Lecture 15. Endogeneity & Instrumental Variable Estimation
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- Lynne Harvey
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1 Lecture 15. Endogeneity & Instrumental Variable Estimation Saw that measurement error (on right hand side) means that OLS will be biased (biased toward zero) Potential solution to endogeneity instrumental variable estimation - A variable that is correlated with the problem variable but which does not suffer from measurement error Tests for endogeneity Other sources of endogeneity Problems with weak instruments
2 Idea of Instrumental Variables attributed to Philip Wright interested in working out whether price of butter was demand or supply driven
3 More formally, an instrument Z for the variable of concern X satisfies 1) X,Z) 0
4 More formally, an instrument Z for the variable of concern X satisfies 1) X,Z) 0 correlated with the problem variable
5 More formally, an instrument Z for the variable of concern X satisfies 1) X,Z) 0 correlated with the problem variable 2) Z,u) = 0
6 More formally, an instrument Z for the variable of concern X satisfies 1) X,Z) 0 correlated with the problem variable 2) Z,u) = 0 but uncorrelated with the residual (so does not suffer from measurement error and also is not correlated with any unobservable factors influencing the dependent variable)
7 Instrumental variable (IV) estimation proceeds as follows:
8 Instrumental variable (IV) estimation proceeds as follows: Given a model y = b0 + b1x + u (1)
9 Instrumental variable (IV) estimation proceeds as follows: Given a model y = b0 + b1x + u (1) Multiply (1) by the instrument Z
10 Instrumental variable (IV) estimation proceeds as follows: Given a model y = b0 + b1x + u (1) Multiply by the instrument Z Zy = Zb0 + b1zx + Zu
11 Instrumental variable (IV) estimation proceeds as follows: Given a model y = b0 + b1x + u (1) Multiply by the instrument Z Zy = Zb0 + b1zx + Zu Follows that Z,y) = Cov[Zb0 + b1zx + Zu]
12 Instrumental variable (IV) estimation proceeds as follows: Given a model y = b0 + b1x + u (1) Multiply by the instrument Z Zy = Zb0 + b1zx + Zu Follows that Z,y) = Cov[Zb0 + b1zx + Zu] = Zb0) + b1z,x) + Z,u)
13 Instrumental variable (IV) estimation proceeds as follows: Given a model y = b0 + b1x + u (1) Multiply by the instrument Z Zy = Zb0 + b1zx + Zu Follows that Z,y) = Cov[Zb0 + b1zx + Zu] = Zb0) + b1z,x) + Z,u) since Zb0) = 0 (using rules on covariance of a constant)
14 Instrumental variable (IV) estimation proceeds as follows: Given a model y = b0 + b1x + u (1) Multiply by the instrument Z Zy = Zb0 + b1zx + Zu Follows that Z,y) = Cov[Zb0 + b1zx + Zu] = Zb0) + b1z,x) + Z,u) since Zb0) = 0 (using rules on covariance of a constant) and Z,u) = 0 (if assumption above about the properties of instruments is correct)
15
16 Instrumental variable (IV) estimation proceeds as follows: Given a model y = b0 + b1x + u (1) Multiply by the instrument Z Zy = Zb0 + b1zx + Zu Follows that Z,y) = Cov[Zb0 + b1zx + Zu] = Zb0) + b1z,x) + Z,u) since Zb0) = 0 (using rules on covariance of a constant) and Z,u) = 0 (if assumption above about the properties of instruments is correct)
17 then Z,y) = 0 + b1z,x) + 0
18 Solving Z,y) = 0 + b1z,x) + 0 for b1 gives the formula to calculate the instrumental variable estimator
19 Solving Z,y) = 0 + b1z,x) + 0 for b1 gives the formula to calculate the instrumental variable estimator So b1 IV = Z, y) Z, X )
20 Solving Z,y) = 0 + b1z,x) + 0 for b1 gives the formula to calculate the instrumental variable estimator So b1 IV = Z, y) Z, X ) (compare with b1 OLS = X, y) Var( X ) )
21 Solving Z,y) = 0 + b1z,x) + 0 for b1 gives the formula to calculate the instrumental variable estimator So b1 IV = Z, y) Z, X ) (compare with b1 OLS = X, y) Var( X ) ) In the presence of measurement error (or endogeneity in general) the IV estimate is unbiased in large samples (but may be biased in small samples) - technically the IV estimator is said to be consistent
