Eco Basic 1 8

Basic Econometrics Course Leader Prof. Dr.Sc VuThieu May 2004 Prof.VuThieu

Basic Econometrics Introduction : What is Econometrics? May 2004 Prof.VuThieu

Introduction What is Econometrics ? Definition 1 : Economic Measurement Definition 2 : Application of the mathematical statistics to economic data in order to lend empirical support to the economic mathematical models and obtain numerical results ( Gerhard Tintner, 1968 ) May 2004 Prof.VuThieu

Introduction What is Econometrics ? Definition 3 : The quantitative analysis of actual economic phenomena based on concurrent development of theory and observation, related by appropriate methods of inference ( P.A.Samuelson, T.C.Koopmans and J.R.N.Stone, 1954 ) May 2004 Prof.VuThieu

Introduction What is Econometrics ? Definition 4 : The social science which applies economics, mathematics and statistical inference to the analysis of economic phenomena ( By Arthur S. Goldberger, 1964 ) Definition 5 : The empirical determination of economic laws ( By H. Theil, 1971 ) May 2004 Prof.VuThieu

Introduction What is Econometrics ? Definition 6 : A conjunction of economic theory and actual measurements, using the theory and technique of statistical inference as a bridge pier ( By T.Haavelmo, 1944 ) And the others May 2004 Prof.VuThieu

May 2004 Prof.VuThieu Econometrics Economic Theory Mathematical Economics Economic Statistics Mathematic Statistics

Introduction Why a separate discipline ? Economic theory makes statements that are mostly qualitative in nature, while econometrics gives empirical content to most economic theory Mathematical economics is to express economic theory in mathematical form without empirical verification of the theory, while econometrics is mainly interested in the later May 2004 Prof.VuThieu

Introduction Why a separate discipline ? Economic Statistics is mainly concerned with collecting, processing and presenting economic data. It does not being concerned with using the collected data to test economic theories Mathematical statistics provides many of tools for economic studies, but econometrics supplies the later with many special methods of quantitative analysis based on economic data May 2004 Prof.VuThieu

Introduction Methodology of Econometrics Statement of theory or hypothesis: Keynes stated: ”Consumption increases as income increases, but not as much as the increase in income”. It means that “The marginal propensity to consume (MPC) for a unit change in income is grater than zero but less than unit” May 2004 Prof.VuThieu

Introduction Methodology of Econometrics (2) Specification of the mathematical model of the theory Y = ß 1 + ß 2 X ; 0 < ß 2 < 1 Y= consumption expenditure X= income ß 1 and ß 2 are parameters; ß 1 is intercept, and ß 2 is slope coefficients May 2004 Prof.VuThieu

Introduction Methodology of Econometrics (3) Specification of the econometric model of the theory Y = ß 1 + ß 2 X + u ; 0 < ß 2 < 1; Y = consumption expenditure; X = income; ß 1 and ß 2 are parameters; ß 1 is intercept and ß 2 is slope coefficients; u is disturbance term or error term. It is a random or stochastic variable May 2004 Prof.VuThieu

Introduction Methodology of Econometrics (4) Obtaining Data (See Table 1.1, page 6) Y= Personal consumption expenditure X= Gross Domestic Product all in Billion US Dollars May 2004 Prof.VuThieu

Introduction Methodology of Econometrics (4) Obtaining Data May 2004 Prof.VuThieu Year X Y 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 2447.1 2476.9 2503.7 2619.4 2746.1 2865.8 2969.1 3052.2 3162.4 3223.3 3260.4 3240.8 3776.3 3843.1 3760.3 3906.6 4148.5 4279.8 4404.5 4539.9 4718.6 4838.0 4877.5 4821.0

Introduction Methodology of Econometrics (5) Estimating the Econometric Model Y^ = - 231.8 + 0.7194 X (1.3.3) MPC was about 0.72 and it means that for the sample period when real income increases 1 USD, led ( on average ) real consumption expenditure increases of about 72 cents Note : A hat symbol (^) above one variable will signify an estimator of the relevant population value May 2004 Prof.VuThieu

Introduction Methodology of Econometrics (6) Hypothesis Testing Are the estimates accord with the expectations of the theory that is being tested? Is MPC < 1 statistically? If so, it may support Keynes’ theory. Confirmation or refutation of economic theories based on sample evidence is object of Statistical Inference (hypothesis testing) May 2004 Prof.VuThieu

Introduction Methodology of Econometrics (7) Forecasting or Prediction With given future value(s) of X, what is the future value(s) of Y? GDP=$6000Bill in 1994, what is the forecast consumption expenditure? Y^= - 231.8+0.7196(6000) = 4084.6 Income Multiplier M = 1/(1 – MPC) (=3.57). decrease (increase) of $1 in investment will eventually lead to $3.57 decrease (increase) in income May 2004 Prof.VuThieu

Introduction Methodology of Econometrics (8) Using model for control or policy purposes Y=4000= -231.8+0.7194 X  X  5882 MPC = 0.72, an income of $5882 Bill will produce an expenditure of $4000 Bill. By fiscal and monetary policy, Government can manipulate the control variable X to get the desired level of target variable Y May 2004 Prof.VuThieu

Introduction Methodology of Econometrics Figure 1.4: Anatomy of economic modelling 1) Economic Theory 2) Mathematical Model of Theory 3) Econometric Model of Theory 4) Data 5) Estimation of Econometric Model 6) Hypothesis Testing 7) Forecasting or Prediction 8) Using the Model for control or policy purposes May 2004 Prof.VuThieu

May 2004 Prof.VuThieu Economic Theory Mathematic Model Econometric Model Data Collection Estimation Hypothesis Testing Forecasting Application in control or policy studies

Basic Econometrics Chapter 1 : THE NATURE OF REGRESSION ANALYSIS May 2004 Prof.VuThieu

1-1. Historical origin of the term “Regression” The term REGRESSION was introduced by Francis Galton Tendency for tall parents to have tall children and for short parents to have short children, but the average height of children born from parents of a given height tended to move (or regress) toward the average height in the population as a whole (F. Galton, “ Family Likeness in Stature ”) May 2004 Prof.VuThieu

1-1. Historical origin of the term “Regression” Galton’s Law was confirmed by Karl Pearson: The average height of sons of a group of tall fathers < their fathers’ height. And the average height of sons of a group of short fathers > their fathers’ height. Thus “regressing” tall and short sons alike toward the average height of all men. (K. Pearson and A. Lee, “ On the law of Inheritance ”) By the words of Galton, this was “ Regression to mediocrity ” May 2004 Prof.VuThieu

1-2. Modern Interpretation of Regression Analysis The modern way in interpretation of Regression: Regression Analysis is concerned with the study of the dependence of one variable ( The Dependent Variable ) , on one or more other variable(s) ( The Explanatory Variable ) , with a view to estimating and/or predicting the (population) mean or average value of the former in term of the known or fixed (in repeated sampling) values of the latter. Examples : (pages 16-19) May 2004 Prof.VuThieu

Dependent Variable Y; Explanatory Variable Xs 1. Y = Son’s Height; X = Father’s Height 2. Y = Height of boys; X = Age of boys 3. Y = Personal Consumption Expenditure X = Personal Disposable Income 4. Y = Demand; X = Price 5. Y = Rate of Change of Wages X = Unemployment Rate 6. Y = Money/Income; X = Inflation Rate 7. Y = % Change in Demand; X = % Change in the advertising budget 8. Y = Crop yield; Xs = temperature, rainfall, sunshine, fertilizer May 2004 Prof.VuThieu

