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1 PROGRAM this copy licensed for use by: TSP/GiveWin 4.5 User #45AGT TSP Version 4.5 (10/02/00) TSP/GiveWin 10MB Copyright (C) 2000 TSP International ALL RIGHTS RESERVED 02/27/01 10:34AM In case of questions or problems, see your local TSP consultant or send a description of the problem and the associated TSP output to: TSP International P.O. Box 61015, Station A Palo Alto, CA USA COMMAND *************************************************************** 1 INPUT 'LOGIN.TSP' *; 1 options memory=8 ; 2 options crt limwarn=0 memory=10 ; 3 3? Do not suppress warnings or the smpl printing until you are 3? sure the job works, but it is helpful to reduce the amount of 3? printing at that point. 3 3 supres smpl ; 4 4? This dataset contains 10 years of data on ~1200 firms for 4? employment, sales, R&D (half are zeros), investment, debt, 4? capital, etc. The high tech sectors have been excluded 4? to reduce data storage. 4? Growth rate defined as the difference in log(employment), 4? annualized. 4 4 smpl ; 5 read (file="sgrowth.dta") ; 6 6? id firm id 6? year 4 digit year, between 1986 and ? sic 4 digit sic code 6? ind ~2 digit industry code 6? sales annual sales ($M) 6? emply employment (1000s) 6? invest investment ($M) 6? rnd R&D spending ($M) 6? cashfl cash flow ($M) 6? kstock knowledge stock ($M) 6? netcap net capital stock ($M) 6? debt long term debt ($M) 6? q Tobin's q 6 6? Renaming the variables from the stata file names. 6 6 sales = s ; 7 rnd = r ;

2 8 emply = e ; 9 invest = i ; 10 kstock = rstock ; 11 netcap = c_ ; 12 debt = b ; title 'Problem 1' ;? ==================== 14 msd (all) sales emply invest rnd cashfl kstock netcap debt q ; 15 hist year ; 16 drnd = rnd<.001 ; 17 dkstock = kstock< ; 18 msd drnd dkstock ; 19? Means by Year 19 do i=86 to 95 ; 20 select year=1900+i ; 21 msd sales emply invest rnd cashfl kstock netcap debt q ; 22 enddo ; 23 select 1 ; title 'Problem 2' ;? ==================== 25 loge = log(emply) ; 26 logesq = loge*loge ; 27 list zhet c loge logesq ; 28 28? Choose firms that are there in 1989, 1990 & ? Defining the sample of good observations good = year=1990 & id=id(+4) & id=id(-1) ; 29 select good ; 30 dloge4 = (loge(+4)-loge)/4 ;? One way to compute the growth rate. 31 gre4 = (emply(+4)-emply)/(emply*4) ;? Another way to compute it. 32 msd (corr,all) dloge4 gre4 ; 33 hist gre4 ; 34 good = good & gre4<1 ;? Trim outliers in the growth rate. 35 select ~good ;? Set the growth rate to missing 36 dloge4 ; gre4=@miss ;? if it is invalid (firm exited) select good ; 39 logel = loge(-1) ;? Make a lagged size variable 40 msd (corr,all) dloge4 gre4 loge logel ; 41 olsq (robust) dloge4 loge c ; 42 42? Results for regressing growth on size are similar to Hall (1987). 42? A doubling of employment size associated with growth rate that is 42? 0.5% lower. The standard error of the regression (in annual growth 42? rate terms) is approximately 12.5 percent. 42? The test statistics indicate that the disturbance may be hetero- 42? skedastic or non-normal, or both title 'Problem 3' ;? = 43 inst (robust,inst=(c,logel)) dloge4 loge c ; 44 44? As in Hall (1987), using an instrument for size makes very little 44? difference, so the negative size-growth relationship cannot be due 44? to serially uncorrelated measurement error (regression to the mean) title 'Problem 4' ;? = 45 select year=1990 ;? Sample is all firms in in94 = good ;? Indicator for survival to 1994 (use good data only). 47

