t(df)-distribution n Distribution function F(t) = P{Tdf ≤ t} n p-value: P{t. N-K > tk | H 0} = 1 – FH 0(tk) For growing df, the t-distribution approaches the standard normal distribution, t follows asymptotically (N → ∞) the N(0, 1)-distribution n 0. 975 -percentiles tdf, 0. 975 of the t(df)-distribution df tdf, 0. 025 n 5 10 20 30 50 100 2. 571 2. 228 2. 085 2. 042 2. 009 1. 984 1. 972 ∞ 1. 96 0. 975 -percentile of the standard normal distribution: z 0. 975 = 1. 96 Oct 1, 2010 Hackl, Econometrics, Lecture 2 11

OLS-Estimators: Asymptotic Distribution If the Gauss-Markov (A 1) - (A 4) assumptions hold but not the normality assumption (A 5): t-statistic follows asymptotically (N → ∞) the standard normal distribution In many situations, the unknown exact properties are substituted by approximate results (asymptotic theory) The t-statistic n Follows the t-distribution with N-K d. f. n follows approximately the standard normal distribution N(0, 1) The approximation error decreases with increasing sample size N n Oct 1, 2010 Hackl, Econometrics, Lecture 2 12

Two-sided t-Test For testing a restriction wrt a single regression coefficient k: n Null hypothesis H 0: k = q n Alternative HA: k ≠ q Test statistic: (computed from the sample with known distribution under the null hypothesis) n Reject H 0, if the p-value P{t. N-K > |tk| | H 0} is small (|tk|-value is large) n Oct 1, 2010 Hackl, Econometrics, Lecture 2 13

Individual Wages, cont’d OLS estimated wage equation (Table 2. 1, Verbeek) Test of null hypothesis H 0: β 2 = 0 (no gender effect on wages) against HA: β 2 > 0 t 2 = b 2/se(b 2) = 1. 1661/0. 1122 = 10. 38 Under H 0, t follows the t-distribution with df = 3294 -2 = 3292 p-value = P{t 3292 > 10. 38 | H 0} = 3. 7 E-25: reject H 0! Oct 1, 2010 Hackl, Econometrics, Lecture 2 14

Individual Wages, cont’d OLS estimated wage equation: Output from GRETL Modell 1: KQ, benutze die Beobachtungen 1 -3294 Abhängige Variable: WAGE const MALE Koeffizient 5, 14692 1, 1661 Mittel d. abh. Var. 3, 269186 Summe d. quad. Res. R-Quadrat F(1, 3292) Log-Likelihood Schwarz-Kriterium Std. Fehler 0, 0812248 0, 112242 t-Quotient 63, 3664 10, 3891 P-Wert <0, 00001 *** 5, 757585 Stdabw. d. abh. Var. 34076, 92 0, 031746 107, 9338 -8522, 228 17060, 66 Stdfehler d. Regress. Korrigiertes R-Quadrat P-Wert(F) Akaike-Kriterium Hannan-Quinn-Kriterium 3, 217364 0, 031452 6, 71 e-25 17048, 46 17052, 82 p-value for t. MALE-test: < 0, 00001 „gender has a significant effect on wages p. h“ Oct 1, 2010 Hackl, Econometrics, Lecture 2 15

Significance Tests For testing a restriction wrt a single regression coefficient k: n Null hypothesis H 0: k = q n n Alternative HA: k ≠ q Test statistic: (computed from the sample with known distribution under the null hypothesis) n Determine the critical value t. N-K, 1 -a/2 for the significance level a from P{|tk| > t. N-K, 1 -a/2 | H 0} = a Reject H 0, if |tk| > t. N-K, 1 -a/2 n Typically, a has the value 0. 05 n Oct 1, 2010 Hackl, Econometrics, Lecture 2 16

Significance Tests, cont’d One-sided test : n Null hypothesis H 0: k = q n n Alternative HA: k > q ( k < q) Test statistic: (computed from the sample with known distribution under the null hypothesis) Determine the critical value t. N-K, a for the significance level a from P{tk > t. N-K, a | H 0} = a Reject H 0, if tk > t. N-K, a (tk < -t. N-K, a) Oct 1, 2010 Hackl, Econometrics, Lecture 2 17

