# OLS in Matrix Form. Let y be an n 1 vector of observations on the dependent variable.

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1 OLS in Matrix Form 1 The True Model Let X be an n k matrix where we have observations on k independent variables for n observations Since our model will usually contain a constant term, one of the columns in the X matrix will contain only ones This column should be treated exactly the same as any other column in the X matrix Let y be an n 1 vector of observations on the dependent variable Let ɛ be an n 1 vector of disturbances or errors Let β be an k 1 vector of unknown population parameters that we want to estimate Our statistical model will essentially look something like the following: Y 1 Y 2 Y n n 1 This can be rewritten more simply as: 1 X 11 X 21 X k1 1 X 12 X 22 X k2 = 1 X 1n X 2n X kn n k β 1 β 2 β n k 1 ɛ 1 ɛ 2 + ɛ n n 1 y = Xβ + ɛ (1) This is assumed to be an accurate reflection of the real world The model has a systematic component (Xβ) and a stochastic component (ɛ) Our goal is to obtain estimates of the population parameters in the β vector 2 Criteria for Estimates Our estimates of the population parameters are referred to as ˆβ Recall that the criteria we use for obtaining our estimates is to find the estimator ˆβ that minimizes the sum of squared residuals ( e 2 i in scalar notation)1 Why this criteria? Where does this criteria come from? The vector of residuals e is given by: e = y X ˆβ (2) 1 Make sure that you are always careful about distinguishing between disturbances (ɛ) that refer to things that cannot be observed and residuals (e) that can be observed It is important to remember that ɛ e 1

2 The sum of squared residuals (RSS) is e e 2 [ e1 e 2 e n ] 1 n e 1 e 2 e n n 1 = [ e 1 e 1 + e 2 e e n e n ] 1 1 (3) It should be obvious that we can write the sum of squared residuals as: e e = (y X ˆβ) (y X ˆβ) = y y ˆβ X y y X ˆβ + ˆβ X X ˆβ = y y 2 ˆβ X y + ˆβ X X ˆβ (4) where this development uses the fact that the transpose of a scalar is the scalar ie y X ˆβ = (y X ˆβ) = ˆβ X y To find the ˆβ that minimizes the sum of squared residuals, we need to take the derivative of Eq 4 with respect to ˆβ This gives us the following equation: e e ˆβ = 2X y + 2X X ˆβ = 0 (5) To check this is a minimum, we would take the derivative of this with respect to ˆβ again this gives us 2X X It is easy to see that, so long as X has full rank, this is a positive definite matrix (analogous to a positive real number) and hence a minimum 3 2 It is important to note that this is very different from ee the variance-covariance matrix of residuals 3 Here is a brief overview of matrix differentiaton when a and b are K 1 vectors a b b b Ab b = b a b = a (6) = 2Ab = 2b A (7) when A is any symmetric matrix Note that you can write the derivative as either 2Ab or 2b A and 2β X y b β X Xβ b = 2β (X y) b = β Aβ b = 2X y (8) = 2Aβ = 2X Xβ (9) when X X is a K K matrix For more information, see Greene (2003, ) and Gujarati (2003, 925) 2

3 From Eq 5 we get what are called the normal equations (X X) ˆβ = X y (10) Two things to note about the (X X) matrix First, it is always square since it is k k Second, it is always symmetric Recall that (X X) and X y are known from our data but ˆβ is unknown If the inverse of (X X) exists (ie (X X) 1 ), then pre-multiplying both sides by this inverse gives us the following equation: 4 (X X) 1 (X X) ˆβ = (X X) 1 X y (11) We know that by definition, (X X) 1 (X X) = I, where I in this case is a k k identity matrix This gives us: I ˆβ = (X X) 1 X y ˆβ = (X X) 1 X y (12) Note that we have not had to make any assumptions to get this far! Since the OLS estimators in the ˆβ vector are a linear combination of existing random variables (X and y), they themselves are random variables with certain straightforward properties 3 Properties of the OLS Estimators The primary property of OLS estimators is that they satisfy the criteria of minimizing the sum of squared residuals However, there are other properties These properties do not depend on any assumptions - they will always be true so long as we compute them in the manner just shown Recall the normal form equations from earlier in Eq 10 Now substitute in y = X ˆβ + e to get (X X) ˆβ = X y (13) (X X) ˆβ = X (X ˆβ + e) (X X) ˆβ = (X X) ˆβ + X e X e = 0 (14) 4 The inverse of (X X) may not exist If this is the case, then this matrix is called non-invertible or singular and is said to be of less than full rank There are two possible reasons why this matrix might be non-invertible One, based on a trivial theorem about rank, is that n < k ie we have more independent variables than observations This is unlikely to be a problem for us in practice The other is that one or more of the independent variables are a linear combination of the other variables ie perfect multicollinearity 3