22 Solving Z,y) = 0 + b1z,x) + 0 for b1 gives the formula to calculate the instrumental variable estimator So b1 IV = Z, y) Z, X ) (compare with b1 OLS = X, y) Var( X ) ) In the presence of measurement error (or endogeneity in general) the IV estimate is unbiased in large samples (but may be biased in small samples) - technically the IV estimator is said to be consistent while the OLS estimator is inconsistent IN THE PRESENCE OF ENDOGENEITY which makes IV a useful estimation technique to employ
23 However can show that (in the 2 variable case) the variance of the IV estimator is given by ^ Var ( β IV 1 ) = N s 2 1 * * Var( X ) r 2 X Z where rxz 2 is the square of the correlation coefficient between endogenous variable and instrument
24 However can show that (in the 2 variable case) the variance of the IV estimator is given by ^ Var ( β IV 1 ) = N s 2 1 * * Var( X ) r 2 X Z where rxz 2 is the square of the correlation coefficient between endogenous variable and instrument ^ s 2 (compared with OLS Var ( β OLS 1 ) = ) N * Var( X )
25 However can show that (in the 2 variable case) the variance of the IV estimator is given by ^ Var ( β IV 1 ) = N s 2 1 * * Var( X ) r 2 X Z where rxz 2 is the square of the correlation coefficient between endogenous variable and instrument ^ s 2 (compared with OLS Var ( β OLS 1 ) = ) N * Var( X ) Since r 2 >0 So IV estimation is less precise (efficient) than OLS estimation May sometimes want to trade off bias against efficiency
26 So why not ensure that the correlation between X and the instrument Z is as high as possible?
27 So why not ensure that the correlation between X and the instrument Z is as high as possible? - if X and Z are perfectly correlated then Z must also be correlated with u and so suffer the same problems as X the initial problem is not solved.
28 So why not ensure that the correlation between X and the instrument Z is as high as possible? - if X and Z are perfectly correlated then Z must also be correlated with u and so suffer the same problems as X the initial problem is not solved. Conversely if the correlation between the endogenous variable and the instrument is small there are also problems
29 Since can always write the IV estimator as b1 IV = Z, y) Z, X )
30 Since can always write the IV estimator as b1 IV = Z, y) Z, X ) sub. in for y = b0 + b1x + u
31 Since can always write the IV estimator as b1 IV = Z, y) Z, X ) sub. in for y = b0 + b1x + u Cov ( Z, b0 + b1 X + u) Z, X )
32 Since can always write the IV estimator as b1 IV = Z, y) Z, X ) sub. in for y = b0 + b1x + u b1 IV = Cov ( Z, b0 + b1 X + u) Z, X ) Cov ( Z, b (, ) (, ) = 0) + b1 Cov Z X + Cov Z u Z, X )
33 Since can always write the IV estimator as b1 IV = Z, y) Z, X ) sub. in for y = b0 + b1x + u b1 IV = Cov ( Z, b0 + b1 X + u) Z, X ) Cov ( Z, b (, ) (, ) = 0) + b1 Cov Z X + Cov Z u Z, X ) b1 IV = 0 + b 1 Z, X ) + Z, u) Z, X ) So b1 IV = b1 + Z, u) Z, X )
34 Since can always write the IV estimator as b1 IV = Z, y) Z, X ) sub. in for y = b0 + b1x + u b1 IV = Cov ( Z, b0 + b1 X + u) Z, X ) Cov ( Z, b (, ) (, ) = 0) + b1 Cov Z X + Cov Z u Z, X ) 0 + b (, ) (, ) b1 IV = 1Cov Z X + Cov Z u Z, X ) Z, u) So b1 IV = b1 + Z, X ) So if X,Z) is small then the IV estimate can be a long way from the true value b1
35 So: always check extent of correlation between X and Z before any IV estimation (see later)
36 So: always check extent of correlation between X and Z before any IV estimation (see later) In large samples you can have as many instruments as you like though finding good ones is a different matter. In small samples a minimum number of instruments is better (bias in small samples increases with no. of instruments).
37 Where to find good instruments?
38 Where to find good instruments? - difficult
39 Where to find good instruments? - difficult - The appropriate instrument will vary depending on the issue under study.
40 In the case of measurement error, could use the rank of X as an instrument (ie order the variable X by size and use the number of the order rather than the actual vale.