Confidence Interval for k Range of values (bkl, bku) for which the null hypothesis on k is not rejected bkl = bk - t. N-K, 1 -a/2 se(bk) < k < bk + t. N-K, 1 -a/2 se(bk) = bkl n Refers to the significance level a of the test n For large values of df and a = 0. 05 (1. 96 ≈ 2) bk – 2 se(bk) < k < bk + 2 se(bk) n Confidence level: g = 1 - a Interpretation: n A range of values for the true k that are not unlikely, given the data (? ) n A range of values for the true k such that 100 g% of all intervals constructed in that way contain the true k Oct 1, 2010 Hackl, Econometrics, Lecture 2 18

Individual Wages, cont’d OLS estimated wage equation (Table 2. 1, Verbeek) The confidence interval for the gender wage difference (in USD p. h. ) n confidence level g = 0. 95 1. 1661 – 1. 96*0. 1122 < 1. 1661 + 1. 96*0. 1122 0. 946 < 2 < 1. 386 (or 0. 94 < 2 < 1. 39) n g = 0. 99: 0. 877 < 2 < 1. 455 Oct 1, 2010 Hackl, Econometrics, Lecture 2 19

Testing a Linear Restriction on Regression Coefficients Linear restriction r’ = q n Null hypothesis H 0: r’ = q n Alternative HA: r’ > q Test statistic n se(r’b) is the square root of V{r’b} = r’V{b}r Under H 0 and (A 1)-(A 5), t follows the t-distribution with df = N-K n GRETL: The option Linear restrictions from Tests on the output window of the Model statement Ordinary Least Squares allows to test linear restrictions on the regression coefficients Oct 1, 2010 Hackl, Econometrics, Lecture 2 20

Testing Several Regression Coefficients: F-test For testing a restriction wrt more than one, say J with 1<J<K, regression coefficients: n n n Null hypothesis H 0: k = 0, K-J+1 ≤ k ≤ K Alternative HA: for at least one k, K-J+1 ≤ k ≤ K, k ≠ 0 F-statistic: (computed from the sample, with known distribution under the null hypothesis; R 02 (R 12): R 2 for (un)restricted model) F follows the F-distribution with J and N-K d. f. under H 0 and given the Gauss-Markov assumptions (A 1)-(A 4) and normality of the εi (A 5) Reject H 0, if the p-value P{FJ, N-K > F | H 0} is small (F-value is large) The test with J = K-1 is a standard test in GRETL q n n Oct 1, 2010 Hackl, Econometrics, Lecture 2 21

Individual Wages, cont’d A more general model is wagei = β 1 + β 2 malei + β 3 schooli + β 4 experi + εi β 2 measures the difference in expected wages p. h. between males and females, given the other regressors fixed, i. e. , with the same schooling and experience: ceteris paribus condition Have school and exper an explanatory power? Test of null hypothesis H 0: β 3 = β 4 = 0 against HA: H 0 not true n R 02 = 0. 0317 n R 12 = 0. 1326 n p-value = P{F 2, 3290 > 191. 24 | H 0} = 2. 68 E-79 Oct 1, 2010 Hackl, Econometrics, Lecture 2 22

Individual Wages, cont’d OLS estimated wage equation (Table 2. 2, Verbeek) Oct 1, 2010 Hackl, Econometrics, Lecture 2 23

Alternatives for Testing Several Regression Coefficients Test again n H 0: k = 0, K-J+1 ≤ k ≤ K HA: at least one of these k ≠ 0 1. The test statistic F can alternatively be calculated as n n n S 0 (S 1): sum of squared residuals for the (un)restricted model F follows under H 0 and (A 1)-(A 5) the F(J, N-K)-distribution 2. If s 2 is known, the test can be based on F = (S 0 -S 1)/s 2 under H 0 and (A 1)-(A 5): Chi-squared distributed with J d. f. n For large N, s 2 is very close to s 2; test with F approximates F-test Oct 1, 2010 Hackl, Econometrics, Lecture 2 24