4 What does X e look like? X 11 X 12 X 1n X 21 X 22 X 2n X k1 X k2 X kn e 1 e 2 e n From X e = 0, we can derive a number of properties X 11 e 1 + X 12 e X 1n e n X 21 e 1 + X 22 e X 2n e n = X k1 e 1 + X k2 e X kn e n 0 0 = 0 (15) 1 The observed values of X are uncorrelated with the residuals X e = 0 implies that for every column x k of X, x ke = 0 In other words, each regressor has zero sample correlation with the residuals Note that this does not mean that X is uncorrelated with the disturbances; we ll have to assume this If our regression includes a constant, then the following properties also hold 2 The sum of the residuals is zero If there is a constant, then the first column in X (ie X 1 ) will be a column of ones This means that for the first element in the X e vector (ie X 11 e 1 + X 12 e X 1n e n ) to be zero, it must be the case that e i = 0 3 The sample mean of the residuals is zero This follows straightforwardly from the previous property ie e = ei n = 0 4 The regression hyperplane passes through the means of the observed values (X and y) This follows from the fact that e = 0 Recall that e = y X ˆβ Dividing by the number of observations, we get e = y x ˆβ = 0 This implies that y = x ˆβ This shows that the regression hyperplane goes through the point of means of the data 5 The predicted values of y are uncorrelated with the residuals The predicted values of y are equal to X ˆβ ie ŷ = X ˆβ From this we have This last development takes account of the fact that X e = 0 ŷ e = (X ˆβ) e = b X e = 0 (16) 6 The mean of the predicted Y s for the sample will equal the mean of the observed Y s ie ŷ = y 4

5 These properties always hold true You should be careful not to infer anything from the residuals about the disturbances For example, you cannot infer that the sum of the disturbances is zero or that the mean of the disturbances is zero just because this is true of the residuals - this is true of the residuals just because we decided to minimize the sum of squared residuals Note that we know nothing about ˆβ except that it satisfies all of the properties discussed above We need to make some assumptions about the true model in order to make any inferences regarding β (the true population parameters) from ˆβ (our estimator of the true parameters) Recall that ˆβ comes from our sample, but we want to learn about the true parameters 4 The Gauss-Markov Assumptions 1 y = Xβ + ɛ This assumption states that there is a linear relationship between y and X 2 X is an n k matrix of full rank This assumption states that there is no perfect multicollinearity In other words, the columns of X are linearly independent This assumption is known as the identification condition 3 E[ɛ X] = 0 E ɛ 1 X ɛ 2 X ɛ n X = E(ɛ 1 ) E(ɛ 2 ) E(ɛ n ) = (17) This assumption - the zero conditional mean assumption - states that the disturbances average out to 0 for any value of X Put differently, no observations of the independent variables convey any information about the expected value of the disturbance The assumption implies that E(y) = Xβ This is important since it essentially says that we get the mean function right 4 E(ɛɛ X) = σ 2 I This captures the familiar assumption of homoskedasticity and no autocorrelation To see why, start with the following: ɛ 1 X E(ɛɛ ɛ 2 X [ X) = E ɛ1 X ɛ 2 X ɛ n X] ] (18) ɛ n X 5

