Starting Dynare (version 5.2).
Calling Dynare with arguments: none
Starting preprocessing of the model file ...
Found 81 equation(s).
Evaluating expressions...done
Computing static model derivatives (order 1).
Computing dynamic model derivatives (order 1).
Processing outputs ...
done
Preprocessing completed.


==== Method of Moments Estimation (SMM) ====



[Warning: Some of the parameters have no value (ltauss, wss, alpha1, alpha2) when using run. If these parameters are not
initialized in a steadystate file or a steady_state_model-block, Dynare may not be able to solve the model. Note that simul,
perfect_foresight_setup, and perfect_foresight_solver do not automatically call the steady state file.] 
Computing data moments. Note that NaN values in the moments (due to leads and lags or missing data) are replaced by the mean of the corresponding moment
[Warning: Some of the parameters have no value (ltauss, wss, alpha1, alpha2) when using run. If these parameters are not
initialized in a steadystate file or a steady_state_model-block, Dynare may not be able to solve the model. Note that simul,
perfect_foresight_setup, and perfect_foresight_solver do not automatically call the steady state file.] 
Initial value of the moment objective function with 500.0 times identity weighting matrix: 2.7384 

Time required to compute objective function once: 0.0530 seconds 

---------------------------------------------------
Simulated method of moments with
  - uncentered moments (prefilter=0)
  - optimizer (mode_compute=5): newrat
  - perturbation order:        1
  - standard errors:           numerical derivatives
  - number of matched moments: 6
  - number of parameters:      6

Estimation stage 1
  - optimal weighting matrix (Bartlett kernel with 20 lags)
    and using data-moments as initial estimate of model-moments
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  6.336874e-33.] 
[> In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('mom.optimal_weighting_matrix', 'D:\Dynare\5.2\matlab\+mom\optimal_weighting_matrix.m', 68)" style="font-weight:bold">mom.optimal_weighting_matrix</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\+mom\optimal_weighting_matrix.m',68,0)">line 68</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('mom.run', 'D:\Dynare\5.2\matlab\+mom\run.m', 878)" style="font-weight:bold">mom.run</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\+mom\run.m',878,0)">line 878</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('ClimateDSGE_EZ_LRR_SMM5.driver', 'E:\Dropbox Folder\Dropbox\Climate Policy Uncertainty and DSGE\Model Quantitative\Climate DSGE Code 2\+ClimateDSGE_EZ_LRR_SMM5\driver.m', 888)" style="font-weight:bold">ClimateDSGE_EZ_LRR_SMM5.driver</a> (<a href="matlab: opentoline('E:\Dropbox Folder\Dropbox\Climate Policy Uncertainty and DSGE\Model Quantitative\Climate DSGE Code 2\+ClimateDSGE_EZ_LRR_SMM5\driver.m',888,0)">line 888</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('dynare', 'D:\Dynare\5.2\matlab\dynare.m', 281)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\dynare.m',281,0)">line 281</a>)
] 
[Warning: newrat: Unknown option (UseParallel)!] 
[> In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('dynare_minimize_objective', 'D:\Dynare\5.2\matlab\optimization\dynare_minimize_objective.m', 313)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\optimization\dynare_minimize_objective.m',313,0)">line 313</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('mom.run', 'D:\Dynare\5.2\matlab\+mom\run.m', 919)" style="font-weight:bold">mom.run</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\+mom\run.m',919,0)">line 919</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('ClimateDSGE_EZ_LRR_SMM5.driver', 'E:\Dropbox Folder\Dropbox\Climate Policy Uncertainty and DSGE\Model Quantitative\Climate DSGE Code 2\+ClimateDSGE_EZ_LRR_SMM5\driver.m', 888)" style="font-weight:bold">ClimateDSGE_EZ_LRR_SMM5.driver</a> (<a href="matlab: opentoline('E:\Dropbox Folder\Dropbox\Climate Policy Uncertainty and DSGE\Model Quantitative\Climate DSGE Code 2\+ClimateDSGE_EZ_LRR_SMM5\driver.m',888,0)">line 888</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('dynare', 'D:\Dynare\5.2\matlab\dynare.m', 281)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\dynare.m',281,0)">line 281</a>)] 
Gradient norm  4.385459494192514e+41
Minimum Hessian eigenvalue 
Maximum Hessian eigenvalue 
 
