Introductory Econometrics: Chapter 2 Slides

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  • REVISION MULTIPLE REGRESSION

    Econometrics I - Exercise 2

    Lubomr Cingl

    March 4, 2014

  • REVISION MULTIPLE REGRESSION

    REVISION

    I Simple regression modelI

    y = 0 + 1x + u (1)

    I y dependent / explainedI x independent / explanatory / controlI u error termI 0 interceptI 1 slopeI we want to know the relationship

  • REVISION MULTIPLE REGRESSION

    ASSUMPTIONS

    I Average value of u in population is 0: E(u) = 0I not very restrictive

    I Zero conditional meanI knowing sth about x does not give any info about uI E(u|x) = E(u) = 0 which implies E(y|x) = 0 + 1x

  • REVISION MULTIPLE REGRESSION

    E(y|x) as a linear function of x;distribution of y centered aroundexp. value

  • REVISION MULTIPLE REGRESSION

    ESTIMATION FROM A SAMPLE

    I we have a random sample of observationsI for each observation holds:I Sample regression lineI

    yi = 0 + 1xi + ui (2)

    I we want best estimates of parametersI 0, 1I 3 ways how to find them: MoM, OLS, ML

  • REVISION MULTIPLE REGRESSION

    FORMULAS TO KNOW

    I intercept0 = y 1x (3)

    I slope

    1 =

    ni=1

    (xi x)(yi y)n

    i=1(xi x)2

    (4)

    ifn

    i=1(xi x)2 > 0

    I 1 is sample covar bw x and y div by variance of xI If x and y are positively correlated, the slope positive

  • REVISION MULTIPLE REGRESSION

    SAMPLE REGRESSION LINE

    I e.g. inc = 0.33 + 0.56edu

  • REVISION MULTIPLE REGRESSION

    R SQUARED

    I each observation can be made up by explained andunexplained part

    I yi = yi + uiI we can define following:I

    (yi y)2 is total sum of squares (Var of y)I

    (yi y)2 explained sum of squaresI

    (ui)2 residual sum of squaresI SST = SSE + SSRI R2 = SSE/SST = 1 SSR/SST

  • REVISION MULTIPLE REGRESSION

    PROPERTIES OF OLS ESTIMATOR

    I UnbiasedI expected value of estimator is its true valueI 1 = 1 +

    (xix)ui(xix)2 = 1

    I VarianceI Assume homoskedasticity Var(u|x) = 2I 2 is the error varianceI Var1 = 2/

    (xi x) = 2/sx2

    I We also have to estimate 2I 2 = 1/(n 2) u12 = SSR/(n 2)I Var1 = 2/

    (xi x) = 1/(n 2)

    u1

    2/sx2

  • REVISION MULTIPLE REGRESSION

    EXAMPLE 2.7

    Consider the savings function

    sav = 0 + 1inc + u,u =

    inc e (5)

    where e is a random variable with E(e) = 0 and Var(e) = 2e .Assume that e is independent of inc.

    1. Show that E(u|inc) = 0, so that the key zero conditionalmean assumption is satisfied. [Hint: if e independent ofinc, then E(e|inc) = E(e) and Var(e|inc) = Var(e)]

    2. Show that Var(u|inc) = 2e inc.3. Provide a discussion that supports the assumption that the

    variance of savings increases with family income.

  • REVISION MULTIPLE REGRESSION

    INTERPRETATION

    linear functionI y = 0 + 1xI marginal effect of x on y

    natural logI y = log(x); x > 0I good to know: log(1 + x) x for x 0 andI log(x1) log(x0) (x1 x0)/x0 = x/x0I therefore log(x) 100 %x

    constant elasticity modelI log(y) = 0 + 1log(x); y, x > 0I 1 is elasticity of y w.r.t. x

  • REVISION MULTIPLE REGRESSION

    EXAMPLE 2.6

    Using data from 1988 for houses sold close to garbage mill,following equation relates housing price to distance fromgarbage incinerator:

    log( price) = 9.4 + 0.312log(dist); n = 135; R2 = 0.162 (6)

    1. Interpret the coefficient on log(dist). Is the sign of thisestimate what you expect it to be?

    2. Do you think simple regression provides an unbiasedestimator of the ceteris paribus elasticity of price withrespect to dist? (Think about the citys decision on where toput the incinerator.)