6 which is the same as: which is the same as: E(ɛɛ X) = E E(ɛɛ X) = ɛ 2 1 X ɛ 1ɛ 2 X ɛ 1 ɛ n X ɛ 2 ɛ 1 X ɛ 2 2 X ɛ 2ɛ n X ɛ n ɛ 1 X ɛ n ɛ 2 X ɛ 2 n X E[ɛ 2 1 X] E[ɛ 1ɛ 2 X] E[ɛ 1 ɛ n X] E[ɛ 2 ɛ 1 X] E[ɛ 2 2 X] E[ɛ 2ɛ n X] E[ɛ n ɛ 1 X] E[ɛ n ɛ 2 X] E[ɛ 2 n X] (19) (20) The assumption of homoskedasticity states that the variance of ɛ i is the same (σ 2 ) for all i ie var[ɛ i X] = σ 2 i The assumption of no autocorrelation (uncorrelated errors) means that cov(ɛ i, ɛ j X) = 0 i j ie knowing something about the disturbance term for one observation tells us nothing about the disturbance term for any other observation With these assumptions, we have: E(ɛɛ X) = σ σ σ 2 Finally, this can be rewritten as: E(ɛɛ X) = σ = σ2 I (22) Disturbances that meet the two assumptions of homoskedasticity and no autocorrelation are referred to as spherical disturbances We can compactly write the Gauss-Markov assumptions about the disturbances as: (21) Ω = σ 2 I (23) where Ω is the variance-covariance matrix of the disturbances ie Ω = E[ɛɛ ] 5 X may be fixed or random, but must be generated by a mechanism that is unrelated to ɛ 6 ɛ X N[0, σ 2 I] This assumption is not actually required for the Gauss-Markov Theorem However, we often assume it to make hypothesis testing easier The Central Limit Theorem is typically evoked to justify this assumption 6

7 5 The Gauss-Markov Theorem The Gauss-Markov Theorem states that, conditional on assumptions 1-5, there will be no other linear and unbiased estimator of the β coefficients that has a smaller sampling variance In other words, the OLS estimator is the Best Linear, Unbiased and Efficient estimator (BLUE) How do we know this? Proof that ˆβ is an unbiased estimator of β We know from earlier that ˆβ = (X X) 1 X y and that y = Xβ + ɛ This means that ˆβ = (X X) 1 X (Xβ + ɛ) ˆβ = β + (X X) 1 X ɛ (24) since (X X) 1 X X = I This shows immediately that OLS is unbiased so long as either (i) X is fixed (non-stochastic) so that we have: E[ ˆβ] = E[β] + E[(X X) 1 X ɛ] = β + (X X) 1 X E[ɛ] (25) where E[ɛ] = 0 by assumption or (ii) X is stochastic but independent of ɛ so that we have: where E(X ɛ) = 0 Proof that ˆβ is a linear estimator of β From Eq 24, we have: E[ ˆβ] = E[β] + E[(X X) 1 X ɛ] = β + (X X) 1 E[X ɛ] (26) ˆβ = β + (X X) 1 X ɛ (27) Since we can write ˆβ = β + Aɛ where A = (X X) 1 X, we can see that ˆβ is a linear function of the disturbances By the definition that we use, this makes it a linear estimator (See Greene (2003, 45) Proof that ˆβ has minimal variance among all linear and unbiased estimators See Greene (2003, 46-47) 7

8 6 The Variance-Covariance Matrix of the OLS Estimates We can derive the variance-covariance matrix of the OLS estimator, ˆβ E[( ˆβ β)( ˆβ β) ] = E[((X X) 1 X ɛ)((x X) 1 X ɛ) ] = E[(X X) 1 X ɛɛ X(X X) 1 ] (28) where we take advantage of the fact that (AB) = B A ie we can rewrite (X X) 1 X ɛ as ɛ X(X X) 1 If we assume that X is non-stochastic, we get: E[( ˆβ β)( ˆβ β) ] = (X X) 1 X E[ɛɛ ]X(X X) 1 (29) From Eq 22, we have E[ɛɛ ] = σ 2 I Thus, we have: We estimate σ 2 with ˆσ 2, where: E[( ˆβ β)( ˆβ β) ] = (X X) 1 X (σ 2 I)X(X X) 1 = σ 2 I(X X) 1 X X(X X) 1 = σ 2 (X X) 1 (30) ˆσ 2 = e e n k (31) To see the derivation of this, see Greene (2003, 49) What does the variance-covariance matrix of the OLS estimator look like? var( ˆβ 1 ) cov( ˆβ 1, ˆβ 2 ) cov( ˆβ 1, ˆβ k ) E[( ˆβ β)( ˆβ β) cov( ˆβ 2, ˆβ 1 ) var( ˆβ 2 ) cov( ˆβ 2, ˆβ k ) ] = cov( ˆβ k, ˆβ 1 ) cov( ˆβ k, ˆβ 2 ) var( ˆβ k ) (32) As you can see, the standard errors of the ˆβ are given by the square root of the elements along the main diagonal of this matrix 61 Hypothesis Testing Recall Assumption 6 from earlier, which stated that ɛ X N[0, σ 2 I] I had stated that this assumption was not necessary for the Gauss-Markov Theorem but was crucial for testing inferences about ˆβ Why? Without this assumption, we know nothing about the distribution of ˆβ How does this assumption about the distribution of the disturbances tell us anything about the distribution of ˆβ? Well, we just saw in Eq 27 that the OLS estimator is just a linear function of the disturbances By assuming that the disturbances have a multivariate normal distribution ie ɛ N[0, σ 2 I] (33) 8