41 In the case of measurement error, could use the rank of X as an instrument (ie order the variable X by size and use the number of the order rather than the actual vale. Clearly correlated with the original value but because it is a rank should not be affected with measurement error
42 In the case of measurement error, could use the rank of X as an instrument (ie order the variable X by size and use the number of the order rather than the actual vale. Clearly correlated with the original value but because it is a rank should not be affected with measurement error - Though this assumes that the measurement error is not so large as to affect the (true) ordering of the X variable
43 egen rankx=rank(x_obs) /* stata command to create the ranking of x_observ */. list x_obs rankx x_observ rankx ranks from smallest observed x to largest Now do instrumental variable estimates using rankx as the instrument for x_obs ivreg y_t (x_ob=rankx) Instrumental variables (2SLS) regression Source SS df MS Number of obs = F( 1, 8) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = y_true Coef. Std. Err. t P> t [95% Conf. Interval] x_observ _cons Instrumented: x_observ Instruments: rankx Can see both estimated coefficients are a little closer to their true values than estimates from regression with measurement error (but not much)in this case the rank of X is not a very good instrumentnote that standard error in
44 instrumented regression is larger than standard error in regression of y_true on x_observed as expected with IV estimation Testing for Endogeneity It is good practice to compare OLS and IV estimates. If estimates are very different this may be a sign that things are amiss.
45 Testing for Endogeneity It is good practice to compare OLS and IV estimates. If estimates are very different this may be a sign that things are amiss. Using the idea that IV estimation will always be (asymptotically) unbiased whereas OLS will only be unbiased if X,u) = 0 then can do the following: Wu-Hausman Test for Endogeneity
46 Testing for Endogeneity It is good practice to compare OLS and IV estimates. If estimates are very different this may be a sign that things are amiss. Using the idea that IV estimation will always be (asymptotically) unbiased whereas OLS will only be unbiased if X,u) = 0 then can do the following: Wu-Hausman Test for Endogeneity 1. Given y = b0 + b1x + u (A) Regress the endogenous variable X on the instrument(s) Z X = d0 + d1z + v (B)
47 Testing for Endogeneity It is good practice to compare OLS and IV estimates. If estimates are very different this may be a sign that things are amiss. Using the idea that IV estimation will always be (asymptotically) unbiased whereas OLS will only be unbiased if X,u) = 0 then can do the following: Wu-Hausman Test for Endogeneity 1. Given y = b0 + b1x + u (A) Regress the endogenous variable X on the instrument(s) Z X = d0 + d1z + v (B) Save the residuals ^ v
48 2. Include this residual as an extra term in the original model
49 Include this residual as an extra term in the original model ie given y = b0 + b1x + u
50 Include this residual as an extra term in the original model ie given y = b0 + b1x + u estimate ^ y = b0 + b1x + b2v + e and test whether b2 = 0 (using a t test)
51 Include this residual as an extra term in the original model ie given y = b0 + b1x + u estimate ^ y = b0 + b1x + b2v + e and test whether b2 = 0 (using a t test) If b2 = 0 conclude there is no correlation between X and u
52 Include this residual as an extra term in the original model ie given y = b0 + b1x + u estimate ^ y = b0 + b1x + b2v + e and test whether b2 = 0 (using a t test) If b2 = 0 conclude there is no correlation between X and u If b2 0 conclude there is correlation between X and u
53 Include this residual as an extra term in the original model ie given y = b0 + b1x + u estimate ^ y = b0 + b1x + b2v + e and test whether b2 = 0 (using a t test) If b2 = 0 conclude there is no correlation between X and u If b2 0 conclude there is correlation between X and u Why?