1-3. Statistical vs. Deterministic Relationships In regression analysis we are concerned with STATISTICAL DEPENDENCE among variables (not Functional or Deterministic), we essentially deal with RANDOM or STOCHASTIC variables (with the probability distributions) May 2004 Prof.VuThieu

1-4. Regression vs. Causation: Regression does not necessarily imply causation . A statistical relationship cannot logically imply causation. “A statistical relationship, however strong and however suggestive, can never establish causal connection: our ideas of causation must come from outside statistics, ultimately from some theory or other” ( M.G. Kendal and A. Stuart, “The Advanced Theory of Statistics” ) May 2004 Prof.VuThieu

1-5. Regression vs. Correlation Correlation Analysis: the primary objective is to measure the strength or degree of linear association between two variables (both are assumed to be random) Regression Analysis: we try to estimate or predict the average value of one variable (dependent, and assumed to be stochastic) on the basis of the fixed values of other variables (independent, and non-stochastic) May 2004 Prof.VuThieu

1-6. Terminology and Notation Dependent Variable  Explained Variable  Predictand  Regressand  Response  Endogenous Explanatory Variable(s)  Independent Variable(s)  Predictor(s)  Regressor(s)  Stimulus or control variable(s)  Exogenous(es) May 2004 Prof.VuThieu

1-7. The Nature and Sources of Data for Econometric Analysis 1) Types of Data : Time series data; Cross-sectional data; Pooled data 2) The Sources of Data 3) The Accuracy of Data May 2004 Prof.VuThieu

1-8. Summary and Conclusions 1) The key idea behind regression analysis is the statistic dependence of one variable on one or more other variable(s) 2) The objective of regression analysis is to estimate and/or predict the mean or average value of the dependent variable on basis of known (or fixed) values of explanatory variable(s) May 2004 Prof.VuThieu

1-8. Summary and Conclusions 3) The success of regression depends on the available and appropriate data 4) The researcher should clearly state the sources of the data used in the analysis, their definitions, their methods of collection, any gaps or omissions and any revisions in the data May 2004 Prof.VuThieu

Basic Econometrics Chapter 2 : TWO-VARIABLE REGRESSION ANALYSIS: Some basic Ideas May 2004 Prof.VuThieu

2-1. A Hypothetical Example Total population: 60 families Y=Weekly family consumption expenditure X=Weekly disposable family income 60 families were divided into 10 groups of approximately the same income level ( 80, 100, 120, 140, 160, 180, 200, 220, 240, 260 ) May 2004 Prof.VuThieu

2-1. A Hypothetical Example Table 2-1 gives the conditional distribution of Y on the given values of X Table 2-2 gives the conditional probabilities of Y: p(Y  X) Conditional Mean (or Expectation): E(Y  X=X i ) May 2004 Prof.VuThieu

May 2004 Prof.VuThieu Table 2-2: Weekly family income X ($), and consumption Y ($) X Y 80 100 120 140 160 180 200 220 240 260 Weekly family consumption expenditure Y ($) 55 65 79 80 102 110 120 135 137 150 60 70 84 93 107 115 136 137 145 152 65 74 90 95 110 120 140 140 155 175 70 80 94 103 116 130 144 152 165 178 75 85 98 108 118 135 145 157 175 180 -- 88 -- 113 125 140 -- 160 189 185 -- -- -- 115 -- -- -- 162 -- 191 Total 325 462 445 707 678 750 685 1043 966 1211 Mean 65 77 89 101 113 125 137 149 161 173

2-1. A Hypothetical Example Figure 2-1 shows the population regression line (curve). It is the regression of Y on X Population regression curve is the locus of the conditional means or expectations of the dependent variable for the fixed values of the explanatory variable X (Fig.2-2) May 2004 Prof.VuThieu

2-2. The concepts of population regression function (PRF) E(Y  X=X i ) = f(X i ) is Population Regression Function (PRF) or Population Regression (PR) In the case of linear function we have linear population regression function (or equation or model) E(Y  X=X i ) = f(X i ) = ß 1 + ß 2 X i May 2004 Prof.VuThieu

2-2. The concepts of population regression function (PRF) E(Y  X=X i ) = f(X i ) = ß 1 + ß 2 X i ß 1 and ß 2 are regression coefficients, ß 1 is intercept and ß 2 is slope coefficient Linearity in the Variables Linearity in the Parameters May 2004 Prof.VuThieu

2-4. Stochastic Specification of PRF U i = Y - E(Y  X=X i ) or Y i = E(Y  X=X i ) + U i U i = Stochastic disturbance or stochastic error term. It is nonsystematic component Component E(Y  X=X i ) is systematic or deterministic. It is the mean consumption expenditure of all the families with the same level of income The assumption that the regression line passes through the conditional means of Y implies that E(U i  X i ) = 0 May 2004 Prof.VuThieu

2-5. The Significance of the Stochastic Disturbance Term U i = Stochastic Disturbance Term is a surrogate for all variables that are omitted from the model but they collectively affect Y Many reasons why not include such variables into the model as follows: May 2004 Prof.VuThieu

2-5. The Significance of the Stochastic Disturbance Term Why not include as many as variable into the model (or the reasons for using u i ) + Vagueness of theory + Unavailability of Data + Core Variables vs. Peripheral Variables + Intrinsic randomness in human behavior + Poor proxy variables + Principle of parsimony + Wrong functional form May 2004 Prof.VuThieu

2-6. The Sample Regression Function (SRF) Table 2-4: A random sample from the population Y X ------------------ 70 80 65 100 90 120 95 140 110 160 115 180 120 200 140 220 155 240 150 260 ------------------ Table 2-5: Another random sample from the population Y X ------------------- 55 80 88 100 90 120 80 140 118 160 120 180 145 200 135 220 145 240 175 260 -------------------- May 2004 Prof.VuThieu

May 2004 Prof.VuThieu SRF1 SRF2 Weekly Consumption Expenditure (Y) Weekly Income (X)

2-6. The Sample Regression Function (SRF) Fig.2-3: SRF1 and SRF 2 Y^ i =  ^ 1 +  ^ 2 X i (2.6.1) Y^ i = estimator of E(Y  X i )  ^ 1 = estimator of  1  ^ 2 = estimator of  2 Estimate = A particular numerical value obtained by the estimator in an application SRF in stochastic form: Y i =  ^ 1 +  ^ 2 X i + u^ i or Y i = Y^ i + u^ i (2.6.3) May 2004 Prof.VuThieu

2-6. The Sample Regression Function (SRF) Primary objective in regression analysis is to estimate the PRF Y i =  1 +  2 X i + u i on the basis of the SRF Y i =  ^ 1 +  ^ 2 X i + e i and how to construct SRF so that  ^ 1 close to  1 and  ^ 2 close to  2 as much as possible May 2004 Prof.VuThieu

2-6. The Sample Regression Function (SRF) Population Regression Function PRF Linearity in the parameters Stochastic PRF Stochastic Disturbance Term u i plays a critical role in estimating the PRF Sample of observations from population Stochastic Sample Regression Function SRF used to estimate the PRF May 2004 Prof.VuThieu

2-7. Summary and Conclusions The key concept underlying regression analysis is the concept of the population regression function (PRF). This book deals with linear PRFs: linear in the unknown parameters. They may or may not linear in the variables. May 2004 Prof.VuThieu