3 47 olsq (robust) dloge4 c loge ; 48 probit (hcov=nbw) in94 c loge ; 49 lmhetp zhet ; 50 50? Estimated Mills ratio is stored by the previous Probit. 50 olsq (robust) dloge4 c ; 51 select in94 ;? Look at Mills Ratio for observed data 52 ; 53 53? Estimates of sigma1 and rho from the Heckman method. 53 select in94 ;? Ad hoc estimate of var(dep var) 54? based on obs which have low Mills 54? ratio correction. 54 msd ; 55 set sigma1 ; 56 frml ; 57 print sigma1 ; 58 analyz rho ; 59 59? Note that the implied rho is -.98 with a very large standard error 59? (conditional on the sigma1 estimate). 59? The value is consistent with anything between -1 and 1. 59? We will get better precision from ML, a more efficient method title 'Problem 5' ;? == 60 select year=1990 ; 61 sampsel (hcov=nbw,maxit=100,tol=.01) in94 c loge dloge4 c loge ; 62 62? The sample selection results reveal that there are two maxima: 62? 62? 1) similar to Hall (1987), with rho very small and insignificant, 62? and no effects on the other coefficients. 62? 2) similar to the Heckman estimate, with a very negative rho and 62? a corresponding effect on the size coefficient (this is the opposite 62? of what we might have expected - firms that survive unexpectedly 62? in spite of their size have a lower growth rate than predicted by 62? their size. This may imply that there are two types of firms - 62? stable (which both grow slowly and survive) and volatile - grow 62? more quickly but are more likely to exit ? The fact that there are two maxima probably explains why Hall's 62? results changed so much between Table III and IV/V ? Save parameter estimates for starting values in weighted version. 62 param d0 d1 b1 b0 sigma rho ; 63 d0 d1 b0 b1 sigma rho ; 64? Log of estimated variance of residuals. 64 logvar = log(@res*@res) ; 65 loge2 = loge*loge ; title 'Problem 6' ;? == select ~in94 ; 68 dloge4 = 0 ;? so the observations for missing growth are not 69? dropped from the estimation. 69 select year=1990 ; 70 frml xb b0+b1*loge; 71 frml zd d0+d1*loge; 72 frml nu1 (dloge4-xb)/sigma ; 73 eqsub nu1 xb ; 74 frml logleq logl = (1-in94)*lcnorm(-zd) + in94*[ -log(sigma) 74 + lnorm(nu1) + lcnorm( (zd+rho*nu1)/sqrt(1-rho*rho) ) ] ; 75 eqsub logleq nu1 zd ;

4 76 76 ml logleq ; title 'Problem 7' ;? == msd logvar ; 79 olsq logvar c loge loge2 ; 80 forcst logfit ; 81 su = sqrt(exp(logfit)) ; 82 msd (pair) su logfit ; 83 wloge = loge/su ; 84 wc = 1/su ; 85 probit (hcov=nbw) in94 wc wloge ; 86 lmhetp zhet ; 87 87? Use ML to do the weighted Probit - note the answer is the same 87? as the previous one frml hsprob in94*lcnorm(xb) + (1-in94)*lcnorm(-xb) ; 88 frml xb (b0 + b1*loge)/su ; 89 eqsub hsprob xb ; 90 param b0.005 b1.005 ; 91 genr xb fit ; 92 msd (all) fit in94 ; 93 ml (silent,hcov=nbw,maxit=100,tol=.01) hsprob ; 94 94? Additional variables for Table V; Clean on them. 94 logq = log(q) ; 95 rnda = rnd/netcap ; 96 inve = invest/emply ; 97 rnde = rnd/emply ; 98 good = rnda<1 & rnde<100 & inve<100 & abs(logq)<2.3 ; 99 lrnde = 0 ; 100 select ~drnd & year=1990 ; 101 lrnde = log(rnde) ; 102 select year=1990 ; 103 linve = log(inve) ; select good & year=1990 & ~miss(linve) ; 105 msd (all,pair) logq rnda linve lrnde drnd in94 dloge4 ; 106 probit (hcov=nbw) in94 c loge logq rnda ; 107 lmhetp zhet ; 108 olsq (robust) dloge4 c loge linve lrnde drnd ; sampsel (hcov=nbw,maxit=100,tol=.01) in94 c loge logq rnda 109 dloge4 c loge linve lrnde drnd ; ? Now do the weighted version dot dloge4 c loge logq rnda linve lrnde drnd ; 111 w. =./su ; 112 enddot ; 113 wt = 1/su**2 ; 114 probit (hcov=nbw) in94 wc wloge wlogq wrnda ; 115 lmhetp zhet ; ? Weighted Least Squares - two different ways to do it ; 116 olsq (weight=wt,robust) dloge4 c loge linve lrnde drnd ; 117 olsq (robust) wdloge4 wc wloge wlinve wlrnde wdrnd ; 118 sampsel (hcov=nbw,maxit=100,tol=.01) in94 wc wloge wlogq wrnda 118 wdloge4 wc wloge wlinve wlrnde wdrnd ; ? Estimates for the weighted version of this model are somewhat 119? different from those in Table V, but the basic results on size-