Iteration 1
Near-singular H problem.
Correct for low angle: 2.28026e-35
Predicted improvement: 48080637438008159624844008751592445785969647991856068845226486716809888137216.000000000
lambda =          1; f = 48080637438008163406405485463605938449293038635524605395889244791111680.0000000
lambda =    0.33333; f = 5342293048667572519881855489153769993429316509262257784968376611766272.0000000
lambda =    0.11111; f = 593588116518619254014402880233133241400314453824094399370881967063040.0000000
lambda =   0.037037; f = 65954235168735472668266986692570360155590494869343822152320218562560.0000000
lambda =   0.012346; f = 7328248352081719684222224063678236097478922330418362059482776731648.0000000
lambda =  0.0041152; f = 814249816897968625158657633529658585187853096210207820436639580160.0000000
lambda =  0.0013717; f = 90472201877552072060298203424900586271867824407859570547951140864.0000000
lambda = 0.00045725; f = 10052466875283565835702347339726826791495053603980205081222971392.0000000
lambda = 0.00015242; f = 1116940763920396467849167555827118763609211863104456891345928192.0000000
lambda = 5.0805e-05; f = 124104529324488504039672978412577006045215377408913715301974016.0000000
lambda = 1.6935e-05; f = 13789392147165389337741442045841889560579486378768190589108224.0000000
lambda =  5.645e-06; f = 1532154683018376771488344037782972861239022445494468779966464.0000000
lambda = 1.8817e-06; f = 170239409224264073331624227236650794840047898028073472229376.0000000
lambda = 6.2723e-07; f = 18915489913807120497999647389106929511989670483583802802176.0000000
lambda = 2.0908e-07; f = 2101721101534124461061167073562882547277119163717017141248.0000000
lambda = 6.9692e-08; f = 233524566837124925599193130658056953037918878879780438016.0000000
lambda = 2.3231e-08; f = 25947174093013876992453989583107162283772951714448015360.0000000
lambda = 7.7435e-09; f = 2883019343668209046236084395034131923071427591937196032.0000000
lambda = 2.5812e-09; f = 320335482629801099660222410820032286834216241261969408.0000000

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f = 372156380818348302302170395649425539072.0000000
lambda = -2.0908e-07; f = 41350786943540373122600294892286509056.0000000
lambda = -6.9692e-08; f = 4594609846339495644422923627060527104.0000000
lambda = -2.3231e-08; f = 510590168872730914480841892720279552.0000000
lambda = -7.7435e-09; f = 56810204709757004357432606378164224.0000000
lambda = -2.5812e-09; f = 6390208691648793658726914038169600.0000000
Norm of dx 2.1927e+35
Near-singular H problem.
Correct for low angle: 2.28026e-39
Predicted improvement: 480806374380081569200458926548600139937390541632111762798544075816129877579399168.000000000
lambda =          1; f = 4808063743800815988195409583303088987268358276644208524877896572205674178543616.0000000
lambda =    0.33333; f = 534229304866757206323447380552659445080782807607304038805220994346613125152768.0000000
lambda =    0.11111; f = 59358811651861926097610102363542387604194466100034693397940055218893327695872.0000000
lambda =   0.037037; f = 6595423516873546272886870756622303816935768005895453140524676946904035098624.0000000
lambda =   0.012346; f = 732824835208171841576417117242553386783740035540382345937526475686410190848.0000000
lambda =  0.0041152; f = 81424981689796864311711084819527305380649533718190909434885781227340562432.0000000
lambda =  0.0013717; f = 9047220187755209238112921219952177432001978149021128637994679402160455680.0000000
lambda = 0.00045725; f = 1005246687528356516626070391938983980266302191478044680933206275961913344.0000000
lambda = 0.00015242; f = 111694076392039642927253953815047005703930996659162333092365753363988480.0000000
lambda = 5.0805e-05; f = 12410452932448848192475634291079317283760981528589692375547891140263936.0000000
lambda = 1.6935e-05; f = 1378939214716538815760809993388440116946805765702397787733581419773952.0000000
lambda =  5.645e-06; f = 153215468301837670140888380850451202621039606523847414902085608538112.0000000
lambda = 1.8817e-06; f = 17023940922426409456296127457655231669641365948731726242092760956928.0000000
lambda = 6.2723e-07; f = 1891548991380712452811895728292584565627176241812321940613764218880.0000000
lambda = 2.0908e-07; f = 210172110153412473971076238881890115061724183792010602739377635328.0000000
lambda = 6.9692e-08; f = 23352456683712491911447093810332970282645504096772663749948473344.0000000
lambda = 2.3231e-08; f = 2594717409301388152549859015692876498481148534783964822875668480.0000000
lambda = 7.7435e-09; f = 288301934366820961660116316465639403444201086778472536622497792.0000000
lambda = 2.5812e-09; f = 32033548262980107485456343032164325297482773841828120239276032.0000000