2-7. Summary and Conclusions For empirical purposes, it is the stochastic PRF that matters. The stochastic disturbance term u i plays a critical role in estimating the PRF. The PRF is an idealized concept, since in practice one rarely has access to the entire population of interest. Generally, one has a sample of observations from population and use the stochastic sample regression (SRF) to estimate the PRF. May 2004 Prof.VuThieu

Basic Econometrics Chapter 3 : TWO-VARIABLE REGRESSION MODEL: The problem of Estimation May 2004 Prof.VuThieu

3-1. The method of ordinary least square (OLS) Least-square criterion: Minimizing  U^ 2 i =  (Y i – Y^ i ) 2 =  (Y i -  ^ 1 -  ^ 2 X) 2 (3.1.2) Normal Equation and solving it for  ^ 1 and  ^ 2 = Least-square estimators [ See (3.1.6)(3.1.7) ] Numerical and statistical properties of OLS are as follows: May 2004 Prof.VuThieu

3-1. The method of ordinary least square (OLS) OLS estimators are expressed solely in terms of observable quantities. They are point estimators The sample regression line passes through sample means of X and Y The mean value of the estimated Y^ is equal to the mean value of the actual Y: E(Y) = E(Y^) The mean value of the residuals U^ i is zero: E(u^ i )=0 u^ i are uncorrelated with the predicted Y^ i and with X i : That are  u^ i Y^ i = 0;  u^ i X i = 0 May 2004 Prof.VuThieu

3-2. The assumptions underlying the method of least squares Ass 1: Linear regression model (in parameters) Ass 2: X values are fixed in repeated sampling Ass 3: Zero mean value of u i : E(u i  X i )=0 Ass 4: Homoscedasticity or equal variance of u i : Var (u i  X i ) =  2 [VS. Heteroscedasticity] Ass 5: No autocorrelation between the disturbances: Cov(u i ,u j  X i ,X j ) = 0 with i # j [VS. Correlation, + or - ] May 2004 Prof.VuThieu

3-2. The assumptions underlying the method of least squares Ass 6: Zero covariance between u i and X i Cov(u i , X i ) = E(u i , X i ) = 0 Ass 7: The number of observations n must be greater than the number of parameters to be estimated Ass 8: Variability in X values. They must not all be the same Ass 9: The regression model is correctly specified Ass 10: There is no perfect multicollinearity between Xs May 2004 Prof.VuThieu

3-3. Precision or standard errors of least-squares estimates In statistics the precision of an estimate is measured by its standard error (SE) var(  ^ 2 ) =  2 /  x 2 i (3.3.1) se(  ^ 2 ) =  Var(  ^ 2 ) (3.3.2) var(  ^ 1 ) =  2  X 2 i / n  x 2 i (3.3.3) se(  ^ 1 ) =  Var(  ^ 1 ) (3.3.4)  ^ 2 =  u^ 2 i / (n - 2) (3.3.5)  ^ =   ^ 2 is standard error of the estimate May 2004 Prof.VuThieu

3-3. Precision or standard errors of least-squares estimates Features of the variance: + var(  ^ 2 ) is proportional to  2 and inversely proportional to  x 2 i + var(  ^ 1 ) is proportional to  2 and  X 2 i but inversely proportional to  x 2 i and the sample size n. + cov (  ^ 1 ,  ^ 2 ) = - var(  ^ 2 ) shows the independence between  ^ 1 and  ^ 2 May 2004 Prof.VuThieu

3-4. Properties of least-squares estimators: The Gauss-Markov Theorem An OLS estimator is said to be BLUE if : + It is linear , that is, a linear function of a random variable, such as the dependent variable Y in the regression model + It is unbiased , that is, its average or expected value, E(  ^ 2 ), is equal to the true value  2 + It has minimum variance in the class of all such linear unbiased estimators An unbiased estimator with the least variance is known as an efficient estimator May 2004 Prof.VuThieu

3-4. Properties of least-squares estimators: The Gauss-Markov Theorem Gauss- Markov Theorem: Given the assumptions of the classical linear regression model, the least-squares estimators, in class of unbiased linear estimators, have minimum variance, that is, they are BLUE May 2004 Prof.VuThieu

3-5. The coefficient of determination r 2 : A measure of “Goodness of fit” Y i = i + i or Y i - = i - i + i or y i = i + i (Note: = ) Squaring on both side and summing =>  y i 2 = 2  x 2 i +  2 i ; or TSS = ESS + RSS May 2004 Prof.VuThieu

3-5. The coefficient of determination r 2 : A measure of “Goodness of fit” TSS =  y i 2 = Total Sum of Squares ESS =  Y^ i 2 =  ^ 2 2  x 2 i = Explained Sum of Squares RSS =  u^ 2 I = Residual Sum of Squares ESS RSS 1 = -------- + -------- ; or TSS TSS RSS RSS 1 = r 2 + ------- ; or r 2 = 1 - ------- TSS TSS May 2004 Prof.VuThieu

3-5. The coefficient of determination r 2 : A measure of “Goodness of fit” r 2 = ESS/TSS is coefficient of determination, it measures the proportion or percentage of the total variation in Y explained by the regression Model 0  r 2  1; r =  r 2 is sample correlation coefficient Some properties of r May 2004 Prof.VuThieu

3-5. The coefficient of determination r 2 : A measure of “Goodness of fit” 3-6. A numerical Example (pages 80-83) 3-7. Illustrative Examples (pages 83-85) 3-8. Coffee demand Function 3-9. Monte Carlo Experiments (page 85) 3-10. Summary and conclusions (pages 86-87) May 2004 Prof.VuThieu

Basic Econometrics Chapter 4 : THE NORMALITY ASSUMPTION: Classical Normal Linear Regression Model (CNLRM) May 2004 Prof.VuThieu

4-2.The normality assumption CNLR assumes that each u i is distributed normally u i  N(0,  2 ) with: Mean = E(u i ) = 0 Ass 3 Variance = E(u 2 i ) =  2 Ass 4 Cov(u i , u j ) = E(u i , u j ) = 0 (i#j) Ass 5 Note : For two normally distributed variables, the zero covariance or correlation means independence of them, so u i and u j are not only uncorrelated but also independently distributed. Therefore u i  NID(0,  2 ) is Normal and Independently Distributed May 2004 Prof.VuThieu

4-2.The normality assumption Why the normality assumption? With a few exceptions, the distribution of sum of a large number of independent and identically distributed random variables tends to a normal distribution as the number of such variables increases indefinitely If the number of variables is not very large or they are not strictly independent, their sum may still be normally distributed May 2004 Prof.VuThieu

4-2.The normality assumption Why the normality assumption? Under the normality assumption for u i , the OLS estimators  ^ 1 and  ^ 2 are also normally distributed The normal distribution is a comparatively simple distribution involving only two parameters (mean and variance) May 2004 Prof.VuThieu

4-3. Properties of OLS estimators under the normality assumption With the normality assumption the OLS estimators  ^ 1 ,  ^ 2 and  ^ 2 have the following properties: 1. They are unbiased 2. They have minimum variance. Combined 1 and 2, they are efficient estimators 3. Consistency, that is, as the sample size increases indefinitely, the estimators converge to their true population values May 2004 Prof.VuThieu