5 119? growth and the correlation of the two equations are the same. 119? One of the reasons the results may be different is that this 119? sample excludes fast-growing technology intensive firms, so that 119? R&D and investment do not have as great a predictive effet on 119? growth. 119 input lmhetp ; 119 Proc LMhetp Zs; 120? LM test for heterosedasticity of Probit model 120? Follows Godfrey, "Misspecification tests in Econometrics", p ? Input argument: Zs - list of variables for heteroskedasticity 120? function 120? Assumes no missing data, although gaps in smpl allowed. 120 title "Godfrey's LM Test for Heteroskedasticity in Probit Eq" ; 121 local Xs den yt xb phi tmp1 tmp2 tx_xs tx_zs; 122 list RHS variables from last Probit estimation 123 den = sqrt(@fit*(1-@fit)); 124 yt this is a divide by zero sometimes 125? but those observations would be automatically 125? dropped from the regression 125 forcst xb;? Fitted value for latent variable 126 phi = norm(xb); 127? transform X variables 127 list(prefix=tx_) tx_xs Xs; local tx_xs; 129 tmp1 = phi/den; 130 dot Xs; 131 tx_. =.*tmp1; 132 enddot; 133? transform Z variables 133 list(prefix=tz_) tx_zs Zs; local tx_zs; 135 tmp2 = -xb*tmp1; 136 dot Zs; 137 tz_. =.*tmp2; 138 enddot; 139 olsq(silent) yt tx_zs tx_xs; 140 set LMhet2 explained sum of squares 141 cdf(chisq,df=@ncid) LMhet2; 142 Endproc; EXECUTION ****************************************************************************** * Problem 1 ========= Number of Observations: SALES EMPLY INVEST RND CASHFL KSTOCK NETCAP DEBT Q

6 SALES D D EMPLY INVEST RND CASHFL KSTOCK NETCAP D DEBT Q Median 1st Qrt 3rd Qrt IQ Range SALES EMPLY INVEST RND CASHFL KSTOCK NETCAP DEBT Q Line 14 (MSD ): 32.7 seconds. HISTOGRAM OF YEAR Number of observations: MINIMUM MAXIMUM * ********************* ********************************** ************************* ******************** ********************* **************************** ***************************************** ****************************** ****** MINIMUM MAXIMUM Number of Observations: DRND DKSTOCK DRND DKSTOCK Number of Observations: 1014

7 SALES EMPLY INVEST RND CASHFL KSTOCK NETCAP DEBT Q SALES D EMPLY INVEST RND CASHFL KSTOCK NETCAP D DEBT Q Number of Observations: 1107 SALES EMPLY INVEST RND CASHFL KSTOCK NETCAP DEBT Q SALES D EMPLY INVEST RND CASHFL KSTOCK NETCAP D DEBT Q Number of Observations: 1166 SALES EMPLY INVEST RND CASHFL

8 KSTOCK NETCAP DEBT Q SALES D EMPLY INVEST RND CASHFL KSTOCK NETCAP D DEBT Q Number of Observations: 1126 SALES EMPLY INVEST RND CASHFL KSTOCK NETCAP DEBT Q SALES D EMPLY INVEST RND CASHFL KSTOCK NETCAP D DEBT Q Number of Observations: 1101 SALES EMPLY INVEST RND CASHFL KSTOCK NETCAP DEBT Q