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f = 37215629310915970130958407142449729261421985792.0000000
lambda = -2.0908e-07; f = 4135069923435185611990559248799339576660328448.0000000
lambda = -6.9692e-08; f = 459452213715098654491453965320863194470678528.0000000
lambda = -2.3231e-08; f = 51050245968422257656191213206741779786760192.0000000
lambda = -7.7435e-09; f = 5672249552124881605539839955595261376462848.0000000
lambda = -2.5812e-09; f = 630249950314061760661761702568224110936064.0000000
Norm of dx 2.1927e+39
Predicted improvement: 85264901488292400217793862041600.000000000
lambda =          1; f = 87709189385267267345506967224320.0000000
lambda =    0.33333; f = 87709189385267267345506967224320.0000000
lambda =    0.11111; f = 69784751152054667166997484142592.0000000
lambda =     0.2148; f = 87709189385267267345506967224320.0000000
lambda =    0.14463; f = 64778916443522843662243986407424.0000000
lambda =    0.18337; f = 87709189385267267345506967224320.0000000
lambda =    0.15903; f = 87709189385267267345506967224320.0000000
lambda =    0.14601; f = 64576920239786430471002658963456.0000000
lambda =    0.15369; f = 87709189385267267345506967224320.0000000
lambda =    0.14904; f = 87709189385267267345506967224320.0000000
lambda =    0.14631; f = 64533078394086262408719439495168.0000000
lambda =    0.14794; f = 64295033271659487250434704277504.0000000
lambda =    0.14959; f = 87709189385267267345506967224320.0000000
Norm of dx     4.3083
Done for param ew =   0.9993
Predicted improvement: 56579490544430856112665184436224.000000000
lambda =          1; f = 87709189385267267345506967224320.0000000
lambda =    0.33333; f = 32107576005598429080093802364928.0000000
lambda =    0.64439; f = 87709189385267267345506967224320.0000000
lambda =     0.4339; f = 24569369100674346321817183977472.0000000
lambda =    0.55011; f = 87709189385267267345506967224320.0000000
lambda =     0.4771; f = 87709189385267267345506967224320.0000000
lambda =    0.43804; f = 24280749996120387910036956905472.0000000
lambda =    0.46107; f = 87709189385267267345506967224320.0000000
lambda =    0.44711; f = 87709189385267267345506967224320.0000000
lambda =    0.43894; f = 24218273144790233737548926025728.0000000
lambda =    0.44382; f = 23880083412293181436438163488768.0000000
lambda =    0.44876; f = 87709189385267267345506967224320.0000000
Norm of dx     4.4727
Done for param em1 =   1.9875
Predicted improvement: 7977662690087520642157212860416.000000000
lambda =          1; f = 23881384101279763708733915398144.0000000
lambda =    0.33333; f = 23880516971352738426600401403904.0000000
lambda =    0.11111; f = 23880227931542405412940861669376.0000000
lambda =   0.037037; f = 23880131585327818380080150740992.0000000
lambda =   0.012346; f = 23880099469965896357040348987392.0000000
lambda =  0.0041152; f = 23880088764850062524696028512256.0000000
lambda =  0.0013717; f = 23880085196478724398664407580672.0000000
lambda = 0.00045725; f = 23880084007021709267979126964224.0000000
lambda = 0.00015242; f = 23880083610535858914965481062400.0000000
lambda = 5.0805e-05; f = 23880083478373904293694638391296.0000000
lambda = 1.6935e-05; f = 23880083434319805328747130781696.0000000
lambda =  5.645e-06; f = 23880083419635517002530594750464.0000000
lambda = 1.8817e-06; f = 23880083414740419459552781533184.0000000
lambda = 6.2723e-07; f = 23880083413108787832554587684864.0000000
lambda = 2.0908e-07; f = 23880083412564991688348482404352.0000000
lambda = 6.9692e-08; f = 23880083412383969501326325317632.0000000
lambda = 2.3231e-08; f = 23880083412323324028744154218496.0000000
lambda = 7.7435e-09; f = 23880083412303404607592294514688.0000000
lambda = 2.5812e-09; f = 23880083412296320445378440724480.0000000