4-3. Properties of OLS estimators under the normality assumption 4.  ^ 1 is normally distributed  N(  1 ,  ^  1 2 ) And Z = (  ^ 1 -  1 )/  ^  1 is  N(0,1) 5.  ^ 2 is normally distributed  N(  2 ,  ^  2 2 ) And Z = (  ^ 2 -  2 )/  ^  2 is  N(0,1) 6. (n-2)  ^ 2 /  2 is distributed as the  2 (n-2) May 2004 Prof.VuThieu

4-3. Properties of OLS estimators under the normality assumption 7.  ^ 1 and  ^ 2 are distributed independently of  ^ 2 . They have minimum variance in the entire class of unbiased estimators, whether linear or not. They are best unbiased estimators (BUE) 8. Let u i is  N(0,  2 ) then Y i is  N[E(Y i ); Var(Y i )] = N[  1 +  2 X i ;  2 ] May 2004 Prof.VuThieu

Some last points of chapter 4 4-4. The method of Maximum likelihood (ML) ML is point estimation method with some stronger theoretical properties than OLS (Appendix 4.A on pages 110-114) The estimators of coefficients  ’s by OLS and ML are identical. They are true estimators of the  ’s (ML estimator of  2 ) =  u^ i 2 /n (is biased estimator) (OLS estimator of  2 ) =  u^ i 2 /n-2 (is unbiased estimator) When sample size (n) gets larger the two estimators tend to be equal May 2004 Prof.VuThieu

Some last points of chapter 4 4-5. Probability distributions related to the Normal Distribution: The t,  2 , and F distributions See section (4.5) on pages 107-108 with 8 theorems and Appendix A, on pages 755-776 4-6. Summary and Conclusions See 10 conclusions on pages 109-110 May 2004 Prof.VuThieu

Basic Econometrics Chapter 5 : TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-1. Statistical Prerequisites S ee Appendix A with key concepts such as probability, probability distributions, Type I Error, Type II Error,level of significance, power of a statistic test, and confidence interval May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-2. Interval estimation: Some basic Ideas How “ close ” is, say,  ^ 2 to  2 ? Pr (  ^ 2 -    2   ^ 2 +  ) = 1 -  (5.2.1) Random interval  ^ 2 -    2   ^ 2 +  if exits, it known as confidence interval  ^ 2 -  is lower confidence limit  ^ 2 +  is upper confidence limit May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-2. Interval estimation: Some basic Ideas (1 -  ) is confidence coefficient, 0 <  < 1 is significance level Equation (5.2.1) does not mean that the Pr of  2 lying between the given limits is (1 -  ), but the Pr of constructing an interval that contains  2 is (1 -  ) (  ^ 2 -  ,  ^ 2 +  ) is random interval May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-2. Interval estimation: Some basic Ideas In repeated sampling, the intervals will enclose, in (1 -  )*100 of the cases, the true value of the parameters For a specific sample, can not say that the probability is (1 -  ) that a given fixed interval includes the true  2 If the sampling or probability distributions of the estimators are known, one can make confidence interval statement like (5.2.1) May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-3. Confidence Intervals for Regression Coefficients Z= (  ^ 2 -  2 )/se(  ^ 2 ) = (  ^ 2 -  2 )  x 2 i /  ~N(0,1) (5.3.1) We did not know  and have to use  ^ instead, so: t= (  ^ 2 -  2 )/se(  ^ 2 ) = (  ^ 2 -  2 )  x 2 i /  ^ ~ t(n-2) (5.3.2) => Interval for  2 Pr [ -t  /2  t  t  /2 ] = 1-  (5.3.3) May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-3. Confidence Intervals for Regression Coefficients Or confidence interval for  2 is Pr [  ^ 2 -t  /2 se(  ^ 2 )   2   ^ 2 +t  /2 se(  ^ 2 )] = 1-  (5.3.5) Confidence Interval for  1 Pr [  ^ 1 -t  /2 se(  ^ 1 )   1   ^ 1 +t  /2 se(  ^ 1 )] = 1-  (5.3.7) May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-4. Confidence Intervals for  2 Pr [(n-2)  ^ 2 /  2  /2   2  (n-2)  ^ 2 /  2 1-  /2 ] = 1-  (5.4.3) The interpretation of this interval is: If we establish (1-  ) confidence limits on  2 and if we maintain a priori that these limits will include true  2 , we shall be right in the long run (1-  ) percent of the time May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-5. Hypothesis Testing: General Comments The stated hypothesis is known as the null hypothesis: H o The H o is tested against and alternative hypothesis: H 1 5-6. Hypothesis Testing: The confidence interval approach One-sided or one-tail Test H 0 :  2   * versus H 1 :  2 >  * May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing Two-sided or two-tail Test H 0 :  2 =  * versus H 1 :  2 #  *  ^ 2 - t  /2 se(  ^ 2 )   2   ^ 2 + t  /2 se(  ^ 2 ) values of  2 lying in this interval are plausible under H o with 100*(1-  )% confidence. If  2 lies in this region we do not reject H o (the finding is statistically insignificant ) If  2 falls outside this interval, we reject H o (the finding is statistically significant ) May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-7. Hypothesis Testing: The test of significance approach A test of significance is a procedure by which sample results are used to verify the truth or falsity of a null hypothesis Testing the significance of regression coefficient: The t-test Pr [  ^ 2 -t  /2 se(  ^ 2 )   2   ^ 2 +t  /2 se(  ^ 2 )]= 1-  (5.7.2) May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-7. Hypothesis Testing: The test of significance approach Table 5-1: Decision Rule for t-test of significance May 2004 Prof.VuThieu Type of Hypothesis H 0 H 1 Reject H 0 if Two-tail  2 =  2 *  2 #  2 * |t| > t  /2,df Right-tail  2   2 *  2 >  2 * t > t  ,df Left-tail  2   2 *  2 <  2 * t < - t  ,df

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-7. Hypothesis Testing: The test of significance approach Testing the significance of  2 : The  2 Test Under the Normality assumption we have:  ^ 2  2 = (n-2) ------- ~  2 (n-2) (5.4.1)  2 From (5.4.2) and (5.4.3) on page 520 => May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-7. Hypothesis Testing: The test of significance approach Table 5-2: A summary of the  2 Test May 2004 Prof.VuThieu H 0 H 1 Reject H 0 if  2 =  2 0  2 >  2 0 Df.(  ^ 2 )/  2 0 >  2  ,df  2 =  2 0  2 <  2 0 Df.(  ^ 2 )/  2 0 <  2 ( 1-  ),df  2 =  2 0  2 #  2 0 Df.(  ^ 2 )/  2 0 >  2  /2,df or <  2 ( 1-  /2), df

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-8. Hypothesis Testing: Some practical aspects 1) The meaning of “Accepting” or “Rejecting” a Hypothesis 2) The Null Hypothesis and the Rule of Thumb 3) Forming the Null and Alternative Hypotheses 4) Choosing  , the Level of Significance May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-8. Hypothesis Testing: Some practical aspects 5) The Exact Level of Significance: The p-Value [ See page 132 ] 6) Statistical Significance versus Practical Significance 7) The Choice between Confidence- Interval and Test-of-Significance Approaches to Hypothesis Testing [Warning: Read carefully pages 117-134 ] May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-9. Regression Analysis and Analysis of Variance TSS = ESS + RSS F=[MSS of ESS] /[MSS of RSS] = =  2 ^ 2  x i 2 /  ^ 2 (5.9.1) If u i are normally distributed; H 0 :  2 = 0 then F follows the F distribution with 1 and n-2 degree of freedom May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-9. Regression Analysis and Analysis of Variance F provides a test statistic to test the null hypothesis that true  2 is zero by compare this F ratio with the F-critical obtained from F tables at the chosen level of significance, or obtain the p-value of the computed F statistic to make decision May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-9. Regression Analysis and Analysis of Variance Table 5-3. ANOVA for two-variable regression model May 2004 Prof.VuThieu Source of Variation Sum of square ( SS) Degree of Freedom - (Df) Mean sum of square ( MSS) ESS (due to regression)  y^ i 2 =  2 ^ 2  x i 2 1  2 ^ 2  x i 2 RSS (due to residuals)  u^ i 2 n-2  u^ i 2 /(n-2)=  ^ 2 TSS  y i 2 n-1