9 SALES D EMPLY INVEST RND CASHFL KSTOCK NETCAP D DEBT Q Number of Observations: 1106 SALES EMPLY INVEST RND CASHFL KSTOCK NETCAP DEBT Q SALES D EMPLY INVEST RND CASHFL KSTOCK NETCAP D DEBT Q Number of Observations: 1141 SALES EMPLY INVEST RND CASHFL KSTOCK NETCAP DEBT Q SALES D EMPLY INVEST RND CASHFL

10 KSTOCK NETCAP D DEBT Q Number of Observations: 1199 SALES EMPLY INVEST RND CASHFL KSTOCK NETCAP DEBT Q SALES D EMPLY INVEST RND CASHFL KSTOCK NETCAP D DEBT Q Number of Observations: 1150 SALES EMPLY INVEST RND CASHFL KSTOCK NETCAP DEBT Q SALES D EMPLY INVEST RND CASHFL KSTOCK NETCAP D DEBT Q

11 Number of Observations: 1035 SALES EMPLY INVEST RND CASHFL KSTOCK NETCAP DEBT Q SALES D EMPLY INVEST RND CASHFL KSTOCK NETCAP D DEBT Q Problem 2 ========= Number of Observations: 897 Results of Covariance procedure ========== DLOGE GRE DLOGE GRE Median 1st Qrt 3rd Qrt IQ Range DLOGE GRE DLOGE4 GRE4 DLOGE GRE Correlation Matrix HISTOGRAM OF GRE4 MINIMUM MAXIMUM

12 ***************************************** ** * * * * * * * * MINIMUM MAXIMUM Number of Observations: 888 Results of Covariance procedure ========== DLOGE GRE LOGE LOGEL DLOGE GRE LOGE LOGEL Median 1st Qrt 3rd Qrt IQ Range DLOGE GRE LOGE LOGEL Correlation Matrix DLOGE4 GRE4 LOGE LOGEL DLOGE GRE LOGE LOGEL Equation 1 ============ Method of estimation = Ordinary Least Squares Dependent variable: DLOGE4 Number of observations: 888 Mean of dep. var. = E-02 LM het. test = [.002] Std. dev. of dep. var. = Durbin-Watson = [<.333] Sum of squared residuals = Jarque-Bera test = [.000] Variance of residuals = Ramsey's RESET2 = [.398] Std. error of regression = F (zero slopes) = [.000]

13 R-squared = Schwarz B.I.C. = Adjusted R-squared = Log likelihood = Estimated Variable Coefficient Error t-statistic P-value LOGE E E [.001] C E E [.171] Errors are heteroskedastic-consistent (HCTYPE=2). Problem 3 ========= Dependent variable: DLOGE4 Endogenous variables: LOGE Included exogenous variables: C Excluded exogenous variables: LOGEL Number of observations: 888 Equation 2 ============ Method of estimation = Instrumental Variable Mean of dep. var. = E-02 R-squared = Std. dev. of dep. var. = Adjusted R-squared = Sum of squared residuals = Durbin-Watson = [<.326] Variance of residuals = E'PZ*E = 0. Std. error of regression = Estimated Variable Coefficient Error t-statistic P-value LOGE E E [.000] C E E [.147] Errors are heteroskedastic-consistent Problem 4 ========= Equation 3 ============ Method of estimation = Ordinary Least Squares Dependent variable: DLOGE4 Number of observations: 888 Mean of dep. var. = E-02 LM het. test = [.002] Std. dev. of dep. var. = Durbin-Watson = [<.333] Sum of squared residuals = Jarque-Bera test = [.000] Variance of residuals = Ramsey's RESET2 = [.398] Std. error of regression = F (zero slopes) = [.000] R-squared = Schwarz B.I.C. = Adjusted R-squared = Log likelihood = Estimated Variable Coefficient Error t-statistic P-value C E E [.171]