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f = 87709189385267267345506967224320.0000000
lambda = -2.0908e-07; f = 87709189385267267345506967224320.0000000
lambda = -6.9692e-08; f = 87709189385267267345506967224320.0000000
lambda = -2.3231e-08; f = 87709189385267267345506967224320.0000000
lambda = -7.7435e-09; f = 87709189385267267345506967224320.0000000
lambda = -2.5812e-09; f = 87709189385267267345506967224320.0000000
Norm of dx 4.9996e-07
Done for param elr =   0.0001
Predicted improvement: 8500542570740298135586306260992.000000000
lambda =          1; f = 87709189385267267345506967224320.0000000
lambda =    0.33333; f = 16711509692608111426910134730752.0000000
lambda =    0.64439; f = 6284730943432174620358616285184.0000000
lambda =     1.2457; f = 87709189385267267345506967224320.0000000
lambda =     0.8388; f = 58525552557493556841220669440.0000000
lambda =     1.0635; f = 87709189385267267345506967224320.0000000
lambda =    0.92232; f = 2260610977895829693507563421696.0000000
lambda =     1.0046; f = 87709189385267267345506967224320.0000000
lambda =    0.95439; f = 87709189385267267345506967224320.0000000
lambda =     0.9067; f = 1274836163320300495796369883136.0000000
lambda =    0.93502; f = 3306957241489803968055122853888.0000000
lambda =    0.96422; f = 87709189385267267345506967224320.0000000
lambda =    0.94659; f = 4434877754703821149242925252608.0000000
lambda =    0.95713; f = 87709189385267267345506967224320.0000000
Norm of dx    0.35574
Done for param rhow =   0.9584
Predicted improvement: 68564667182049009664046137344.000000000
lambda =          1; f = 4992090745689857621745467392.0000000
Norm of dx   0.014842
Done for param rhocm =   0.6148
Predicted improvement: 4954105994591023552490635264.000000000
lambda =          1; f = 1048068955615518103961600.0000000
Norm of dx  0.0061044
Done for param nu1 =   0.0039
Sequence of univariate steps!!
Actual dxnorm 2.1062
FVAL          1.048068955615518e+24
Improvement   8.770918833719832e+31
Ftol          1e-06
Htol          1e-06
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  4.591238e-37.] 
[> In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('newrat', 'D:\Dynare\5.2\matlab\optimization\newrat.m', 272)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\optimization\newrat.m',272,0)">line 272</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('dynare_minimize_objective', 'D:\Dynare\5.2\matlab\optimization\dynare_minimize_objective.m', 326)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\optimization\dynare_minimize_objective.m',326,0)">line 326</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('mom.run', 'D:\Dynare\5.2\matlab\+mom\run.m', 919)" style="font-weight:bold">mom.run</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\+mom\run.m',919,0)">line 919</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('ClimateDSGE_EZ_LRR_SMM5.driver', 'E:\Dropbox Folder\Dropbox\Climate Policy Uncertainty and DSGE\Model Quantitative\Climate DSGE Code 2\+ClimateDSGE_EZ_LRR_SMM5\driver.m', 888)" style="font-weight:bold">ClimateDSGE_EZ_LRR_SMM5.driver</a> (<a href="matlab: opentoline('E:\Dropbox Folder\Dropbox\Climate Policy Uncertainty and DSGE\Model Quantitative\Climate DSGE Code 2\+ClimateDSGE_EZ_LRR_SMM5\driver.m',888,0)">line 888</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('dynare', 'D:\Dynare\5.2\matlab\dynare.m', 281)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\dynare.m',281,0)">line 281</a>)
] 
Elapsed time for iteration 2.8467 s.
 