    3. What other factors about a house affect its price? Mightthese be correlated with distance from the incinerator?

  • REVISION MULTIPLE REGRESSION

    REGRESSION THROUGH ORIGIN

    restriction: x = 0 if y = 0I then we estimate y = 1xI we know that

    1 =

    ni=1

    (xi x)(yi y)n

    i=1(xi x)2

    (7)

    if we set x = 0, y = 0 we get possibly biased estimator:

    1 =

    ni=1

    (xi)(yi)

    ni=1

    (xi)2(8)

  • REVISION MULTIPLE REGRESSION

    EXAMPLE 2.8

    Consider standard simple regression model with standardassumptions met. Let 1 be the estimator of 1 obtained byassuming the intercept is zero.

    1. Find E(1) in terms of the xi, 0, and 1. Verify that 1 isunbiased for 1 when the population intercept 0 is zero.Are there other cases where 1 is unbiased?

    2. Find the variance of 1. Hint: It does not depend on 0.3. Show that Var(1) Var(1). Hint:

    (xi)2

    (xi x)2

    with strict inequality unless x = 0.4. Comment on the trade-off between bias and variance

    when choosing between 1 and 1.

  • REVISION MULTIPLE REGRESSION

    EXAMPLE 2.9

    1. Let 0 and 1 be the intercept and slope from the regressionof yi on xi, using n observations. Let c1 and c2, with c2 6= 0,be constants. Let 0 and 1 be the intercept and slope fromthe regression c1yi on c2xi. Show that 1 = (c1/c2)1 and0 = c10, thereby verifying the claims on units ofmeasurement in Section 2.4. [Hint: To obtain 1, 0 plugthe scaled versions of x and y into the OLS formulas.]

    2. Now let and 0 and 1 be from the regression (c1 + yi) on(c2 + xi) (with no restriction on c1 or c2). Show that 1 = 1and 0 = 0 + c1 c21.

  • REVISION MULTIPLE REGRESSION

    EXAMPLE 2.9

    3. Now let and 0 and 1 be the OLS estimates from theregression log(yi) on (xi), y > 0. For c1 > 0 let 0 and 1 be theintercept and slope from the regression of log(c1yi) on (xi).Show that 1 = 1 and 0 = 0 + log(c1).4. Now, assuming that xi > 0, let 0 and 1 be the intercept andslope from the regression of yi on log(c2xi). How do 0 and 1compare with the intercept and slope from the regression of yion log(xi)?

  • REVISION MULTIPLE REGRESSION

    Multiple regression

  • REVISION MULTIPLE REGRESSION

    MULTIPLE REGRESSION MODEL

    I paralels with simple reg model

    y = 0 + 1x1 + 2x2 + ...+ u (9)

    I y dependent / explainedI x independent / explanatory / controlI u error termI similar assumptions about the error termI 0 interceptI linear - linear in parametersI 1,2, ... slopesI Interpretation: y = 1x1I ceteris paribus: holding other factors constant

  • REVISION MULTIPLE REGRESSION

    EXAMPLE I

    I effect of education on wage

    wage = 0 + 1educ + 2exper + u (10)

    I we take exper out from error termI ceteris paribus: holding experience constant, we can

    measure the effect of education on wageI partial effect

  • REVISION MULTIPLE REGRESSION

    EXAMPLE II

    I effect of income on consumption

    cons = 0 + 1inc + 2(inc)2 + u (11)

    I same way of getting parametersI interpretation different: alone inc non-sense

    consinc

    1 + 22inc

    I marginal effect of income on consumption depends onboth terms

  • REVISION MULTIPLE REGRESSION

    ASSUMPTIONS

    I Expected value of u is 0: E(u) = 0I Zero conditional mean (ASS3)

    I knowing sth about x does not give any info about uI E(u|x1, x2, ..., xk) = 0I all unobserved factors in error term uncorrelated with all xi

    I No perfect collinearity (ASS4)I no independent variable is constantI no exact linear relationship among them holdsI xs can be correlated, but not perfectly

    I we need more observations than parameters: n k + 1

  • REVISION MULTIPLE REGRESSION

    ESTIMATION FROM A SAMPLE

    I we have a random sample of observations (ASS2)I for each observation holds (ASS1):

    yi = 0 + 1xi1 + 2xi2 + ...+ kxik + ui (12)

    I we want best estimates of parametersI 0, 1, ...I 3 ways how to find them: MoM, OLS, MLI we will get the estimates Sample regression ft

    yi = 0 + 1xi2 + 2xi2 + ...+ kxik (13)

