1 / 27

Multiple Regression Applications III - PowerPoint PPT Presentation

Multiple Regression Applications III. Lecture 18. Dummy variables. Include qualitative indicators into the regression: e.g. gender, race, regime shifts. So far, have only seen the change in the intercept for the regression line.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

PowerPoint Slideshow about 'Multiple Regression Applications III' - aisha

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

Multiple Regression Applications III

Lecture 18

• Include qualitative indicators into the regression: e.g. gender, race, regime shifts.

• So far, have only seen the change in the intercept for the regression line.

• Suppose now we wish to investigate if the slope changes as well as the intercept.

• This can be written as a general equation:

Wi = a + b1Agei + b2Marriedi + b3Di + b4(Di*Agei) + b5(Di*Marriedi) + ei

• Suppose first we wish to test for the difference between males and females.

• For females and males separately, the model would be:

Wi = a + b1Agei + b2Marriedi + e

• in so doing we argue thatwould be different for males and females

• we want to think about two sub-sample groups: males and females

• we can test the hypothesis that the intercept and partial slope coefficients will be different for these 2 groups

• To test our hypothesis we’ll estimate the regression equation above (Wi = a + b1Agei + b2Marriedi + e) for the whole sample and then for the two sub-sample groups

• We test to see if our estimated coefficients are the same between males and females

• Our null hypothesis is:

H0 : aM, b1M, b2M = aF, b1F, b2F

• We have an unrestricted form and a restricted form

• unrestricted: used when we estimate for the sub-sample groups separately

• restricted: used when we estimate for the whole sample

• What type of statistic will we use to carry out this test?

• F-statistic:

q = k, the number of parameters in the model

n = n1 + n2 where n is complete sample size

• The sum of squared residuals for the unrestricted form will be:

SSRU = SSRM + SSRF

• L17_2.xls

• the data is sorted according to the dummy variable “female”

• there is a second dummy variable for marital status

• there are 3 estimated regression equations, one each for the total sample, male sub-sample, and female sub-sample

• The output allows us to gather the necessary sum of squared residuals and sample sizes to construct the test statistic:

• Since F0.05,3, 27 = 2.96 > F* we cannot reject the null hypothesis that the partial slope coefficients are the same for males and females

Interactive terms (6)

Irene O. Wong:

• What if F* > F0.05,3, 27 ? How to read the results?

• There’s a difference between the two sub-samples and therefore we should estimate the wage equations separately

• Or we could interact the dummy variables with the other variables

• To interact the dummy variables with the age and marital status variables, we multiply the dummy variable by the age and marital status variables to get:

Wt = a + b1Agei + b2Marriedi + b3Di + b4(Di*Agei) + b5(Di*Marriedi) + ei

• Using L17_2.xls you can construct the interactive terms by multiplying the FEMALE column by the AGE and MARRIED columns

• one way to see if the two sub-samples are different, look at the t-ratios on the interactive terms

• in this example, neither of the t-ratios are statistically significant so we can’t reject the null hypothesis

• If we want to estimate the equation for the first sub-sample (males) we take the expectation of the wage equation where the dummy variable for female takes the value of zero:

E(Wt|Di = 0) = a + b1Agei + b2Marriedi

• We can do the same for the second sub-sample (Females)

E(Wt|Di = 1) = (a + b3) + (b1 + b4)Agei + (b2 + b3)Marriedi

• We can see that by using only one regression equation, we have allowed the intercept and partial slope coefficients to vary by sub-sample

• Phillips curve as an example of a regime shift.