Iteration 2
Near-singular H problem.
Correct for low angle: 5.87101e-56
Predicted improvement: 946638616018816932347956131535124353826098800330227535625574765183585995283825860542464.000000000
lambda =          1; f = 1223770673373348741962589257066324888215191854372182130339319119872.0000000
lambda =    0.33333; f = 135974519263705428764746693726506482723330941297202633645991395328.0000000
lambda =    0.11111; f = 15108279918189491110637429971232108166802438333278279523377348608.0000000
lambda =   0.037037; f = 1678697768687721518696143922256912780361210620752618213473255424.0000000
lambda =   0.012346; f = 186521974298635650717023834076836441035168977354097214653202432.0000000
lambda =  0.0041152; f = 20724663810959515477671365825461932507653469084237347065692160.0000000
lambda =  0.0013717; f = 2302740423439946481351861248597966069358378664803068870131712.0000000
lambda = 0.00045725; f = 255860047048882907682380941018804676428702538636223087378432.0000000
lambda = 0.00015242; f = 28428894116542558306810359256064018832358163337791221530624.0000000
lambda = 5.0805e-05; f = 3158766012949172448302863574369584475048599906159949774848.0000000
lambda = 1.6935e-05; f = 350974001438797001614746903423465297567105185846917070848.0000000
lambda =  5.645e-06; f = 38997111270977451278271497945403874124559565982013587456.0000000
lambda = 1.8817e-06; f = 4333012363441938501590929005807265070812784659695403008.0000000
lambda = 6.2723e-07; f = 481445818160215502493114418735850606770734106111115264.0000000
lambda = 2.0908e-07; f = 53493979795579499095476716939613735948919735985373184.0000000
lambda = 6.9692e-08; f = 5943775532842166418472080128299762560568075078336512.0000000
lambda = 2.3231e-08; f = 660419503649129685129203084146104563217727387336704.0000000
lambda = 7.7435e-09; f = 73379944849903291424626753525352557872419048521728.0000000
lambda = 2.5812e-09; f = 8153327205544811023674671258621555629795949150208.0000000

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f = 7332390886874074475171769959539171055289135409267442673330334929853079758045184.0000000
lambda = -2.0908e-07; f = 814710098541564013408480808971690356695368662957549739562911173714771456294912.0000000
lambda = -6.9692e-08; f = 90523344282395984349158728900958211629667533133993321936824863396295474151424.0000000
lambda = -2.3231e-08; f = 10058149364710667427365705391869118801904091767807750805296308483932796485632.0000000
lambda = -7.7435e-09; f = 1117572151634518759270721568720623322346769174036114223580436449319614152704.0000000
lambda = -2.5812e-09; f = 124174683514946514858742984554112005070108967764753989059258617782102654976.0000000
Norm of dx 4.3172e+45
Predicted improvement: 1048067815373877404499968.000000000
lambda =          1; f = 1239311205917.1643066
Norm of dx 0.00015204
Done for param ew =   0.9991
Predicted improvement: 1239368094382.796142578
lambda =          1; f =         7335.9274317
Norm of dx 6.4973e-10
Done for param em1 =   1.9875
Predicted improvement: 107409080001802583146496.000000000
lambda =          1; f = 13186376939466548838400.0000000
lambda =    0.33333; f = 1465152995010514518016.0000000
lambda =    0.11111; f = 162794775616179503104.0000000
lambda =   0.037037; f = 18088308470530875392.0000000
lambda =   0.012346; f = 2009812235280261632.0000000
lambda =  0.0041152; f = 223312379778808416.0000000
lambda =  0.0013717; f = 24812496241453132.0000000
lambda = 0.00045725; f = 2756940275163170.5000000
lambda = 0.00015242; f = 306323487342501.8125000
lambda = 5.0805e-05; f = 34036258819811.2812500
lambda = 1.6935e-05; f = 3781707346740.1308594
lambda =  5.645e-06; f = 420186935750.1475830
lambda = 1.8817e-06; f =  46692618303.1851349
lambda = 6.2723e-07; f =   5188467647.6580582
lambda = 2.0908e-07; f =    574624936.7338171
lambda = 6.9692e-08; f =     63206339.0237639
lambda = 2.3231e-08; f =      6841046.7953037
lambda = 7.7435e-09; f =       783020.4353890
lambda = 2.5812e-09; f =        84175.0857279