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-10. Application of Regression Analysis: Problem of Prediction By the data of Table 3-2, we obtained the sample regression (3.6.2) : Y^ i = 24.4545 + 0.5091X i , where Y^ i is the estimator of true E(Y i ) There are two kinds of prediction as follows: May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-10. Application of Regression Analysis: Problem of Prediction Mean prediction : Prediction of the conditional mean value of Y corresponding to a chosen X, say X 0 , that is the point on the population regression line itself (see pages 137-138 for details) Individual prediction : Prediction of an individual Y value corresponding to X 0 (see pages 138-139 for details) May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-11. Reporting the results of regression analysis An illustration: Y^ I = 24.4545 + 0.5091X i (5.1.1) Se = (6.4138) (0.0357) r 2 = 0.9621 t = (3.8128) (14.2405) df= 8 P = (0.002517) (0.000000289) F 1,2 =2202.87 May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-12. Evaluating the results of regression analysis: Normality Test: The Chi-Square (  2 ) Goodness of fit Test  2 N-1-k =  (O i – E i ) 2 /E i (5.12.1) O i is observed residuals (u^ i ) in interval i E i is expected residuals in interval i N is number of classes or groups; k is number of parameters to be estimated. If p-value of obtaining  2 N-1-k is high (or  2 N-1-k is small) => The Normality Hypothesis can not be rejected May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-12. Evaluating the results of regression analysis: Normality Test: The Chi-Square (  2 ) Goodness of fit Test H 0 : u i is normally distributed H 1 : u i is un-normally distributed Calculated-  2 N-1-k =  (O i – E i ) 2 /E i (5.12.1) Decision rule: Calculated-  2 N-1-k > Critical-  2 N-1-k then H 0 can be rejected May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-12. Evaluating the results of regression analysis: The Jarque-Bera (JB) test of normality This test first computes the Skewness (S) and Kurtosis (K) and uses the following statistic: JB = n [S 2 /6 + (K-3) 2 /24] (5.12.2) Mean= x bar =  x i /n ; SD 2 =  (x i - x bar ) 2 /(n-1) S=m 3 /m 2 3/2 ; K=m 4 /m 2 2 ; m k =  (x i - x bar ) k /n May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-12. (Continued) Under the null hypothesis H 0 that the residuals are normally distributed Jarque and Bera show that in large sample (asymptotically) the JB statistic given in (5.12.12) follows the Chi-Square distribution with 2 df. If the p-value of the computed Chi-Square statistic in an application is sufficiently low, one can reject the hypothesis that the residuals are normally distributed. But if p-value is reasonable high, one does not reject the normality assumption. May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-13. Summary and Conclusions 1. Estimation and Hypothesis testing constitute the two main branches of classical statistics 2. Hypothesis testing answers this question: Is a given finding compatible with a stated hypothesis or not? 3. There are two mutually complementary approaches to answering the preceding question: Confidence interval and test of significance. May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-13. Summary and Conclusions 4. Confidence-interval approach has a specified probability of including within its limits the true value of the unknown parameter. If the null-hypothesized value lies in the confidence interval, H 0 is not rejected, whereas if it lies outside this interval, H 0 can be rejected May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-13. Summary and Conclusions 5. Significance test procedure develops a test statistic which follows a well-defined probability distribution (like normal, t, F, or Chi-square). Once a test statistic is computed, its p-value can be easily obtained. The p-value The p-value of a test is the lowest significance level, at which we would reject H 0 . It gives exact probability of obtaining the estimated test statistic under H 0 . If p-value is small, one can reject H 0 , but if it is large one may not reject H 0 . May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-13. Summary and Conclusions 6. Type I error is the error of rejecting a true hypothesis. Type II error is the error of accepting a false hypothesis. In practice, one should be careful in fixing the level of significance  , the probability of committing a type I error (at arbitrary values such as 1%, 5%, 10%). It is better to quote the p-value of the test statistic. May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-13. Summary and Conclusions 7. This chapter introduced the normality test to find out whether u i follows the normal distribution. Since in small samples, the t, F,and Chi-square tests require the normality assumption, it is important that this assumption be checked formally May 2004 Prof.VuThieu

Chapter 5 TWO-VARIABLE REGRESSION: Interval Estimation and Hypothesis Testing 5-13. Summary and Conclusions (ended) 8. If the model is deemed practically adequate, it may be used for forecasting purposes. But should not go too far out of the sample range of the regressor values. Otherwise, forecasting errors can increase dramatically. May 2004 Prof.VuThieu

Basic Econometrics Chapter 6 EXTENSIONS OF THE TWO-VARIABLE LINEAR REGRESSION MODEL May 2004 Prof.VuThieu

Chapter 6 EXTENSIONS OF THE TWO-VARIABLE LINEAR REGRESSION MODELS 6-1. Regression through the origin The SRF form of regression: Y i =  ^ 2 X i + u^ i (6.1.5)   Comparison two types of regressions: * Regression through-origin model and * Regression with intercept   May 2004 Prof.VuThieu

Chapter 6 EXTENSIONS OF THE TWO-VARIABLE LINEAR REGRESSION MODELS 6-1. Regression through the origin Comparison two types of regressions:  ^ 2 =  X i Y i /  X 2 i (6.1.6) O   ^ 2 =  x i y i /  x 2 i (3.1.6) I var(  ^ 2 ) =  2 /  X 2 i (6.1.7) O var(  ^ 2 ) =  2 /  x 2 i (3.3.1) I  ^ 2 =  u^ i ) 2 /(n-1) (6.1.8) O   ^ 2 =  u^ i ) 2 /(n-2) (3.3.5) I  May 2004 Prof.VuThieu

Chapter 6 EXTENSIONS OF THE TWO-VARIABLE LINEAR REGRESSION MODELS 6-1. Regression through the origin r 2 for regression through-origin model Raw r 2 = (  X i Y i ) 2 /  X 2 i  Y 2 i (6.1.9 ) Note: Without very strong a priory expectation, well advise is sticking to the conventional, intercept-present model. If intercept equals to zero statistically, for practical purposes we have a regression through the origin. If in fact there is an intercept in the model but we insist on fitting a regression through the origin, we would be committing a specification error May 2004 Prof.VuThieu

Chapter 6 EXTENSIONS OF THE TWO-VARIABLE LINEAR REGRESSION MODELS 6-1. Regression through the origin Illustrative Examples: 1) Capital Asset Pricing Model - CAPM (page 156) 2) Market Model (page 157) 3) The Characteristic Line of Portfolio Theory (page 159) May 2004 Prof.VuThieu