54 Include this residual as an extra term in the original model ie given y = b0 + b1x + u estimate ^ y = b0 + b1x + b2v + e and test whether b2 = 0 (using a t test) If b2 = 0 conclude there is no correlation between X and u If b2 0 conclude there is correlation between X and u Why? because X = d0 + d1z + v
55 Include this residual as an extra term in the original model ie given y = b0 + b1x + u estimate ^ y = b0 + b1x + b2v + e and test whether b2 = 0 (using a t test) If b2 = 0 conclude there is no correlation between X and u If b2 0 conclude there is correlation between X and u Why? because X = d0 + d1z + v Endogenous X = instrument + something else
56 Include this residual as an extra term in the original model ie given y = b0 + b1x + u (A) estimate ^ y = b0 + b1x + b2v + e and test whether b2 = 0 (using a t test) If b2 = 0 conclude there is no correlation between X and u If b2 0 conclude there is correlation between X and u Why? because X = d0 + d1z + v Endogenous X = instrument + something else and so only way X could be correlated with u in (A) is through v
57 Include this residual as an extra term in the original model ie given y = b0 + b1x + u (A) estimate ^ y = b0 + b1x + b2v + e and test whether b2 = 0 (using a t test) If b2 = 0 conclude there is no correlation between X and u If b2 0 conclude there is correlation between X and u Why? because X = d0 + d1z + v Endogenous X = instrument + something else and so only way X could be correlated with u in (A) is through v (since Z is not correlated with u by assumption) This means the residual u in (A) depends on v + some other residual
58 Include this residual as an extra term in the original model ie given y = b0 + b1x + u (A) estimate ^ y = b0 + b1x + b2v + e and test whether b2 = 0 (using a t test) If b2 = 0 conclude there is no correlation between X and u If b2 0 conclude there is correlation between X and u Why? because X = d0 + d1z + v and so only way X could be correlated with u is through v This means the residual in (A) depends on v + some other residual u = b2v + e
59 Include this residual as an extra term in the original model ie given y = b0 + b1x + u (A) estimate ^ y = b0 + b1x + b2v + e (B) and test whether b2 = 0 (using a t test) If b2 = 0 conclude there is no correlation between X and u If b2 0 conclude there is correlation between X and u Why? because X = d0 + d1z + v and so only way X could be correlated with u is through v This means the residual in (A) depends on v + some residual u = b2v + e
60 So estimate (B) instead and test whether coefficient on v is significant ^ y = b0 + b1x + b2v + e (B) If it is, conclude that X and error term are indeed correlated; there is endogeneity N.B. This test is only as good as the instruments used and is only valid asymptotically. This may be a problem in small samples and so you should generally use this test only with sample sizes well above 100.
61 Example: The data set ivdat.dta contains information on the number of GCSE passes of a sample of 16 year olds and the total income of the household in which they live. Income tends to be measured with error. Individuals tend to mis-report incomes, particularly third-party incomes and nonlabour income. The following regression may therefore be subject to measurement error in one of the right hand side variables, (the gender dummy variable is less subject to error).. reg nqfede inc1 female Source SS df MS Number of obs = F( 2, 249) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = nqfede Coef. Std. Err. t P> t [95% Conf. Interval] inc female _cons To test endogeneity first regress the suspect variable on the instrument and any exogenous variables in the original regression reg inc1 ranki female Source SS df MS Number of obs = F( 2, 249) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = inc1 Coef. Std. Err. t P> t [95% Conf. Interval] ranki female _cons
62 save the residuals. predict uhat, resid 2. include residuals as additional regressor in the original equation. reg nqfede inc1 female uhat Source SS df MS Number of obs = F( 3, 248) = 9.94 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = nqfede Coef. Std. Err. t P> t [95% Conf. Interval] inc female uhat _cons Now added residual is not statistically significantly different from zero, so conclude that there is no endogeneity bias in the OLS estimates. Hence no need to instrument. Note you can also get this result by typing the following command after the ivreg command ivendog Tests of endogeneity of: inc1 H0: Regressor is exogenous Wu-Hausman F test: F(1,248) P-value = Durbin-Wu-Hausman chi-sq test: Chi-sq(1) P-value = the first test is simply the square of the t value on uhat in the last regression (since t 2 = F)
63 N.B. This test is only as good as the instruments used and is only valid asymptotically. This may be a problem in small samples and so you should generally use this test only with sample sizes well above 100.