14 LOGE E E [.001] Errors are heteroskedastic-consistent (HCTYPE=2). Equation 4 ============ PROBIT ESTIMATION Working space used: 7741 STARTING VALUES C LOGE VALUE F= FNEW= ISQZ= 0 STEP= CRIT= F= FNEW= ISQZ= 0 STEP= CRIT= F= FNEW= ISQZ= 0 STEP= CRIT= F= FNEW= ISQZ= 0 STEP= CRIT= E- 04 CONVERGENCE ACHIEVED AFTER 4 ITERATIONS 8 FUNCTION EVALUATIONS. Dependent variable: IN94 Number of observations = 1101 Scaled R-squared = Number of positive obs. = 888 LR (zero slopes) = [.000] Mean of dep. var. = Schwarz B.I.C. = Sum of squared residuals = Log likelihood = R-squared = Fraction of Correct Predictions = C [.000] LOGE [.000] Errors computed from (Newton) analytic second derivatives C [.000] LOGE [.000] Errors computed from (BHHH) covariance of analytic first derivatives C [.000] LOGE [.000] Errors computed from (Eicker-White) analytic first and second derivatives dp/dx 0 1 C LOGE

15 Godfrey's LM Test for Heteroskedasticity in Probit Eq =========== CHISQ(4) Test Statistic: , Upper tail area: Equation 6 ============ Method of estimation = Ordinary Least Squares Dependent variable: DLOGE4 Number of observations: 888 Mean of dep. var. = E-02 LM het. test = [.094] Std. dev. of dep. var. = Durbin-Watson = [<.362] Sum of squared residuals = Jarque-Bera test = [.000] Variance of residuals = Ramsey's RESET2 = [.550] Std. error of regression = F (zero slopes) = [.001] R-squared = Schwarz B.I.C. = Adjusted R-squared = Log likelihood = Estimated Variable Coefficient Error t-statistic P-value C [.410] LOGE [.493] Errors are heteroskedastic-consistent (HCTYPE=2). HISTOGRAM MINIMUM MAXIMUM ********************* ***************************************** ************************************* ************************* ************* ******** **** ** * * MINIMUM MAXIMUM SIGMA1 = Results of Parameter Analysis ======== RHO [.493]

16 Wald Test for the Hypothesis that the given set of Parameters are jointly zero: CHISQ(1) = ; P-value = F Test for the Hypothesis that the given set of Parameters are jointly zero: F(1,11128) = ; P-value = Constrained original coefficients ============ Estimated Variable Coefficient Error t-statistic P-value C E E [.116] LOGE E E [1.00] Problem 5 ========= Equation 7 ============ Sample Selection Estimation 204 function evaluations used for grid search on RHO. Working space used: STARTING VALUES C LOGE C LOGE VALUE SIGMA RHO VALUE F= FNEW= ISQZ= 0 STEP= CRIT= E- 01 F= FNEW= ISQZ= 0 STEP= CRIT= E- 02 F= FNEW= ISQZ= 0 STEP= CRIT= E- 05 CONVERGENCE ACHIEVED AFTER 3 ITERATIONS 6 FUNCTION EVALUATIONS. Working space used: F= FNEW= ISQZ= 0 STEP= CRIT= E- 01 F= FNEW= ISQZ= 0 STEP= CRIT= E- 04 F= FNEW= ISQZ= 0 STEP= CRIT= E- 08 CONVERGENCE ACHIEVED AFTER 3 ITERATIONS

17 12 FUNCTION EVALUATIONS. NOTE: multiple local minima for objective function # RHO -LogL Probit Dependent variable: IN94 Regression Dependent variable: DLOGE4 Number of observations = 1101 Schwarz B.I.C. = Number of positive obs. = 888 Log likelihood = Fraction of positive obs. = C [.000] LOGE [.000] C E [.000] LOGE E [.000] SIGMA E [.000] RHO [.000] Errors computed from (Newton) analytic second derivatives C [.000] LOGE [.000] C E [.000] LOGE E [.000] SIGMA E [.000] RHO [.000] Errors computed from (BHHH) covariance of analytic first derivatives C [.000] LOGE [.000] C E [.000] LOGE E [.000] SIGMA [.000] RHO [.000] Errors computed from (Eicker-White) analytic first and second derivatives dp/dx 0 1 C LOGE Problem 6 =========

18 MAXIMUM LIKELIHOOD ESTIMATION ======== EQUATION: LOGLEQ Working space used: 7273 STARTING VALUES D0 D1 B0 B1 VALUE SIGMA RHO VALUE F= FNEW= ISQZ= 0 STEP= CRIT= E- 11 CONVERGENCE ACHIEVED AFTER 1 ITERATIONS 2 FUNCTION EVALUATIONS. Number of observations = 1101 Log likelihood = Schwarz B.I.C. = D [.000] D [.000] B E [.000] B E [.000] SIGMA E [.000] RHO [.000] Errors computed from (BHHH) covariance of analytic first derivatives Problem 7 ========= Number of Observations: 888 LOGVAR LOGVAR Equation 8 ============ Method of estimation = Ordinary Least Squares