Chapter 6 EXTENSIONS OF THE TWO-VARIABLE LINEAR REGRESSION MODELS 6-2. Scaling and units of measurement Let Y i =  ^ 1 +  ^ 2 X i + u^ i (6.2.1) Define Y* i =w 1 Y i and X* i =w 2 X i then:  ^ 2 = (w 1 /w 2 )  ^ 2 (6.2.15)  ^ 1 = w 1  ^ 1 (6.2.16)  *^ 2 = w 1 2  ^ 2 (6.2.17) Var(  ^ 1 ) = w 2 1 Var(  ^ 1 ) (6.2.18) Var(  ^ 2 ) = (w 1 /w 2 ) 2 Var(  ^ 2 ) (6.2.19) r 2 xy = r 2 x*y* (6.2.20) May 2004 Prof.VuThieu

Chapter 6 EXTENSIONS OF THE TWO-VARIABLE LINEAR REGRESSION MODELS 6-2. Scaling and units of measurement From one scale of measurement, one can derive the results based on another scale of measurement. If w 1 = w 2 the intercept and standard error are both multiplied by w 1 . If w 2 =1 and scale of Y changed by w 1 , then all coefficients and standard errors are all multiplied by w 1 . If w 1 =1 and scale of X changed by w 2 , then only slope coefficient and its standard error are multiplied by 1/w 2 . Transformation from (Y,X) to (Y*,X*) scale does not affect the properties of OLS Estimators A numerical example : (pages 161, 163-165) May 2004 Prof.VuThieu

6-3. Functional form of regression model The log-linear model Semi-log model Reciprocal model May 2004 Prof.VuThieu

6-4. How to measure elasticity The log-linear model Exponential regression model: Y i =  1 X i   e u i (6.4.1) By taking log to the base e of both side: lnY i = ln  1 +  2 lnX i + u i , by setting ln  1 =   lnY i =  +  2 lnX i + u i (6.4.3) (log-log, or double-log, or log-linear model) This can be estimated by OLS by letting Y* i =  +  2 X* i + u i , where Y* i =lnY i , X* i =lnX i ;  2 measures the ELASTICITY of Y respect to X, that is, percentage change in Y for a given (small) percentage change in X. May 2004 Prof.VuThieu

6-4. How to measure elasticity The log-linear model The elasticity E of a variable Y with respect to variable X is defined as: E=dY/dX=(% change in Y)/(% change in X) ~ [(  Y/Y) x 100] / [(  X/X) x100]= = (  Y/  X)x (X/Y) = slope x (X/Y) An illustrative example : The coffee demand function (pages 167-168) May 2004 Prof.VuThieu

6-5. Semi-log model : Log-lin and Lin-log Models How to measure the growth rate: The log-lin model Y t = Y 0 (1+r) t (6.5.1) lnY t = lnY 0 + t ln(1+r) (6.5.2) lnY t =    +  2 t , called constant growth model (6.5.5) where  1 = lnY 0 ;  2 = ln(1+r) lnY t =    +  2 t + u i (6.5.6) It is Semi-log model, or log-lin model. The slope coefficient measures the constant proportional or relative change in Y for a given absolute change in the value of the regressor (t)  2 = (Relative change in regressand)/(Absolute change in regressor) (6.5.7) May 2004 Prof.VuThieu

6-5. Semi-log model : Log-lin and Lin-log Models Instantaneous Vs. compound rate of growth  2 is instantaneous rate of growth antilog(  2 ) – 1 is compound rate of growth The linear trend model Y t =    +  2 t + u t (6.5.9) If  2 >  there is an upward trend in Y If  2 <  there is an downward trend in Y Note: (i) Cannot compare the r 2 values of models (6.5.5) and (6.5.9) because the regressands in the two models are different, (ii) Such models may be appropriate only if a time series is stationary. May 2004 Prof.VuThieu

6-5. Semi-log model : Log-lin and Lin-log Models The lin-log model: Y i =  1 +  2 lnX i + u i  (6.5.11)   2 = (Change in Y) / Change in lnX = (Change in Y)/(Relative change in X) ~ (  Y)/(  X/X) (6.5.12) or  Y =  2 (  X/X) (6.5.13) That is, the absolute change in Y equal to  2 times the relative change in X. May 2004 Prof.VuThieu

6-6. Reciprocal Models : Log-lin and Lin-log Models The reciprocal model: Y i =  1 +  2 ( 1/X i ) + u i   (6.5.14) As X increases definitely, the term  2 ( 1/X i ) approaches to zero and Y i approaches the limiting or asymptotic value  1 ( See figure 6.5 in page 174 ) An Illustrative example : The Phillips Curve for the United Kingdom 1950-1966  May 2004 Prof.VuThieu

6-7. Summary of Functional Forms Table 6.5 (page 178) May 2004 Prof.VuThieu Model Equation Slope = dY/dX Elasticity = (dY/dX).(X/Y) Linear Y =      X     (X/Y) */ Log-linear (log-log) lnY =      lnX    (Y  X)   Log-lin lnY =      X    Y     X */ Lin-log Y =      lnX  2 (1/X)    Y) */ Reciprocal Y =      X) -  2 (1/X 2 ) -    XY) */

6-7. Summary of Functional Forms Note : */ indicates that the elasticity coefficient is variable, depending on the value taken by X or Y or both. when no X and Y values are specified, in practice, very often these elasticities are measured at the mean values E(X) and E(Y). ----------------------------------------------- 6 -8. A note on the stochastic error term 6-9. Summary and conclusions (pages 179-180) May 2004 Prof.VuThieu

Basic Econometrics Chapter 7 MULTIPLE REGRESSION ANALYSIS: The Problem of Estimation May 2004 Prof.VuThieu

7-1. The three-Variable Model: Notation and Assumptions Y i = ß 1 + ß 2 X 2i + ß 3 X 3i + u i (7.1.1) ß 2 , ß 3 are partial regression coefficients With the following assumptions: + Zero mean value of U i: : E(u i |X 2i ,X 3i ) = 0.  i (7.1.2) + No serial correlation: Cov(u i ,u j ) = 0,  i # j (7.1.3) + Homoscedasticity: Var(u i ) =  2 (7.1.4) + Cov(u i ,X 2i ) = Cov(u i ,X 3i ) = 0 (7.1.5) + No specification bias or model correct specified (7.1.6) + No exact collinearity between X variables (7.1.7) (no multicollinearity in the cases of more explanatory vars. If there is linear relationship exits, X vars. Are said to be linearly dependent) + Model is linear in parameters May 2004 Prof.VuThieu

7-2. Interpretation of Multiple Regression E(Y i | X 2i ,X 3i ) = ß 1 + ß 2 X 2i + ß 3 X 3i (7.2.1) (7.2.1) gives conditional mean or expected value of Y conditional upon the given or fixed value of the X 2 and X 3 May 2004 Prof.VuThieu

7-3. The meaning of partial regression coefficients Y i = ß 1 + ß 2 X 2i + ß 3 X 3 +….+ ß s X s + u i ß k measures the change in the mean value of Y per unit change in X k , holding the rest explanatory variables constant. It gives the “direct” effect of unit change in X k on the E(Y i ), net of X j (j # k) How to control the “true” effect of a unit change in X k on Y? (read pages 195-197) May 2004 Prof.VuThieu

7-4. OLS and ML estimation of the partial regression coefficients This section (pages 197-201) provides: 1. The OLS estimators in the case of three-variable regression Y i = ß 1 + ß 2 X 2i + ß 3 X 3 + u i 2. Variances and standard errors of OLS estimators 3. 8 properties of OLS estimators (pp 199-201) 4. Understanding on ML estimators May 2004 Prof.VuThieu