64 Endogeneity & Simultaneous Equation Models Often failure to establish a one-way causal relationship in an econometric model also leads to to endogeneity problems (again violates assumption that X,u) = 0 and so OLS will give biased estimates)
65 Endogeneity & Simultaneous Equation Models Often failure to establish a one-way causal relationship in an econometric model also leads to to endogeneity problems (again violates assumption that X,u) = 0 and so OLS will give biased estimates) Eg C = a + by + e (1) Y= C +I+G + v (2)
66 Endogeneity & Simultaneous Equation Models Often failure to establish a one-way causal relationship in an econometric model also leads to to endogeneity problems (again violates assumption that X,u) = 0 and so OLS will give biased estimates) Eg C = a + by + e (1) Y= C +I+G + v (2) This is a 2 equation simultaneous equation system. C and Y appear on both sides of respective equations and are interdependent since
67 Endogeneity & Simultaneous Equation Models Often failure to establish a one-way causal relationship in an econometric model also leads to to endogeneity problems (again violates assumption that X,u) = 0 and so OLS will give biased estimates) Eg C = a + by + e (1) Y= C +I+G + v (2) This is a 2 equation simultaneous equation system. C and Y appear on both sides of respective equations and are interdependent since Any shock, represented by Δe
68 Endogeneity & Simultaneous Equation Models Often failure to establish a one-way causal relationship in an econometric model also leads to to endogeneity problems (again violates assumption that X,u) = 0 and so OLS will give biased estimates) Eg C = a + by + e (1) Y= C +I+G + v (2) This is a 2 equation simultaneous equation system. C and Y appear on both sides of respective equations and are interdependent since Any shock, represented by Δe ΔC in (1)
69 Endogeneity & Simultaneous Equation Models Often failure to establish a one-way causal relationship in an econometric model also leads to to endogeneity problems (again violates assumption that X,u) = 0 and so OLS will give biased estimates) Eg C = a + by + e (1) Y= C +I+G + v (2) This is a 2 equation simultaneous equation system. C and Y appear on both sides of respective equations and are interdependent since Any shock, represented by Δe ΔC in (1) but then this ΔC Δ Y from (2)
70 Endogeneity & Simultaneous Equation Models Often failure to establish a one-way causal relationship in an econometric model also leads to to endogeneity problems (again violates assumption that X,u) = 0 and so OLS will give biased estimates) Eg C = a + by + e (1) Y= C +I+G + v (2) This is a 2 equation simultaneous equation system. C and Y appear on both sides of respective equations and are interdependent since Any shock, represented by Δe ΔC in (1) but then this ΔC Δ Y from (2) and then this ΔY Δ C from (1)
71
72 Endogeneity & Simultaneous Equation Models Often failure to establish a one-way causal relationship in an econometric model also leads to to endogeneity problems (again violates assumption that X,u) = 0 and so OLS will give biased estimates) Eg C = a + by + e (1) Y= C +I+G + v (2) This is a 2 equation simultaneous equation system. C and Y appear on both sides of respective equations and are interdependent since Any shock, represented by Δe ΔC in (1) but then this ΔC Δ Y from (2) and then this ΔY Δ C from (1)
73 so changes in C lead to changes in Y and changes in Y lead to changes in C but the fact that Δe ΔC ΔY
74 but the fact that Δe ΔC ΔY means X,u) (or in this case Y,e) ) 0 in (1) C = a + by + e (1)
75 but the fact that Δe ΔC ΔY means X,u) (or in this case Y,e) ) 0 in (1) which given OLS formula implies ^ X, Y ) b = Var( X ) C = a + by + e (1)
76 but the fact that Δe ΔC ΔY means X,u) (or in this case Y,e) ) 0 in (1) C = a + by + e (1) which given OLS formula implies ^ X, Y ) Y, C) b = = (in this example) Var( X ) Var( Y )
77 but the fact that Δe ΔC ΔY means X,u) (or in this case Y,e) ) 0 in (1) C = a + by + e (1) which given OLS formula implies ^ X, Y ) Y, C) Y, e) b = = = b + (sub in for C Var( X ) Var( Y ) Var( Y ) from (1))
78 but the fact that Δe ΔC ΔY means X,u) (or in this case Y,e) ) 0 in (1) C = a + by + e (1) which given OLS formula implies ^ X, Y ) Y, C) Y, e) b = = = b + (sub in for C Var( X ) Var( Y ) Var( Y ) from (1)) means E(^) b b
79 but the fact that Δe ΔC ΔY means X,u) (or in this case Y,e) ) 0 in (1) C = a + by + e (1) which given OLS formula implies ^ X, Y ) Y, C) b = = = b + Var( X ) Var( Y ) Y, e) Var( Y ) means E(^) b b So OLS in the presence of interdependent variables gives biased estimates.
