Chapter 2. Appendix A: Vertical line model parameter estimates
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1 Chapter 2 Appendix A: Vertical line model parameter estimates 1
2 Model Summary Model Variables 4 Parameters 166 Equations 4 Number of Statements 252 Note: The parameter beta12 is shared by 2 of the equations to be estimated. Note: The parameter beta13 is shared by 2 of the equations to be estimated. Note: The parameter beta14 is shared by 2 of the equations to be estimated. Note: The parameter beta23 is shared by 2 of the equations to be estimated. Note: The parameter beta24 is shared by 2 of the equations to be estimated. Note: The parameter beta34 is shared by 2 of the equations to be estimated. The 4 Equations to redgrplbs = F(alpha1(esf_a2), beta11(esf_a1), beta12, beta13, beta14, a1, a2, a3, a4, a5, a6, a7, a8, a9, m2a, m3a, m4a, c1, d1, e1, z1, ban1, j1, m5a, m6a, m7a, m8a, m9a, m10a, m11a, m12a, g1, g2, g3, g4, y1, y2, gg1, gg2, gg3, gg4, mx1) swgrplbs = F(beta12, alpha2(esf_a2), beta22(esf_a1), beta23, beta24, a10, a11, a12, a13, a14, a15, a16, a17, a18, m2b, m3b, m4b, c2, d2, e2, z2, ban2, j2, m5b, m6b, m7b, m8b, m9b, m10b, g5, m11b, m12b, g6, g7, g8, y4, y5, gg5, gg6, gg7, gg8, mx2) swsnaplbs = F(beta13, beta23, alpha3(esf_a2), beta33(esf_a1), beta34, a19, a20, a21, a22, a23, a24, a25, a26, a27, m2c, m3c, m4c, c3, d3, e3, z3, ban3, j3, m5c, m6c, m7c, m8c, m9c, m10c, m11c, m12c, g9, g10, g11, g12, y7, y8, gg9, gg10, gg11, gg12, mx3) l_misclbs = F(beta14, beta24, beta34, alpha4(esf_a2), beta44(esf_a1), a28, a29, a30, a31, a32, a33, a34, a35, a36, m2d, m3d, m4d, c4, d4, e4, z4, ban4, j4, m5d, m6d, m7d, m8d, m9d, m10d, m11d, m12d, g13, g14, g15, g16, y10, y11, gg13, gg14, gg15, gg16, mx4) 2
3 Observations will be weighted by inv NOTE: At FIML Iteration 0 CONVERGE=0.001 Criteria Met. 3
4 Data Set Options DATA= OUT= OUTEST= NEW9 REST FIN Minimization Summary Parameters d 162 Method Hessian Covariance Estimator Gauss GLS Cross Iterations 0 Final Convergence Criteria R 6.329E-6 PPC(beta13) RPC. Object. Trace(S) Gradient norm 1.9E-12 Log likelihood Observations Processed Read Solved 19503
5 Nonlinear FIML Summary of Residual Errors DF DF Adj R- Equation Model Error SSE MSE Root MSE R-Square Sq redgrplbs E swgrplbs E swsnaplbs E l_misclbs E Nonlinear FIML Parameter s Parameter Pr > t alpha beta beta beta beta <.0001 a a a <.0001 a <.0001 a <.0001 a <.0001 a <.0001 a <.0001 a m2a m3a
6 Nonlinear FIML Parameter s Parameter Pr > t m4a c d <.0001 e <.0001 z ban j m5a m6a <.0001 m7a <.0001 m8a <.0001 m9a <.0001 m10a <.0001 m11a m12a g g <.0001 g <.0001 g y <.0001 y <.0001 gg gg <.0001 gg <