  • REVISION MULTIPLE REGRESSION

    PROPERTIES

    I if ASS1 thru ASS4 hold, then our OLS estimator isunbiased

    I i.e. the procedure of getting the estimate is unbiased

    I if we add irrelevant variableI no effect on parameters of relevant variables - still unbiasedI slight increase in R2

    I if we omit important variableI OLS will be biased, usuallyI omitted variable bias

  • REVISION MULTIPLE REGRESSION

    OMMITED VARIABLE BIAS

    y = 0 + 1x1 + 2x2 + u

    y = 0 + 1x1

    E(1) = 1 + 2

    (xi1 x1)xi2(xi1 x1)2 = 1 + 21

  • REVISION MULTIPLE REGRESSION

    VARIANCE

    I Assume homoskedasticity Var(u|x1, x2...xk) = 2I variance of u same for all combinations of outcomes of xs

    Var(j) =2

    (xij xj)(1 R2j )=

    2

    SSTj(1 R2j )(14)

    I R2j is R2 from the regression of xj on all other xs

    I size importantI We also have to estimate 2

    I 2 = 1/(n k 1) u12 = SSR/dfI Varj =

    2

    SSTj(1R2j )I sej =

    SSTj(1R2j )

  • REVISION MULTIPLE REGRESSION

    EXAMPLE 3.9

    The following equation describes the median housing price in acommunity in terms of amount of pollution (nox for nitrousoxide) and the average number of rooms in houses in thecommunity (rooms):

    log(price) = 0 + 1log(nox) + 2rooms + u

    1. What are the probable signs of the regression slopes?Interpret 1, explain.

    2. Why might nox [more precisely, log(nox)] and rooms benegatively correlated? If this is the case, does the simpleregression of log(price) on log(nox) produce an upward ordownward biased estimator of 1?

  • REVISION MULTIPLE REGRESSION

    EXAMPLE 3.9

    3. Using the data in HPRICE2.RAW, the following equationswere estimated:

    log( price) = 11.71 1.043log(nox),n = 506,R2 = 0.264

    log( price) = 9.230.718log(nox)+0.306rooms,n = 506,R2 = 0.514Is the relationship between the simple and multiple regressionestimates of the elasticity of price with respect to nox what youwould have predicted, given your answer in part 2.? Does thismean that -0.718 is definitely closer to the true elasticity than-1.043?

  • REVISION MULTIPLE REGRESSION

    EXAMPLE 3.12

    1. Consider the simple regression model y = 0 + 1x + u underthe first four Gauss-Markov assumptions. For some functiong(x), for example g(x) = x2 or g(x) = log(1 + x2) definezi = g(xi). Define a slope estimator as

    1 =

    (zi z)yi/

    (zi z)xi

    Show that 1 is linear and unbiased. Remember, becauseE(u|x) = 0, you can treat both xi and zi as nonrandom in yourderivation.2. Add the homoskedasticity assumption, MLR.5. Show that

    Var(1) = (

    (zi z)2)/(

    (zi z)xi)2

  • REVISION MULTIPLE REGRESSION

    EXAMPLE 3.12

    3. Show directly that, under the Gauss-Markovassumptions,Var(1) Var(1), where 1 is the OLS estimator.[Hint: The Cauchy-Schwartz inequality in Appendix B impliesthat

    (n1

    (zi z)(xi x))2 (n1

    (zi z)2)(n1

    (xi x)2)

    notice that we can drop x from the sample covariance.]

  • REVISION MULTIPLE REGRESSION

    EXAMPLE 3.15

    The file CEOSAL2.RAW contains data on 177 chief executiveofficers, which can be used to examine the effects of firmperformance on CEO salary.

    1. Estimate a model relating annual salary to firm sales andmarket value. Make the model of the constant elasticityvariety for both independent variables. Write the resultsout in equation form.

    2. Add profits to the model from part 1. Why can thisvariable not be included in logarithmic form? Would yousay that these firm performance variables explain most ofthe variation in CEO salaries?

  • REVISION MULTIPLE REGRESSION

    EXAMPLE 3.15

    3. Add the variable ceoten to the model in part 2. What is theestimated percentage return for another year of CEO tenure,holding other factors fixed?4. Find the sample correlation coefficient between the variableslog(mktval) and profits. Are these variables highly correlated?What does this say about the OLS estimators?

    RevisionRevisionMeasures of fit

    Multiple Regression

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