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f = 1048068955615518103961600.0000000
lambda = -2.0908e-07; f = 1048068955615518103961600.0000000
lambda = -6.9692e-08; f = 1048068955615518103961600.0000000
lambda = -2.3231e-08; f = 1048068955615518103961600.0000000
lambda = -7.7435e-09; f = 1048068955615518103961600.0000000
lambda = -2.5812e-09; f = 1048068955615518103961600.0000000
Norm of dx 4.3862e-07
Done for param elr =   0.0001
Predicted improvement: 97658428571.835403442
lambda =          1; f =  98706295928.3909302
lambda =    0.33333; f =  11053441235.9585457
lambda =    0.11111; f =   1237620814.0480812
lambda =   0.037037; f =    154795374.9090815
lambda =   0.012346; f =       149657.1387091
lambda =  0.0041152; f =      9674201.0000436
lambda =  0.0013717; f =       113277.1994263
lambda = 0.00045725; f =       100363.6820801
lambda = 0.00015242; f =      4220172.3092130
lambda = 5.0805e-05; f =       296182.2906580
lambda = 1.6935e-05; f =        11218.9253063
lambda =  5.645e-06; f =         7335.9274317
lambda = 1.8817e-06; f =         7335.9274317
lambda = 6.2723e-07; f =         7335.9274317
lambda = 2.0908e-07; f =         7335.9274317
lambda = 6.9692e-08; f =         7335.9274317
lambda = 2.3231e-08; f =         7335.9274317
lambda = 7.7435e-09; f =         7335.9274317
lambda = 2.5812e-09; f =         7335.9274317

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =         7335.9274317
Norm of dx 6.5154e-12
Done for param rhow =   0.9584
Predicted improvement: 26514123215.112384796
lambda =          1; f =  27448855370.6931229
lambda =    0.33333; f =   3676495929.8182712
lambda =    0.11111; f =    490061302.8906434
lambda =   0.037037; f =    252326409.8338335
lambda =   0.012346; f =    116284073.1880933
lambda =  0.0041152; f =    149979157.1675735
lambda =  0.0013717; f =       102516.8205913
lambda = 0.00045725; f =     11595404.6230529
lambda = 0.00015242; f =     76212073.7424928
lambda = 5.0805e-05; f =     20660366.0924141
lambda = 1.6935e-05; f =     11016271.5912718
lambda =  5.645e-06; f =         7335.9274317
lambda = 1.8817e-06; f =         7335.9274317
lambda = 6.2723e-07; f =         7335.9274317
lambda = 2.0908e-07; f =         7335.9274317
lambda = 6.9692e-08; f =         7335.9274317
lambda = 2.3231e-08; f =         7335.9274317
lambda = 7.7435e-09; f =         7335.9274317
lambda = 2.5812e-09; f =         7335.9274317

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =         7335.9274317
Norm of dx 7.0554e-12
Done for param rhocm =   0.6148
Predicted improvement: 70390556.967196971
lambda =          1; f =    241815029.7898576
lambda =    0.33333; f =    130907049.7501352
lambda =    0.11111; f =     70854024.4490163
lambda =   0.037037; f =     30849484.5829497
lambda =   0.012346; f =      6636467.7784682
lambda =  0.0041152; f =      1981427.2152341
lambda =  0.0013717; f =     33313425.9032134
lambda = 0.00045725; f =      9389383.3338284
lambda = 0.00015242; f =      1892179.0901057
lambda = 5.0805e-05; f =     21926785.1130681
lambda = 1.6935e-05; f =     59803387.6760109
lambda =  5.645e-06; f =      3425983.9119254
lambda = 1.8817e-06; f =      8606713.9099685
lambda = 6.2723e-07; f =     15187880.7700662
lambda = 2.0908e-07; f =         7335.9274317
lambda = 6.9692e-08; f =         7335.9274317
lambda = 2.3231e-08; f =         7335.9274317
lambda = 7.7435e-09; f =         7335.9274317
lambda = 2.5812e-09; f =         7335.9274317