7 Nonlinear FIML Parameter s Parameter Pr > t gg <.0001 mx alpha beta <.0001 beta <.0001 beta a a a <.0001 a <.0001 a <.0001 a <.0001 a <.0001 a <.0001 a m2b m3b m4b <.0001 c d <.0001 e <.0001 z ban j
8 Nonlinear FIML Parameter s Parameter Pr > t m5b <.0001 m6b <.0001 m7b <.0001 m8b <.0001 m9b <.0001 m10b g m11b <.0001 m12b g <.0001 g <.0001 g <.0001 y y gg <.0001 gg gg <.0001 gg mx alpha beta <.0001 beta <.0001 a a
9 Nonlinear FIML Parameter s Parameter Pr > t a a a a a <.0001 a <.0001 a <.0001 m2c m3c m4c <.0001 c d <.0001 e z <.0001 ban j m5c <.0001 m6c <.0001 m7c <.0001 m8c <.0001 m9c m10c <.0001 m11c <.0001 m12c <
10 Nonlinear FIML Parameter s Parameter Pr > t g g g g <.0001 y y gg <.0001 gg gg gg mx <.0001 alpha <.0001 beta <.0001 a <.0001 a a a a a <.0001 a a a m2d m3d <
11 Nonlinear FIML Parameter s Parameter Pr > t m4d <.0001 c <.0001 d <.0001 e z <.0001 ban <.0001 j m5d <.0001 m6d <.0001 m7d <.0001 m8d <.0001 m9d <.0001 m10d <.0001 m11d <.0001 m12d <.0001 g <.0001 g g g y y gg gg gg
12 Nonlinear FIML Parameter s Parameter Pr > t gg <.0001 mx Nonlinear FIML s Own supply elasticity of red gro (-0.5)*(beta12*sqrt(xpswgrp/xpredgrp)+beta13*sqrt(xpswsnap/xpredgrp) +beta14*sqrt(xl_pmisc/xpredgrp))*(xesf_a1/xredgrplbs) Nonlinear FIML s Own supply elasticity of sw grou <.0001 (-0.5)*(beta12*sqrt(xpredgrp /xpswgrp)+beta23*sqrt(xpswsnap/xpswgrp) +beta24* sqrt(xl_pmisc/xpswgrp))*(xesf_a1/xswgrplbs) Nonlinear FIML s Own supply elasticity of sw snap <.0001 (-0.5)*(beta13*sqrt(xpredgrp/xpswsnap)+beta23*sqrt(xpswgrp/xpswsnap) +beta34 *sqrt(xl_pmisc/xpswsnap))*(xesf_a1/xswsnaplbs) 12
13 Nonlinear FIML s Own supply elasticity of l_miscl <.0001 (-0.5)*(beta14*sqrt(xpredgrp/xl_pmisc)+beta24*sqrt(xpswgrp/xl_pmisc) +beta34*sqrt(xpswsnap/xl_pmisc))*(xesf_a1/xl_misclbs) Nonlinear FIML s Cross-price red grouper with sw (0.5)*(beta12*sqrt(xpswgrp/xpredgrp))*(xesf_a1/xredgrplbs) Nonlinear FIML s Cross-price red grouper with sna (0.5)*(beta13*sqrt(xpswsnap/xpredgrp))*(xesf_a1/xredgrplbs ) Nonlinear FIML s Cross-price red grouper with oth <.0001 (0.5)*(beta14*sqrt(xl_pmisc/xpredgrp))*(xesf_a1/xredgrplbs) 13
14 Nonlinear FIML s Cross-price sw grouper with red (0.5)*(beta12*sqrt(xpredgrp/xpswgrp))*(xesf_a1/xswgrplbs) Nonlinear FIML s Cross-price sw grouper with snap <.0001 (0.5)*(beta23*sqrt(xpswsnap/xpswgrp))*(xesf_a1/xswgrplbs ) Nonlinear FIML s Cross-price sw grouper with othe (0.5)*(beta24*sqrt(xl_pmisc/xpswgrp))*(xesf_a1/xswgrplbs) Nonlinear FIML s Cross-price snappers with red gr (0.5)*(beta13*sqrt(xpredgrp/xpswsnap))*(xesf_a1/xswsnaplbs ) 14