19 Dependent variable: LOGVAR Number of observations: 888 Mean of dep. var. = LM het. test = [.620] Std. dev. of dep. var. = Durbin-Watson = [<.312] Sum of squared residuals = Jarque-Bera test = [.000] Variance of residuals = Ramsey's RESET2 = [.082] Std. error of regression = F (zero slopes) = [.000] R-squared = Schwarz B.I.C. = Adjusted R-squared = Log likelihood = Estimated Variable Coefficient Error t-statistic P-value C [.000] LOGE [.000] LOGE [.050] Number of Observations: 1101 Num.Obs SU LOGFIT SU LOGFIT Equation 9 ============ PROBIT ESTIMATION Working space used: 7741 STARTING VALUES WC WLOGE VALUE F= FNEW= ISQZ= 0 STEP= CRIT= F= FNEW= ISQZ= 0 STEP= CRIT= F= FNEW= ISQZ= 0 STEP= CRIT= F= FNEW= ISQZ= 0 STEP= CRIT= E- 03 F= FNEW= ISQZ= 0 STEP= CRIT= E- 10 CONVERGENCE ACHIEVED AFTER 5 ITERATIONS 10 FUNCTION EVALUATIONS. Dependent variable: IN94 Number of observations = 1101 Scaled R-squared = Number of positive obs. = 888 LR (zero slopes) = [.000] Mean of dep. var. = Schwarz B.I.C. = Sum of squared residuals = Log likelihood =

20 R-squared = Fraction of Correct Predictions = WC E [.000] WLOGE E E [.009] Errors computed from (Newton) analytic second derivatives WC E [.000] WLOGE E E [.005] Errors computed from (BHHH) covariance of analytic first derivatives WC E [.000] WLOGE E E [.015] Errors computed from (Eicker-White) analytic first and second derivatives dp/dx 0 1 WC WLOGE Godfrey's LM Test for Heteroskedasticity in Probit Eq =========== CHISQ(4) Test Statistic: , Upper tail area: Number of Observations: 1101 FIT IN FIT IN Median 1st Qrt 3rd Qrt IQ Range FIT IN

21 Number of Observations: 1065 Num.Obs LOGQ RNDA LINVE LRNDE DRND IN DLOGE Median LOGQ RNDA LINVE LRNDE DRND IN DLOGE st Qrt 3rd Qrt IQ Range LOGQ RNDA LINVE LRNDE DRND IN DLOGE Equation 11 ============ PROBIT ESTIMATION Working space used: STARTING VALUES C LOGE LOGQ RNDA VALUE F= FNEW= ISQZ= 0 STEP= CRIT= F= FNEW= ISQZ= 0 STEP= CRIT= F= FNEW= ISQZ= 0 STEP= CRIT= F= FNEW= ISQZ= 0 STEP= CRIT= E- 04 F= FNEW= ISQZ= 0 STEP= CRIT= E- 11 CONVERGENCE ACHIEVED AFTER 5 ITERATIONS 10 FUNCTION EVALUATIONS. Dependent variable: IN94 Number of observations = 1065 Scaled R-squared = Number of positive obs. = 864 LR (zero slopes) = [.000] Mean of dep. var. = Schwarz B.I.C. = Sum of squared residuals = Log likelihood = R-squared =

22 Fraction of Correct Predictions = C [.000] LOGE [.000] LOGQ [.615] RNDA [.315] Errors computed from (Newton) analytic second derivatives C [.000] LOGE [.000] LOGQ [.632] RNDA [.333] Errors computed from (BHHH) covariance of analytic first derivatives C [.000] LOGE [.000] LOGQ [.598] RNDA [.300] Errors computed from (Eicker-White) analytic first and second derivatives dp/dx 0 1 C LOGE LOGQ RNDA Godfrey's LM Test for Heteroskedasticity in Probit Eq =========== CHISQ(7) Test Statistic: , Upper tail area: Equation 13 ============ Method of estimation = Ordinary Least Squares Dependent variable: DLOGE4 Number of observations: 1065 Mean of dep. var. = E-02 LM het. test = [.000] Std. dev. of dep. var. = Durbin-Watson = [<.583] Sum of squared residuals = Jarque-Bera test = [.000] Variance of residuals = Ramsey's RESET2 = [.034] Std. error of regression = F (zero slopes) = [.001] R-squared = Schwarz B.I.C. =