Individual Wages, cont’d A more general model is wagei = β 1 + β 2 malei + β 3 schooli + β 4 experi + εi Have school and exper an explanatory power? n Test of null hypothesis H 0: β 3 = β 4 = 0 against HA: H 0 not true n S 0 = 34076. 92 n S 1 = 30527. 87 F = [(34076. 92 - 30527. 87)/2]/[30527. 87/(3294 -4)] = 191. 24 Does any regressor contribute to explanation? n Overall F-test for H 0: β 2 = … = β 4 = 0 against HA: H 0 not true (see Table 2. 2 or GRETL-output): J=3 F = 167. 63, p-value: 4. 0 E-101 Oct 1, 2010 Hackl, Econometrics, Lecture 2 25

The General Case Test of H 0: R = q: J linear restrictions on coefficients (R: Jx. K matrix, q: J-vector) Example: Wald test: test statistic ξ = (Rb - q)’[RV{b}R’]-1(Rb - q) n follows under H 0 for large N approximately the Chi-squared distribution with J d. f. n Test based on F = ξ /J is algebraically identical to the F-test with Oct 1, 2010 Hackl, Econometrics, Lecture 2 26

p-value, Size, and Power Type I error: the null hypothesis is rejected, while it is actually true n p-value: the probability to commit the type I error In experimental situations, the probability of committing the type I error can be chosen before applying the test; this probability is the significance level α and denoted the size of the test n In model-building situations, not a decision but learning from data is intended; multiple testing is quite usual; use of p-values is more appropriate than using a strict α Type II error: the null hypothesis is not rejected, while it is actually wrong; the decision is not in favor of the true alternative n The probability to decide in favor of the true alternative, i. e. , not making a type II error, is called the power of the test; depends of true parameter values n Oct 1, 2010 Hackl, Econometrics, Lecture 2 27

p-value, Size, and Power, cont’d n The smaller the size of the test, the larger is its power (for a given sample size) n The more HA deviates from H 0, the larger is the power of a test of a given size (given the sample size) The larger the sample size, the larger is the power of a test of a given size n Attention! Significance vs relevance Oct 1, 2010 Hackl, Econometrics, Lecture 2 28

Contents n n n Goodness-of-Fit Hypothesis Testing Asymptotic Properties of the OLS estimator Multicollinearity Prediction Oct 1, 2010 Hackl, Econometrics, Lecture 2 29

OLS Estimators: Asymptotic Properties Gauss-Markov assumptions (A 1)-(A 4) plus the normality assumption (A 5) are in many situations very restrictive An alternative are properties derived from asymptotic theory n Asymptotic results hopefully are sufficiently precise approximations for large (but finite) N n Typically, Monte Carlo simulations are used to assess the quality of asymptotic results Asymptotic theory: deals with the case where the sample size N goes to infinity: N → ∞ Oct 1, 2010 Hackl, Econometrics, Lecture 2 30

Chebychev’s Inequality: Bound for probability of deviations from its mean P{|z-E{z}| > ks} < k-2 for all k>0; true for any distribution with moments E{z} and s 2 = V{z} For OLS-estimator bk: n n for all d>0; ckk: the k-th diagonal element of (X‘X)-1 = (Σi xi xi’)-1 For growing N: the elements of Σi xi xi’ increase, V{bk} decreases Given (A 6), for all d>0 Oct 1, 2010 Hackl, Econometrics, Lecture 2 31

OLS Estimators: Consistency If (A 2) from the Gauss-Markov assumptions (uncorrelated xi and ei) and the assumption (A 6) are fulfilled: A 6 1/N (ΣNi=1 xi xi’) = 1/N (X’X) converges with growing N to a finite, nonsingular matrix Σxx bk converges in probability to k for N → ∞ Consistency of the OLS estimators b: n For N → ∞, b converges in probability to β, i. e. , the probability that b differs from β by a certain amount goes to zero n plim. N → ∞ b = β n The distribution of b collapses in β Needs no assumptions beyond (A 2) and (A 6)! Oct 1, 2010 Hackl, Econometrics, Lecture 2 32