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =    107258339.7224997
lambda = -2.0908e-07; f =         7335.9274317
Norm of dx 7.4074e-13
Done for param nu1 =   0.0039
Sequence of univariate steps!!
Actual dxnorm 0.00015204
FVAL          7335.9274
Improvement   1.048068955615518e+24
Ftol          1e-06
Htol          1e-06
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  8.806783e-44.] 
[> In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('newrat', 'D:\Dynare\5.2\matlab\optimization\newrat.m', 272)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\optimization\newrat.m',272,0)">line 272</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('dynare_minimize_objective', 'D:\Dynare\5.2\matlab\optimization\dynare_minimize_objective.m', 326)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\optimization\dynare_minimize_objective.m',326,0)">line 326</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('mom.run', 'D:\Dynare\5.2\matlab\+mom\run.m', 919)" style="font-weight:bold">mom.run</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\+mom\run.m',919,0)">line 919</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('ClimateDSGE_EZ_LRR_SMM5.driver', 'E:\Dropbox Folder\Dropbox\Climate Policy Uncertainty and DSGE\Model Quantitative\Climate DSGE Code 2\+ClimateDSGE_EZ_LRR_SMM5\driver.m', 888)" style="font-weight:bold">ClimateDSGE_EZ_LRR_SMM5.driver</a> (<a href="matlab: opentoline('E:\Dropbox Folder\Dropbox\Climate Policy Uncertainty and DSGE\Model Quantitative\Climate DSGE Code 2\+ClimateDSGE_EZ_LRR_SMM5\driver.m',888,0)">line 888</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('dynare', 'D:\Dynare\5.2\matlab\dynare.m', 281)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\dynare.m',281,0)">line 281</a>)
] 
Elapsed time for iteration 3.9117 s.
 
Iteration 3
Near-singular H problem.
Correct for low angle: 1.88893e-54
Predicted improvement: 217780906163210179707748123059751909744040841724211934056643589858809348096.000000000
lambda =          1; f = 28311554675648913932579746993471322083512832491520.0000000
lambda =    0.33333; f = 3145728297294322544327658067440290161800903655424.0000000
lambda =    0.11111; f = 349525366366035847273032942449663539732212088832.0000000
lambda =   0.037037; f = 38836151818448434798984906035288046395642085376.0000000
lambda =   0.012346; f = 4315127979827603162303544988237893212458450944.0000000
lambda =  0.0041152; f = 479458664425289319484111957401881283817111552.0000000
lambda =  0.0013717; f = 53273184936143242314980839715476721679466496.0000000
lambda = 0.00045725; f = 5919242770682583442284568301459849552592896.0000000
lambda = 0.00015242; f = 657693641186953930729542187207392120274944.0000000
lambda = 5.0805e-05; f = 73077071242994874744566982856132414930944.0000000
lambda = 1.6935e-05; f = 8119674582554988903556577195927231528960.0000000
lambda =  5.645e-06; f = 902186064728331982860720559236300406784.0000000
lambda = 1.8817e-06; f = 100242896080925784268731587238958202880.0000000
lambda = 6.2723e-07; f = 11138099564547310937314559538654871552.0000000
lambda = 2.0908e-07; f = 1237566618283034319031024809242787840.0000000
lambda = 6.9692e-08; f = 137507402031448251521199176457125888.0000000
lambda = 2.3231e-08; f = 15278600225716474440882583351853056.0000000
lambda = 7.7435e-09; f = 1697622247301830653559384901156864.0000000
lambda = 2.5812e-09; f = 188624694144647858401886548787200.0000000