23 Adjusted R-squared = Log likelihood = Estimated Variable Coefficient Error t-statistic P-value C E [.087] LOGE E E [.004] LINVE E E [.157] LRNDE E E [.858] DRND E [.032] Errors are heteroskedastic-consistent (HCTYPE=2). Equation 14 ============ Sample Selection Estimation 238 function evaluations used for grid search on RHO. Working space used: STARTING VALUES C LOGE LOGQ RNDA VALUE C LOGE LINVE LRNDE VALUE DRND SIGMA RHO VALUE F= FNEW= ISQZ= 0 STEP= CRIT= F= FNEW= ISQZ= 0 STEP= CRIT= F= FNEW= ISQZ= 0 STEP= CRIT= E- 02 F= FNEW= ISQZ= 0 STEP= CRIT= E- 06 CONVERGENCE ACHIEVED AFTER 4 ITERATIONS 8 FUNCTION EVALUATIONS. Working space used: F= FNEW= ISQZ= 0 STEP= CRIT= E- 01 F= FNEW= ISQZ= 0 STEP= CRIT= E- 03 F= FNEW= ISQZ= 0 STEP= CRIT= E- 05 CONVERGENCE ACHIEVED AFTER 3 ITERATIONS 14 FUNCTION EVALUATIONS. NOTE: multiple local minima for objective function # RHO -LogL Probit Dependent variable: IN94

24 Regression Dependent variable: DLOGE4 Number of observations = 1065 Schwarz B.I.C. = Number of positive obs. = 864 Log likelihood = Fraction of positive obs. = C [.000] LOGE [.000] LOGQ [.015] RNDA [.529] C [.049] LOGE E [.000] LINVE E E [.065] LRNDE E E [.729] DRND E [.019] SIGMA E [.000] RHO [.000] Errors computed from (Newton) analytic second derivatives C [.000] LOGE [.000] LOGQ [.017] RNDA [.516] C E [.044] LOGE E [.000] LINVE E E [.040] LRNDE E E [.735] DRND E [.031] SIGMA E [.000] RHO [.000] Errors computed from (BHHH) covariance of analytic first derivatives C [.000] LOGE [.000] LOGQ [.017] RNDA [.548] C [.116] LOGE E [.000] LINVE E E [.146] LRNDE E E [.727] DRND E [.013] SIGMA [.000] RHO [.000] Errors computed from (Eicker-White) analytic first and second derivatives dp/dx 0 1 C LOGE LOGQ RNDA

25 Equation 15 ============ PROBIT ESTIMATION Working space used: STARTING VALUES WC WLOGE WLOGQ WRNDA VALUE F= FNEW= ISQZ= 0 STEP= CRIT= F= FNEW= ISQZ= 0 STEP= CRIT= F= FNEW= ISQZ= 0 STEP= CRIT= F= FNEW= ISQZ= 0 STEP= CRIT= E- 03 F= FNEW= ISQZ= 0 STEP= CRIT= E- 09 CONVERGENCE ACHIEVED AFTER 5 ITERATIONS 10 FUNCTION EVALUATIONS. Dependent variable: IN94 Number of observations = 1065 Scaled R-squared = Number of positive obs. = 864 LR (zero slopes) = [.000] Mean of dep. var. = Schwarz B.I.C. = Sum of squared residuals = Log likelihood = R-squared = Fraction of Correct Predictions = WC E [.000] WLOGE E E [.008] WLOGQ E E [.608] WRNDA [.206] Errors computed from (Newton) analytic second derivatives WC E [.000] WLOGE E E [.004] WLOGQ E E [.626] WRNDA [.228] Errors computed from (BHHH) covariance of analytic first derivatives WC E [.000] WLOGE E E [.015] WLOGQ E E [.592] WRNDA [.186] Errors computed from analytic first and second derivatives