OLS Estimators: Consistency, cont’d Consistency of the OLS estimators can also be shown to hold under weaker assumptions: The OLS estimators b are consistent, plim. N → ∞ b = β, if the assumptions (A 7) and (A 6) are fulfilled A 7 The error terms have zero mean and are uncorrelated with each of the regressors: E{xi εi} = 0 Follows from and plim(b - β) = Sxx-1 E{xi εi} Oct 1, 2010 Hackl, Econometrics, Lecture 2 33

Consistency of s 2 The estimator s 2 for the error term variance σ2 is consistent, plim. N → ∞ s 2 = σ2, if the assumptions (A 3), (A 6), and (A 7) are fulfilled Oct 1, 2010 Hackl, Econometrics, Lecture 2 34

Consistency: Some Properties n plim g(b) = g(β) q n if plim s 2 = σ2, plim s = σ The conditions for consistency are weaker than those for unbiasedness Oct 1, 2010 Hackl, Econometrics, Lecture 2 35

OLS Estimators: Asymptotic Normality n n n Distribution of OLS-estimators mostly unknown Approximate distribution, based on the asymptotic distribution Most estimators in econometrics follow asymptotically the normal distribution Asymptotic distribution of the consistent estimator b: distribution of N 1/2(b - β) for N → ∞ Under the Gauss-Markov assumptions (A 1)-(A 4) and assumption (A 6), the OLS estimators b fulfills “→” means “is asymptotically distributed as” Oct 1, 2010 Hackl, Econometrics, Lecture 2 36

OLS Estimators: Approximate Normality Under the Gauss-Markov assumptions (A 1)-(A 4) and assumption (A 6), the OLS estimators b follow approximately the normal distribution The approximate distribution does not make use of assumption (A 5), i. e. , the normality of the error terms! Tests of hypotheses on coefficients k, n t-test n F-test can be performed by making use of the approximate normal distribution Oct 1, 2010 Hackl, Econometrics, Lecture 2 37

Assessment of Approximate Normality Quality of n approximate normal distribution of OLS-estimators p-values of t- and F-tests n power of tests, confidence intervals, ec. depends on sample size N and factors related to Gauss-Markov assumptions etc. Monte Carlo studies: simulations that indicate consequences of deviations from ideal situations Example: yi = 1 + 2 xi + ei; distribution of b 2 under classical assumptions? n 1) Choose N; 2) generate xi, ei, calculate yi, i=1, …, N; 3) estimate b 2 n Repeat steps 1)-3) R times: the R values of b 2 allow assessment of the distribution of b 2 n Oct 1, 2010 Hackl, Econometrics, Lecture 2 38

Contents n n n Goodness-of-Fit Hypothesis Testing Asymptotic Properties of the OLS estimator Multicollinearity Prediction Oct 1, 2010 Hackl, Econometrics, Lecture 2 39

Multicollinearity OLS estimators b = (X’X)-1 X’y for regression coefficients require that the Kx. K matrix X’X or Σi xi xi’ can be inverted In real situations, regressors may be correlated, such as n experience and schooling (measured in years) n age and experience n inflation rate and nominal interest rate n common trends of economic time series, e. g. , in lag structures Multicollinearity: between the explanatory variables exists n an exact linear relationship n an approximate linear relationship Oct 1, 2010 Hackl, Econometrics, Lecture 2 40

Multicollinearity: Consequences Approximate linear relationship between regressors: n When correlations between regressors are high: hard to identify the individual impact of each of the regressors n Inflated variances q q n If xk can be approximated by the other regressors, variance of bk is inflated; Smaller tk-statistic, reduced power of t-test Example: yi = 1 xi 1 + 2 xi 2 + ei q with sample variances of X 1 and X 2 equal 1 and correlation r 12, Oct 1, 2010 Hackl, Econometrics, Lecture 2 41