9 we are also saying that the OLS estimator is also distributed multivariate normal ie ˆβ N[β, σ 2 (X X) 1 ] (34) but where the mean is β and the variance is σ 2 (X X) 1 It is this that allows us to conduct the normal hypothesis tests that we are familiar with 7 Robust (Huber of White) Standard Errors Recall from Eq 29 that we have: var cov( ˆβ) = (X X) 1 X E[ɛɛ ]X(X X) 1 = (X X) 1 (X ΩX)(X X) 1 (35) This helps us to make sense of White s heteroskedasticity consistent standard errors 5 Recall that heteroskedasticity does not cause problems for estimating the coefficients; it only causes problems for getting the correct standard errors We can compute ˆβ without making any assumptions about the disturbances ie ˆβOLS = (X X) 1 X y However, to get the results of the Gauss Markov Theorem (things like E[ ˆβ] = β etc) and to be able to conduct hypothesis tests ( ˆβ N[β, σ 2 (X X) 1 ]), we need to make assumptions about the disturbances One of the assumptions is that E[ee ] = σ 2 I This assumption includes the assumption of homoskedasticity var[ɛ i X] = σ 2 i However, it is not always the case that the variance will be the same for all observations ie we have σi 2 instead of σ 2 Basically, there may be many reasons why we are better at predicting some observations than others Recall the variance-covariance matrix of the disturbance terms from earlier: E[ɛ 2 1 X] E[ɛ 1ɛ 2 X] E[ɛ 1 ɛ n X] E(ɛɛ E[ɛ 2 ɛ 1 X] E[ɛ 2 2 X) = Ω = X] E[ɛ 2ɛ n X] (36) E[ɛ n ɛ 1 X] E[ɛ n ɛ 2 X] E[ɛ 2 n X] If we retain the assumption of no autocorrelation, this can be rewritten as: σ E(ɛɛ 0 σ2 2 0 X) = Ω = 0 0 σn 2 (37) Basically, the main diagonal contains n variances of ɛ i The assumption of homoskedasticity states that each of these n variances are the same ie σi 2 = σ 2 But this is not always an appropriate 5 As we ll see later in the semester, it also helps us make sense of Beck and Katz s panel-corrected standard errors 9

10 assumption to make Our OLS standard errors will be incorrect insofar as: X E[ɛɛ ]X σ 2 (X X) (38) Note that our OLS standard errors may be too big or too small So, what can we do if we suspect that there is heteroskedasticity? Essentially, there are two options 1 Weighted Least Squares: To solve the problem, we just need to find something that is proportional to the variance We might not know the variance for each observation, but if we know something about where it comes from, then we might know something that is proportional to it In effect, we try to model the variance Note that this only solves the problem of heteroskedasticity if we assume that we have modelled the variance correctly - we never know if this is true or not 2 Robust standard errors (White 1980): This method treats heteroskedasticity as a nuisance rather than something to be modelled How do robust standard errors work? We never observe disturbances (ɛ) but we do observe residuals (e) While each individual residual (e i ) is not going to be a very good estimator of the corresponding disturbance (ɛ i ), White (1980) showed that X ee X is a consistent (but not unbiased) estimator of X E[ɛɛ ]X 6 Thus, the variance-covariance matrix of the coefficient vector from the White estimator is: rather than: from the normal OLS estimator var cov( ˆβ) = (X X) 1 X ee X(X X) 1 (39) var cov( ˆβ) = X X) 1 X ɛɛ X(X X) 1 = (X X) 1 X (σ 2 I)X(X X) 1 (40) White (1980) suggested that we could test for the presence of heteroskedasticity by examining the extent to which the OLS estimator diverges from his own estimator White s test is to regress the squared residuals (e 2 i ) on the terms in X X ie on the squares and the cross-products of the independent variables If the R 2 exceeds a critical value (nr 2 χ 2 k ), then heteroskedasticity causes problems At that point use the White estimator (assuming your sample is sufficiently large) Neal Beck suggests that, by and large, using the White estimator can do little harm and some good 6 It is worth remembering that X ee X is a consistent (but not unbiased) estimator of X E[ɛɛ ]X since this means that robust standard errors are only appropriate when the sample is relatively large (say, greater than 100 degrees of freedom) 10