7-5. The multiple coefficient of determination R 2 and the multiple coefficient of correlation R This section provides: 1. Definition of R 2 in the context of multiple regression like r 2 in the case of two-variable regression 2. R =  R 2 is the coefficient of multiple regression, it measures the degree of association between Y and all the explanatory variables jointly 3. Variance of a partial regression coefficient Var(ß^ k ) =  2 /  x 2 k (1/(1-R 2 k )) (7.5.6) Where ß^ k is the partial regression coefficient of regressor X k and R 2 k is the R 2 in the regression of X k on the rest regressors May 2004 Prof.VuThieu

7-6. Example 7.1: The expectations-augmented Philips Curve for the US (1970-1982) This section provides an illustration for the ideas introduced in the chapter Regression Model (7.6.1) Data set is in Table 7.1 May 2004 Prof.VuThieu

7-7. Simple regression in the context of multiple regression: Introduction to specification bias This section provides an understanding on “ Simple regression in the context of multiple regression”. It will cause the specification bias which will be discussed in Chapter 13 May 2004 Prof.VuThieu

7-8. R 2 and the Adjusted-R 2 R 2 is a non-decreasing function of the number of explanatory variables. An additional X variable will not decrease R 2 R 2 = ESS/TSS = 1- RSS/TSS = 1-  u^ 2 I /  y^ 2 i (7.8.1) This will make the wrong direction by adding more irrelevant variables into the regression and give an idea for an adjusted-R 2 (R bar ) by taking account of degree of freedom R 2 bar = 1- [  u^ 2 I /(n-k)] / [  y^ 2 i /(n-1) ] , or (7.8.2) R 2 bar = 1-  ^ 2 / S 2 Y (S 2 Y is sample variance of Y) K= number of parameters including intercept term By substituting (7.8.1) into (7.8.2) we get R 2 bar = 1- (1-R 2 ) (n-1)/(n- k) (7.8.4) For k > 1, R 2 bar < R 2 thus when number of X variables increases R 2 bar increases less than R 2 and R 2 bar can be negative May 2004 Prof.VuThieu

7-8. R 2 and the Adjusted-R 2 Comparing Two R 2 Values: To compare, the size n and the dependent variable must be the same Example 7-2: Coffee Demand Function Revisited (page 210) The “game” of maximizing adjusted-R 2 : Choosing the model that gives the highest R 2 bar may be dangerous, for in regression our objective is not for that but for obtaining the dependable estimates of the true population regression coefficients and draw statistical inferences about them Should be more concerned about the logical or theoretical relevance of the explanatory variables to the dependent variable and their statistical significance May 2004 Prof.VuThieu

7-9. Partial Correlation Coefficients This section provides: 1. Explanation of simple and partial correlation coefficients 2. Interpretation of simple and partial correlation coefficients (pages 211-214) May 2004 Prof.VuThieu

7-10. Example 7.3: The Cobb-Douglas Production function More on functional form Y i =  1 X  2 2i X  3 3i e U i (7.10.1) By log-transform of this model: lnY i = ln  1 +  2 ln X 2i +  3 ln X 3i + U i =  0 +  2 ln X 2i +  3 ln X 3i + U i (7.10.2) Data set is in Table 7.3 Report of results is in page 216 May 2004 Prof.VuThieu

7-11 Polynomial Regression Models Y i =  0 +  1 X i +  2 X 2 i +…+  k X k i + U i (7.11.3) Example 7.4: Estimating the Total Cost Function Data set is in Table 7.4 Empirical results is in page 221 -------------------------------------------------------------- 7-12. Summary and Conclusions (page 221) May 2004 Prof.VuThieu