80 but the fact that Δe ΔC ΔY means X,u) (or in this case Y,e) ) 0 in (1) C = a + by + e (1) which given OLS formula implies ^ X, Y ) Y, C) b = = = b + Var( X ) Var( Y ) Y, e) Var( Y ) means E(^) b b So OLS in the presence of interdependent variables gives biased estimates. Any right hand side variable which has the property Cov ( X, u) 0 is said to be endogenous
81
82 Solution: IV estimation (as with measurement error, since symptom, if not cause, is the same)
83 Solution: IV estimation (as with measurement error, since symptom, if not cause, is the same) ^ Z, y ) b IV = (A) Z, X ) Again, problem is where to find instruments. In a simultaneous equation model, the answer may often be in the system itself
84 Solution: IV estimation (as with measurement error, since symptom, if not cause, is the same) ^ Z, y ) b IV = (A) Z, X ) Again, problem is where to find instruments. In a simultaneous equation model, the answer may often be in the system itself Example Price = b0 + b1wage + e (1) Wage = d0 + d1price + d2unemployment + v (2)
85 Solution: IV estimation (as with measurement error, since symptom, if not cause, is the same) ^ Z, y ) b IV = (A) Z, X ) Again, problem is where to find instruments. In a simultaneous equation model, the answer may often be in the system itself Example Price = b0 + b1wage + e (1) Wage = d0 + d1price + d2unemployment + v (2) This time wages and prices are interdependent so OLS on either (1) or (2) will give biased estimates.. but
86 Solution: IV estimation (as with measurement error, since symptom, if not cause, is the same) ^ Z, y ) b IV = (A) Z, X ) Again, problem is where to find instruments. In a simultaneous equation model, the answer may often be in the system itself Example Price = b0 + b1wage + e (1) Wage = d0 + d1price + d2unemployment + v (2) This time wages and prices are interdependent so OLS on either (1) or (2) will give biased estimates.. but unemployment does not appear in (1) by assumption
87 (can this be justified?) but is correlated with wages through (2).
88 This means unemployment can be used as an instrument for wages in (1) since Price = b0 + b1wage + e (1) Wage = d0 + d1price + d2unemployment + v (2) a) Unemployment, e) = 0 (by assumption it doesn t appear in (1) ) so uncorrelated with residual, which is one requirement of an instrument
89 This means unemployment can be used as an instrument for wages in (1) since Price = b0 + b1wage + e (1) Wage = d0 + d1price + d2unemployment + v (2) a) Unemployment, e) = 0 (by assumption it doesn t appear in (1) ) so uncorrelated with residual, which is one requirement of an instrument and b) Unemployment, Wage) 0 so correlated with endogenous RHS variable, which is the other requirement of an instrument
90 This means unemployment can be used as an instrument for wages in (1) since Price = b0 + b1wage + e (1) Wage = d0 + d1price + d2unemployment + v (2) a) Unemployment, e) = 0 (by assumption it doesn t appear in (1) ) so uncorrelated with residual, which is one requirement of an instrument and b) Unemployment, Wage) 0 so correlated with endogenous RHS variable, which is the other requirement of an instrument This process of using extra exogenous variables as instruments for endogenous RHS variables is known as identification
91 This means unemployment can be used as an instrument for wages in (1) since Price = b0 + b1wage + e (1) Wage = d0 + d1price + d2unemployment + v (2) a) Unemployment, e) = 0 (by assumption the variable doesn t appear in (1) ) so uncorrelated with residual, which is one requirement of an instrument and b) Unemployment, Wage) 0 so correlated with endogenous RHS variable, which is the other requirement of an instrument This process of using extra exogenous variables as instruments for endogenous RHS variables is known as identification If there are no additional exogenous variables outside the original equation that can be used as instruments for the endogenous RHS variables then the equation is said to be unidentified
92 This means unemployment can be used as an instrument for wages in (1) since Price = b0 + b1wage + e (1) Wage = d0 + d1price + d2unemployment + v (2) a) Unemployment, e) = 0 (by assumption it doesn t appear in (1) ) so uncorrelated with residual, which is one requirement of an instrument and b) Unemployment, Wage) 0 so correlated with endogenous RHS variable, which is the other requirement of an instrument This process of using extra exogenous variables as instruments for endogenous RHS variables is known as identification If there are no additional exogenous variables outside the original equation that can be used as instruments for the endogenous RHS variables then the equation is said to be unidentified (In the example above (2) is unidentified because despite Price being endogenous, there are no other exogenous variables not already in (2) that can be used as instruments for Price).
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