11 8 Partitioned Regression and the Frisch-Waugh-Lovell Theorem Imagine that our true model is: y = X 1 β 1 + X 2 β 2 + ɛ (41) In other words, there are two sets of independent variables For example, X 1 might contain some independent variables (perhaps also the constant) whereas X 2 contains some other independent variables The point is that X 1 and X 2 need not be two variables only We will estimate: y = X 1 ˆβ1 + X 2 ˆβ2 + e (42) Say, we wanted to isolate the coefficients associated with X 2 ie ˆβ2 The normal form equations will be: 7 [ (1) X 1 X 1 X 1 X ] [ ] [ ] 2 ˆβ1 X (2) X 2 X 1 X 2 X = 1 y 2 ˆβ 2 X 2 y (43) First, let s solve for ˆβ 1 (X 1X 1 ) ˆβ 1 + (X 1X 2 ) ˆβ 2 = X 1y (X 1X 1 ) ˆβ 1 = X 1y (X 1X 2 ) ˆβ 2 ˆβ 1 = (X 1X 1 ) 1 X 1y (X 1X 1 ) 1 X 1X 2 ˆβ2 ˆβ 1 = (X 1X 1 ) 1 X 1(y X 2 ˆβ2 ) (44) 81 Omitted Variable Bias The solution shown in Eq 44 is the set of OLS coefficients in the regression of y on X 1, ie, (X 1 X 1) 1 X 1 y, minus a correction vector (X 1 X 1) 1 X 1 X 2 ˆβ 2 This correction vector is the equation for omitted variable bias The first part of the correction vector up to ˆβ 2, ie (X 1 X 1) 1 X 1 X 2, is just the regression of the variables in X 2 done separately and then put together into a matrix on all the variables in X 1 This will only be zero if the variables in X 1 are linearly unrelated (uncorrelated or orthogonal) to the variables in X 2 The correction vector will also be zero if ˆβ2 = 0 ie if X 2 variables have no impact on y Thus, you can ignore all potential omitted variables that are either (i) unrelated to the included variables or (ii) unrelated to the dependent variable Any omitted variables that do not meet these conditions will change your estimates of ˆβ1 if they were to be included Greene (2003, 148) writes the omitted variable formula slightly differently He has E[b 1 ] = β 1 + P 12 β 2 (45) where P 12 = (X 1 X 1) 1 X 1 X 2, where b 1 is the coefficient vector of a regression omitting the X 2 7 To see this, compare with Eq 10 11

12 matrix, and β 1 and β 2 are the true coefficient vectors from a full regression including both X 1 and X 2 82 The Residual Maker and the Hat Matrix Before going any further, I introduce some useful matrices Note that: e = y X ˆβ = y X(X X) 1 X y = (I X(X X) 1 X )y = My (46) where M is called the residual maker since it makes residuals out of y M is a square matrix and is idempotent A matrix A is idempotent if A 2 = AA = A MM = (I X(X X) 1 X )(I X(X X) 1 X ) = I 2 2X(X X) 1 X + X(X X) 1 X X(X X) 1 X = I 2X(X X) 1 X + X(X X) 1 X = I X(X X) 1 X = M (47) This will prove useful The M matrix also has the properties that MX = 0 and Me = e A related matrix is the hat matrix (H) which makes ŷ our of y Note that: where: ŷ = y e = [I M]y = Hy (48) H = X(X X) 1 X (49) Greene refers to this matrix as P, but he is the only one that does this 83 Frisch-Waugh-Lovell Theorem So far we have solved for ˆβ 1 ˆβ 1 = (X 1X 1 ) 1 X 1(y X 2 ˆβ2 ) (50) 12