Exact Multicollinearity Exact linear relationship between regressors: n Example: Wage equation q Regressors male and female in addition to intercept q Regressor exper defined as exper = age - school - 6 n Σi xi xi’ is not invertible n Econometric software reports ill-defined matrix Σi xi xi’ n GRETL drops regressor Remedy: n Exclude one of the regressors n Example: Wage equation q q q Drop regressor female, use regressor male in addition to intercept Alternatively: use female and intercept Not good: use of male and female Oct 1, 2010 Hackl, Econometrics, Lecture 2 42

Variance Inflation Factor Variance of bk Rk 2: R 2 of the regression of xk on all other regressors n If xk can be approximated by the other regressors, Rk 2 is close to 1, the variance inflated Variance inflation factor: VIF(bk) = (1 - Rk 2)-1 Large values for some or all VIFs indicate multicollinearity Attention! Large values for VIF can also have other causes n Small value of variance of Xk n Small number N of observations Oct 1, 2010 Hackl, Econometrics, Lecture 2 43

Other Indicators Large values for some or all variance inflation factors VIF(bk) are an indicator for multicollinearity Other indicators: n At least one of the Rk 2, k = 1, …, K, has a large value n Large values of standard errors se(bk) (low t-statistics), but reasonable or good R 2 and F-statistics n Effect of adding a regressor on standard errors se(bk) of estimates bk of regressors already in the model: increasing values of se(bk) indicate multicollinearity Oct 1, 2010 Hackl, Econometrics, Lecture 2 44

Contents n n n Goodness-of-Fit Hypothesis Testing Asymptotic Properties of the OLS estimator Multicollinearity Prediction Oct 1, 2010 Hackl, Econometrics, Lecture 2 45

The Predictor Given the relation yi = xi’ + ei Given estimators b, predictor for Y at x 0, i. e. , y 0 = x 0’ + e 0: ŷ 0 = x 0’b Prediction error: f 0 = ŷ 0 - y 0 = x 0’(b – ) + e 0 Some properties of ŷ 0: n Under assumptions (A 1) and (A 2), E{b} = and ŷ 0 is an unbiased predictor n Variance of ŷ 0 V{ŷ 0} = V{x 0’b} = x 0’ V{b} x 0 = s 2 x 0’(X’X)-1 x 0 n Variance of the prediction error f 0 V{f 0} = V{x 0’(b – ) + e 0} = s 2 + s 2 x 0’(X’X)-1 x 0 = s²f 0 given that f 0 and b are uncorrelated 100 g% prediction interval: ŷ 0 – z 1 -g/2 sf 0 ≤ y 0 ≤ ŷ 0 + z 1 -g/2 sf 0 Oct 1, 2010 Hackl, Econometrics, Lecture 2 46

Example: Simple Regression Given the relation yi = 1 + xi 2 + ei Predictor for Y at x 0, i. e. , y 0 = 1 + x 0 2 + e 0: ŷ 0 = b 1 + x 0’b 2 Variance of the prediction error n Prediction intervals for various x‘s Oct 1, 2010 Hackl, Econometrics, Lecture 2 47

Your Homework 1. For Verbeek’s data set “WAGES” use GRETL (a) for estimating a linear regression model with intercept for WAGES p. h. with explanatory variables MALE, SCHOOL, and AGE; (b) interpret the coefficients of the model; (c) test the hypothesis that men and women, on average, have the same wage p. h. , against the alternative that women earn less; (d) calculate a 95% confidence interval for the wage difference of males and females. 2. Generate a variable EXPER_B by adding the Binomial random variable BE ~ B(2, 0. 05); (a) estimate two linear regression models with intercept for WAGES p. h. with explanatory variables (i) MALE, SCHOOL, EXPER and AGE, and (ii) MALE, SCHOOL, EXPER_B and AGE; compare the R² of the models; (b) compare the VIFs for the variables of the two models. Oct 1, 2010 Hackl, Econometrics, Lecture 2 48

Your Homework 3. Show for a linear regression with intercept that 4. Show that the F-test based on and the F-test based on are identical. Oct 1, 2010 Hackl, Econometrics, Lecture 2 49