Basic Econometrics Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-3. Hypothesis testing in multiple regression: Testing hypotheses about an individual partial regression coefficient Testing the overall significance of the estimated multiple regression model, that is, finding out if all the partial slope coefficients are simultaneously equal to zero Testing that two or more coefficients are equal to one another Testing that the partial regression coefficients satisfy certain restrictions Testing the stability of the estimated regression model over time or in different cross-sectional units Testing the functional form of regression models May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-4. Hypothesis testing about individual partial regression coefficients With the assumption that u i ~ N(0,  2 ) we can use t-test to test a hypothesis about any individual partial regression coefficient. H 0 :  2 = 0 H 1 :  2  0 If the computed t value > critical t value at the chosen level of significance, we may reject the null hypothesis; otherwise, we may not reject it May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-5. Testing the overall significance of a multiple regression: The F-Test For Y i =  1 +  2 X 2i +  3 X 3i + ........+  k X ki + u i To test the hypothesis H 0 :  2 =  3 =....=  k = 0 ( all slope coefficients are simultaneously zero ) versus H 1 : Not at all slope coefficients are simultaneously zero, compute F=(ESS/df)/(RSS/df)=(ESS/(k-1))/(RSS/(n-k)) (8.5.7) ( k = total number of parameters to be estimated including intercept ) If F > F critical = F  (k-1,n-k), reject H 0 Otherwise you do not reject it May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-5. Testing the overall significance of a multiple regression Alternatively, if the p-value of F obtained from (8.5.7) is sufficiently low, one can reject H 0 An important relationship between R 2 and F: F=(ESS/(k-1))/(RSS/(n-k)) or R 2 /(k-1) F = ---------------- (8.5.1) (1-R 2 ) / (n-k) ( see prove on page 249) May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-5. Testing the overall significance of a multiple regression in terms of R 2 For Y i =  1 +  2 X 2i +  3 X 3i + ........+  k X ki + u i To test the hypothesis H 0 :  2 =  3 = .....=  k = 0 ( all slope coefficients are simultaneously zero ) versus H 1 : Not at all slope coefficients are simultaneously zero, compute F = [R 2 /(k-1)] / [(1-R 2 ) / (n-k)] (8.5.13) ( k = total number of parameters to be estimated including intercept ) If F > F critical = F  (k-1,n-k), reject H 0 May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-5. Testing the overall significance of a multiple regression Alternatively, if the p-value of F obtained from (8.5.13) is sufficiently low, one can reject H 0 The “Incremental” or “Marginal” contribution of an explanatory variable: Let  X is the new (additional) term in the right hand of a regression. Under the usual assumption of the normality of u i and the H O :  = 0, it can be shown that the following F ratio will follow the F distribution with respectively degree of freedom May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-5. Testing the overall significance of a multiple regression [R 2 new - R 2 old ] / Df 1 F com = ---------------------- (8.5.18) [1 - R 2 new ] / Df 2 Where Df 1 = number of new regressors Df 2 = n – number of parameters in the new model R 2 new is standing for coefficient of determination of the new regression (by adding  X); R 2 old is standing for coefficient of determination of the old regression (before adding  X). May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-5. Testing the overall significance of a multiple regression Decision Rule: If F com > F  Df1 , Df2 one can reject the Ho that  = 0 and conclude that the addition of X to the model significantly increases ESS and hence the R 2 value When to Add a New Variable? If |t| of coefficient of X > 1 (or F= t 2 of that variable exceeds 1) When to Add a Group of Variables? If adding a group of variables to the model will give F value greater than 1; May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-6. Testing the equality of two regression coefficients Y i =  1 +  2 X 2i +  3 X 3i +  4 X 4i + u i (8.6.1) Test the hypotheses: H 0 :  3 =  4 or  3 -  4 = 0 (8.6.2) H 1 :  3   4 or  3 -  4  0 Under the classical assumption it can be shown: t = [(  ^ 3 -  ^ 4 ) – (  3 -  4 )] / se(  ^ 3 -  ^ 4 ) follows the t distribution with (n-4) df because (8.6.1) is a four-variable model or, more generally, with (n-k) df. where k is the total number of parameters estimated, including intercept term. se(  ^ 3 -  ^ 4 ) =  [var( (  ^ 3 ) + var(  ^ 4 ) – 2cov (  ^ 3 ,  ^ 4 )] (8.6.4) (see appendix) May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference t = (  ^ 3 -  ^ 4 ) /  [var( (  ^ 3 ) + var(  ^ 4 ) – 2cov (  ^ 3 ,  ^ 4 )] (8.6.5) Steps for testing : 1. Estimate  ^ 3 and  ^ 4 2. Compute se(  ^ 3 -  ^ 4 ) through (8.6.4) 3. Obtain t- ratio from (8.6.5) with H 0 :  3 =  4 4. If t-computed > t-critical at designated level of significance for given df, then reject H 0 . Otherwise do not reject it. Alternatively, if the p-value of t statistic from (8.6.5) is reasonable low, one can reject H 0 . Example 8.2: The cubic cost function revisited May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-7. Restricted least square: Testing linear equality restrictions Y i =  1 X   2 2i X   3 3i e u i (7.10.1) and (8.7.1) Y = output X 2 = labor input X 3 = capital input In the log-form: lnY i =  0 +  2 lnX 2i +  3 lnX 3i + u i (8.7.2) with the constant return to scale:  2 +  3 = 1 (8.7.3) May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-7 . Restricted least square: Testing linear equality restrictions How to test (8.7.3) The t Test approach (unrestricted): test of the hypothesis H 0 :  2 +  3 = 1 can be conducted by t- test: t = [(  ^ 2 +  ^ 3 ) – (  2 +  3 )] / se(  ^ 2 -  ^ 3 ) (8.7.4) The F Test approach (restricted least square -RLS): Using, say,  2 = 1-  3 and substitute it into (8.7.2) we get: ln(Y i /X 2i ) =  0 +  3 ln(X 3i /X 2i ) + u i (8.7.8). Where (Y i /X 2i ) is output/labor ratio, and (X 3i / X 2i ) is capital/labor ratio May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-7. Restricted least square: Testing linear equality restrictions  u^ 2 UR =RSS UR of unrestricted regression (8.7.2) and  u^ 2 R = RSS R of restricted regression (8.7.7), m = number of linear restrictions, k = number of parameters in the unrestricted regression, n = number of observations. R 2 UR and R 2 R are R 2 values obtained from unrestricted and restricted regressions respectively. Then F=[(RSS R – RSS UR )/m]/[RSS UR /(n-k)] = = [(R 2 UR – R 2 R ) / m] / [1 – R 2 UR / (n-k)] (8.7.10) follows F distribution with m, (n-k) df . Decision rule: If F > F m, n-k , reject H 0 :  2 +  3 = 1 May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-7. Restricted least square: Testing linear equality restrictions  Note: R 2 UR  R 2 R (8.7.11)  and  u^ 2 UR   u^ 2 R (8.7.12) Example 8.3 : The Cobb-Douglas Production function for Taiwanese Agricultural Sector, 1958-1972. (pages 259-260). Data in Table 7.3 (page 216) General F Testing (page 260) Example 8.4 : The demand for chicken in the US, 1960-1982. Data in exercise 7.23 (page 228) May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-8. Comparing two regressions: Testing for structural stability of regression models Table 8.8: Personal savings and income data, UK, 1946-1963 (millions of pounds) Savings function: Reconstruction period: Y t =  1 +  2 X t + U 1t (t = 1,2,...,n 1 ) Post-Reconstruction period: Y t =  1 +  2 X t + U 2t (t = 1,2,...,n 2 ) Where Y is personal savings, X is personal income, the u s are disturbance terms in the two equations and n 1 , n 2 are the number of observations in the two period May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-8. Comparing two regressions: Testing for structural stability of regression models + The structural change may mean that the two intercept are different, or the two slopes are different, or both are different, or any other suitable combination of the parameters. If there is no structural change we can combine all the n 1 , n 2 and just estimate one savings function as: Y t =  1 +  2 X t + U t (t = 1,2,...,n 1 , 1,....n 2 ). (8.8.3) How do we find out whether there is a structural change in the savings-income relationship between the two period? A popular test is Chow-Test, it is simply the F Test discussed earlier H O :  i =  i  i Vs H 1 :  i that  i   i May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-8. Comparing two regressions: Testing for structural stability of regression models + The assumptions underlying the Chow test u 1t and u 2t ~ N(0,s 2 ), two error terms are normally distributed with the same variance u 1t and u 2t are independently distributed Step 1 : Estimate (8.8.3), get RSS, say, S 1 with df = (n 1 +n 2 – k); k is number of parameters estimated ) Step 2 : Estimate (8.8.1) and (8.8.2) individually and get their RSS, say, S 2 and S 3 , with df = (n 1 – k) and (n 2 -k) respectively. Call S 4 = S 2 +S 3 ; with df = (n 1 +n 2 – 2k) Step 3 : S 5 = S 1 – S 4 ; May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-8. Comparing two regressions: Testing for structural stability of regression models Step 4 : Given the assumptions of the Chow Test, it can be show that F = [S 5 / k] / [S 4 / (n 1 +n 2 – 2k)] (8.8.4) follows the F distribution with Df = (k, n 1 +n 2 – 2k) Decision Rule : If F computed by (8.8.4) > F- critical at the chosen level of significance a => reject the hypothesis that the regression (8.8.1) and (8.8.2) are the same, or reject the hypothesis of structural stability; One can use p-value of the F obtained from (8.8.4) to reject H 0 if p-value low reasonably. + Apply for the data in Table 8.8 May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-9. Testing the functional form of regression: Choosing between linear and log-linear regression models: MWD Test (MacKinnon, White and Davidson) H 0 : Linear Model Y is a linear function of regressors, the X s ; H 1 : Log-linear Model Y is a linear function of logs of regressors, the lnX s ; May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference 8-9. Testing the functional form of regression: Step 1 : Estimate the linear model and obtain the estimated Y values. Call them Yf (i.e.,Y^). Take lnYf. Step 2 : Estimate the log-linear model and obtain the estimated lnY values, call them lnf (i.e., ln^Y ) Step 3 : Obtain Z 1 = (lnYf – lnf) Step 4 : Regress Y on X s and Z 1 . Reject H 0 if the coefficient of Z 1 is statistically significant, by the usual t - test Step 5 : Obtain Z 2 = antilog of (lnf – Yf) Step 6 : Regress lnY on lnX s and Z 2 . Reject H 1 if the coefficient of Z 2 is statistically significant, by the usual t-test May 2004 Prof.VuThieu

Chapter 8 MULTIPLE REGRESSION ANALYSIS: The Problem of Inference Example 8.5 : The demand for Roses (page 266-267). Data in exercise 7.20 (page 225) 8-10. Prediction with multiple regression Follow the section 5-10 and the illustration in pages 267-268 by using data set in the Table 8.1 (page 241) 8-11. The troika of hypothesis tests: The likelihood ratio (LR), Wald (W) and Lagarange Multiplier (LM) Tests 8-12. Summary and Conclusions May 2004 Prof.VuThieu

Eco Basic 1 8

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Eco Basic 1 8