13 Now we insert this into (2) of Eq 43 This gives us X 2y = X 2X 1 (X 1X 1 ) 1 X 1y X 2X 1 (X 1X 1 ) 1 X 1X 2 ˆβ2 + X 2X 2 ˆβ2 X 2y X 2X 1 (X 1X 1 ) 1 X 1y = X 2X 2 ˆβ2 X 2X 1 (X 1X 1 ) 1 X 1X 2 ˆβ2 X 2y X 2X 1 (X 1X 1 ) 1 X 1y = [X 2X 2 X 2X 1 (X 1X 1 ) 1 X 1X 2 ] ˆβ 2 X 2y X 2X 1 (X 1X 1 ) 1 X 1y = [(X 2 X 2X 1 (X 1X 1 ) 1 X 1)X 2 ] ˆβ 2 X 2y X 2X 1 (X 1X 1 ) 1 X 1y = [X 2(I X 1 (X 1X 1 ) 1 X 1)X 2 ] ˆβ 2 (X 2 X 2X 1 (X 1X 1 ) 1 X 1)y = [X 2(I X 1 (X 1X 1 ) 1 X 1)X 2 ] ˆβ 2 X 2(I X 1 (X 1X 1 ) 1 X 1)y = [X 2(I X 1 (X 1X 1 ) 1 X 1)X 2 ] ˆβ 2 ˆβ 2 = [X 2(I X 1 (X 1X 1 ) 1 X 1)X 2 ] 1 X 2(I X 1 (X 1X 1 ) 1 X 1)y = (X 2M 1 X 2 ) 1 (X 2M 1 y) (51) Recall that M is the residual maker In this case, M 1 makes residuals for regressions on the X 1 variables: M 1 y is the vector of residuals from regressing y on the X 1 variables and M 1 X 2 is the matrix made up of the column by column residuals of regressing each variable (column) in X 2 on all the variables in X 1 Because M is both idempotent and symmetric, we can rewrite Eq 51 as where X 2 = M 1X 2 and y = M 1 y ˆβ 2 = (X 2 X 2 ) 1 X 2 y (52) From this it is easy to see that ˆβ 2 can be obtained from regressing y on X2 (you ll get good at spotting regressions ie equations of the (X X) 1 X y form The starred variables are just the residuals of the variables (y or X 2 ) after regressing them on the X 1 variables This leads to the Frisch-Waugh-Lovell Theorem: In the OLS regression of vector y on two sets of variables, X 1 and X 2, the subvector ˆβ 2 is the set of coefficients obtained when the residuals from a regression of y on X 1 alone are regressed on the set of residuals obtained when each column of X 2 is regressed on X 1 We ll come back to the FWL Theorem when we look at fixed effects models 84 Example Imagine we have the following model Y = β 0 + β 1 X 1 + β 2 X 2 + β 3 X 3 + ɛ (53) If we regressed Y on X 1, X 2, and X 3, we would get ˆβ 1, ˆβ2, ˆβ3 We could get these estimators differently Say we partitioned the variables into (i) X 1 and (ii) X 2 and X 3 Step 1: regress Y on X 1 and obtain residuals (e1) ie M 1 y 13

14 Step 2: regress X 2 on X 1 and obtain residuals (e2) ie first column of M 1 X 2 Step 3: regress X 3 on X 1 and obtain residuals (e3) ie second column of M 1 X 2 Step 4: regress e1 on e2 and e3 ie regress M 1 y on M 1 X 2 Step 5: the coefficient on e2 will be ˆβ 2 and the coefficient on e3 will be ˆβ 3 Steps 2 and 3 are called partialing out or netting out the effect of X 1 For this reason, the coefficients in multiple regression are often called partial regression coefficients This is what it means to say we are holding the X 1 variables constant in the regression So the difference between regressing Y on both X 1 and X 2 instead of on just X 2 is that in the first case we first regress both the dependent variables and all the X 2 variables separately on X 1 and then regress the residuals on each other, but in the second case we just regress y on the X 2 variables 14

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