15 Nonlinear FIML s Cross-price snapper with sw grou <.0001 (0.5)*(beta23*sqrt(xpswgrp/xpswsnap))*(xesf_a1/xswsnaplbs ) Nonlinear FIML s Cross-price snapper with other s <.0001 (0.5)*(beta34*sqrt(xl_pmisc/xpswsnap))*(xesf_a1/xswsnaplbs ) Nonlinear FIML s Cross-price other species with <.0001 (0.5)*(beta14*sqrt(xpredgrp/xl_pmisc))*(xesf_a1/xl_misclbs) Nonlinear FIML s Cross-price other species with (0.5)*(beta24*sqrt(xpswgrp/xl_pmisc))*(xesf_a1/xl_misclbs) Nonlinear FIML s Cross-price other species with <.0001 (0.5)*(beta34*sqrt(xpswsnap/xl_pmisc))*(xesf_a1/xl_misclbs) 15
16 Nonlinear FIML s Red Grouper Scale Elasticity (2*alpha1*xesf_a1+beta11+beta12*sqrt(xpswgrp/xpredgrp)+beta13*sqrt(xps wsnap/xpredgrp) +beta14*sqrt(xl_pmisc/xpredgrp))*(xesf_a1/xredgrplbs) Nonlinear FIML s Shallow water grouper Scale Elas (2*alpha2*xesf_a1+beta22+beta12*sqrt(xpredgrp/xpswgrp)+beta23*sqrt(xps wsnap/xpswgrp) +beta24*sqrt(xl_pmisc /xpswgrp))*(xesf_a1/xswgrplbs) Nonlinear FIML s Snappers Scale Elasticity (2*alpha3*xesf_a1+beta33+beta13*sqrt(xpredgrp/xpswsnap)+beta23*sqrt(xps wgrp/xpswsnap) +beta34*sqrt(xl_pmisc /xpswsnap))*(xesf_a1/xswsnaplbs) Nonlinear FIML s Other species Scale Elasticity <.0001 (2*alpha4*xesf_a1+beta44+beta14*sqrt(xpredgrp/xl_pmisc)+beta24*sqrt(xps wgrp/xl_pmisc) +beta34*sqrt(xpswsnap/xl_pmisc))*(xesf_a1/xl_misclbs) 16
17 Test Results Test Type Statistic Pr > ChiSq Label Test0 Wald <.0001 alpha1, alpha2, alpha3, alpha4 Test1 Wald <.0001 beta12, beta13, beta14, beta23, beta24, Test2 Wald <.0001 beta12, beta13, beta14 Test3 Wald <.0001 beta12, beta23, beta24 Test4 Wald <.0001 beta13, beta23, beta34 Test5 Wald <.0001 beta14, beta24, beta34 Test6 Wald <.0001 ban1,ban2,ban3,ban4 Test7 Wald <.0001 c1,c2,c3,c4 Test8 Wald <.0001 d1,d2,d3,d4 Test9 Wald <.0001 e1,e2,e3,e4 Test10 Wald <.0001 g1,g2,g3,g4,gg1,gg2,gg3,gg4,mx1, g5,g6, Test11 Wald j1,j2,j3,j4 Test12 Wald <.0001 z1,z2,z3,z4 Test13 Wald <.0001 m2a, m3a, m4a, m5a, m6a, m7a, m8a, m9a, Test14 Wald <.0001 a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12, Test15 Wald <.0001 y1,y2, y4,y5, y7,y8,y10,y11 Number of Observations Statistics for System Used Log Likelihood Missing 0 Sum of Weights
18 Heteroscedasticity Test Equation Test Statistic DF Pr > ChiSq Variables redgrplbs White's Test <.0001 Cross of all vars swgrplbs White's Test <.0001 Cross of all vars swsnaplbs White's Test <.0001 Cross of all vars l_misclbs White's Test <.0001 Cross of all vars 18
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