• Data points from 1950 - 1970: There is a downward sloping, reciprocal relationship between wage inflation and unemployment

W

UN

• But if we look at data points from 1971 - 1996:

• From the data we can detect an upward sloping relationship

• ALWAYS graph the data between the 2 main variables of interest

W

UN

• There seems to be a regime shift between the two periods

• note: this is an arbitrary choice of regime shift - it was not dictated by a specific change

• We will use the Chow Test (F-test) to test for this regime shift

• the test will use a restricted form:

• it will also use an unrestricted form:

• D is the dummy variable for the regime shift, equal to 0 for 1950-1970 and 1 for 1971-1996

• L17_3.xls estimates the restricted regression equations and calculates the F-statistic for the Chow Test:

• The null hypothesis will be:

H0 : b1 = b3 = 0

• we are testing to see if the dummy variable for the regime shift alters the intercept or the slope coefficient

• The F-statistic is (* indicates restricted)

Where q=2

• The expectation of wage inflation for the first time period:

• The expectation of wage inflation for the second time period:

• You can use the spreadsheet data to carry out these calculations

Relaxing Assumptions

Lecture 18

• A review of what we have learned in regression so far and a look forward to what we will happen when we relax assumptions around the regression line

• Introduction to new concepts:

• Heteroskedasticity

• Serial correlation (also known as autocorrelation)

• Non-independence of independent variables

• Calculating the linear regression model (using OLS)

• Use of the sum of square residuals: calculate the variance for the regression line and the mean squared deviation

• Hypothesis tests: t-tests, F-tests, c2 test.

• Coefficient of determination (R2) and the adjustment.

• Modeling: use of log-linear, logs, reciprocal.

• Relationship between F and R2

• Imposing linear restrictions: e.g. H0: b2 = b3 = 0 (q = 2); H0: a + b = 1.

• Dummy variables and interactions; Chow test.

• What are the assumptions we have used throughout?

• Two assumptions about the population for the bi-variate case: 1. E(Y|X) = a + bX (the conditional expectation function is linear); 2. V(Y|X) = (conditional variances are constant)

• Assumptions concerning the sampling procedure (i= 1..n) 1. Values of Xi (not all equal) are prespecified; 2. Yi is drawn from the subpopulation having X = Xi; 3. Yi ‘s are independent.

• Consequences are: 1. E(Yi) = a + bXi; 2. V(Yi) = s2; 3. C(Yh, Yi) = 0

• How can we test to see if these assumptions don’t hold?

• What can we do if the assumptions don’t hold?

• We would like our estimates to be BLUE

• We need to look out for three potential violations of the CLRM assumptions: heteroskedasticity, autocorrelation, and non-independence of X (or simultaneity bias).

• Heteroskedasticity: usually found in cross-section data (and longitudinal)

• In earlier lectures, we saw that the variance of is

• This is an example of homoskedasticity, where the variance is constant

X1

X2

X3

Homoskedasticity (2)

• Homoskedasticity can be illustrated like this:

Y

constant

variance around

the regression line

• But, we don’t always have constant variance s2

• We may have a variance that varies with each observation, or

• When there is heteroskedasticty, the variance around the regression line varies with the values of X

• The non-constant variance around the regression line can be drawn like this:

Y

X

X1

X2

X3

• Serial correlation can be found in time series data (and longitudinal data)

• Under serial correlation, we have covariance terms

• where Yi and Yh are correlated or each Yi is not independently drawn

• This results in nonzero covariance terms

• Example: We can think of this using time series data such that unemployment at time t is related to unemployment in the previous time period t-1

• If we have a model with unemployment as the dependent variable Yt then

• Yt and Yt-1 are related

• et and et-1 are also related

• The non-independence of independent variables is the third violation of the ordinary least squares assumptions

• Remember from the OLS derivation that we minimized the sum of the squared residuals

• we needed independence between the X variable and the error term

• if not, the values of X are not pre-specified

• without independence, the estimates are biased

• Heteroskedasticity and serial correlation

• make the estimates inefficient

• therefore makes the estimated standard errors incorrect

• Non-independence of independent variables

• makes estimates biased

• instrumental variables and simultaneous equations are used to deal with this third type of violation

• Starting next lecture we’ll take a more in-depth look at the three violations of the CLRM assumptions