26 (Eicker-White) dp/dx 0 1 WC WLOGE WLOGQ WRNDA Godfrey's LM Test for Heteroskedasticity in Probit Eq =========== CHISQ(7) Test Statistic: , Upper tail area: Equation 17 ============ Method of estimation = Weighted Regression Weight: WT Dependent variable: DLOGE4 Number of observations: 1065 (Statistics based on transformed data) Mean of dep. var. = E-02 Std. dev. of dep. var. = Sum of squared residuals = Variance of residuals = Std. error of regression = R-squared = Adjusted R-squared = Durbin-Watson = Sum of weights = F (zero slopes) = [.000] Schwarz B.I.C. = Log likelihood = (Statistics based on original data) Mean of dep. var. = E-02 Std. dev. of dep. var. = Sum of squared residuals = Variance of residuals = Std. error of regression = R-squared = Adjusted R-squared = Durbin-Watson = Estimated Variable Coefficient Error t-statistic P-value C E [.032] LOGE E E [.000] LINVE E E [.023] LRNDE E E [.795] DRND E [.008] Errors are heteroskedastic-consistent (HCTYPE=2). Equation 18

27 ============ Method of estimation = Ordinary Least Squares Dependent variable: WDLOGE4 Number of observations: 1065 Mean of dep. var. = LM het. test = [.000] Std. dev. of dep. var. = Durbin-Watson = [<.592] Sum of squared residuals = Jarque-Bera test = [.000] Variance of residuals = Ramsey's RESET2 = [.019] Std. error of regression = F (zero slopes) = [.000] R-squared = Schwarz B.I.C. = Adjusted R-squared = Log likelihood = Estimated Variable Coefficient Error t-statistic P-value WC E [.032] WLOGE E E [.000] WLINVE E E [.023] WLRNDE E E [.795] WDRND E [.008] Errors are heteroskedastic-consistent (HCTYPE=2). Equation 19 ============ Sample Selection Estimation 231 function evaluations used for grid search on RHO. Working space used: STARTING VALUES WC WLOGE WLOGQ WRNDA VALUE WC WLOGE WLINVE WLRNDE VALUE WDRND SIGMA RHO VALUE F= FNEW= ISQZ= 0 STEP= CRIT= E- 01 F= FNEW= ISQZ= 0 STEP= CRIT= E- 04 CONVERGENCE ACHIEVED AFTER 2 ITERATIONS 4 FUNCTION EVALUATIONS. Probit Dependent variable: IN94 Regression Dependent variable: WDLOGE4 Number of observations = 1065 Schwarz B.I.C. = Number of positive obs. = 864 Log likelihood = Fraction of positive obs. =

28 WC E [.000] WLOGE E E [.071] WLOGQ E [.001] WRNDA [.133] WC E [.002] WLOGE E [.000] WLINVE E E [.090] WLRNDE E E [.877] WDRND E [.004] SIGMA [.000] RHO [.000] Errors computed from (Newton) analytic second derivatives WC E [.000] WLOGE E E [.066] WLOGQ E [.002] WRNDA [.159] WC [.004] WLOGE E [.000] WLINVE E E [.103] WLRNDE E E [.876] WDRND E [.005] SIGMA [.000] RHO [.000] Errors computed from (BHHH) covariance of analytic first derivatives WC E [.000] WLOGE E E [.081] WLOGQ E [.001] WRNDA [.115] WC E [.002] WLOGE E [.000] WLINVE E E [.086] WLRNDE E E [.884] WDRND E [.005] SIGMA [.000] RHO [.000] Errors computed from (Eicker-White) analytic first and second derivatives dp/dx 0 1 WC WLOGE WLOGQ WRNDA ****************************************************************************** * END OF OUTPUT.

29 TOTAL NUMBER OF NUMERIC WARNINGS: 214 TOTAL NUMBER OF WARNING MESSAGES: 62 MEMORY USAGE: ITEM: DATA ARRAY TOTAL MEMORY UNITS: (4-BYTE WORDS) (MEGABYTES) MEMORY ALLOCATED : MEMORY ACTUALLY REQUIRED : CURRENT VARIABLE STORAGE :