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f = 2717861034637585608420952561490042135416786600585803571355516034613248.0000000
lambda = -2.0908e-07; f = 301984559404176258530915260263938276765772841921470376959942173130752.0000000
lambda = -6.9692e-08; f = 33553839933797360728699316361020174537909632478853319993171420119040.0000000
lambda = -2.3231e-08; f = 3728204437088594971376512205960202397290728630328674609163524898816.0000000
lambda = -7.7435e-09; f = 414244937454288361331647397054840299155357390206112709233619238912.0000000
lambda = -2.5812e-09; f = 46027215272698707464183771819827996885566302341249420873038823424.0000000
Norm of dx 8.3117e+40
Predicted improvement:     2009.489958697
lambda =          1; f =         6981.9522355
lambda =    0.33333; f =         7027.9813249
lambda =    0.11111; f =         7231.0171395
lambda =   0.037037; f =         8057.3590228
lambda =   0.012346; f =         8156.2903117
lambda =  0.0041152; f =         7335.9274317
lambda =  0.0013717; f =         7335.9274317
lambda = 0.00045725; f =         7335.9274317
lambda = 0.00015242; f =         7335.9274317
lambda = 5.0805e-05; f =         7335.9274317
lambda = 1.6935e-05; f =         7335.9274317
lambda =  5.645e-06; f =         7335.9274317
lambda = 1.8817e-06; f =         7335.9274317
lambda = 6.2723e-07; f =         7335.9274317
lambda = 2.0908e-07; f =         7335.9274317
lambda = 6.9692e-08; f =         7335.9274317
lambda = 2.3231e-08; f =         7335.9274317
lambda = 7.7435e-09; f =         7335.9274317
lambda = 2.5812e-09; f =         7335.9274317
Norm of dx 6.6572e-15
Done for param ew =   0.9991
Predicted improvement:        1.042719403
lambda =          1; f =         7419.4273502
lambda =    0.33333; f =         7184.9586034
lambda =    0.11111; f =         6981.9522355
lambda =   0.037037; f =         6981.9522355
lambda =   0.012346; f =         6981.9522355
lambda =  0.0041152; f =         6981.9522355
lambda =  0.0013717; f =         6981.9522355
lambda = 0.00045725; f =         6981.9522355
lambda = 0.00015242; f =         6981.9522355
lambda = 5.0805e-05; f =         6981.9522355
lambda = 1.6935e-05; f =         6981.9522355
lambda =  5.645e-06; f =         6981.9522355
lambda = 1.8817e-06; f =         6981.9522355
lambda = 6.2723e-07; f =         6981.9522355
lambda = 2.0908e-07; f =         6981.9522355
lambda = 6.9692e-08; f =         6981.9522355
lambda = 2.3231e-08; f =         6981.9522355
lambda = 7.7435e-09; f =         6981.9522355
lambda = 2.5812e-09; f =         6981.9522355

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =         6981.9522355
Norm of dx 5.9596e-16
Done for param em1 =   1.9875
Predicted improvement: 8567741713293104906240.000000000
lambda =          1; f =         7335.9274317
lambda =    0.33333; f =         7335.9274317
lambda =    0.11111; f =         7335.9274317
lambda =   0.037037; f =         7335.9274317
lambda =   0.012346; f =         7335.9274317
lambda =  0.0041152; f =         7335.9274317
lambda =  0.0013717; f =         7335.9274317
lambda = 0.00045725; f =         7335.9274317
lambda = 0.00015242; f =         7335.9274317
lambda = 5.0805e-05; f =         7335.9274317
lambda = 1.6935e-05; f =         7335.9274317
lambda =  5.645e-06; f =         7335.9274317
lambda = 1.8817e-06; f =         7335.9274317
lambda = 6.2723e-07; f =         7335.9274317
lambda = 2.0908e-07; f =         7335.9274317
lambda = 6.9692e-08; f =         7335.9274317
lambda = 2.3231e-08; f =         7335.9274317
lambda = 7.7435e-09; f =         7335.9274317
lambda = 2.5812e-09; f =         7335.9274317

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =   6743385615.1239471
lambda = -2.0908e-07; f =    751954328.7023270
lambda = -6.9692e-08; f =     83037839.8240447
lambda = -2.3231e-08; f =      9305496.8715944
lambda = -7.7435e-09; f =      1162908.6675455
lambda = -2.5812e-09; f =       195419.2704901
Norm of dx      5e-07
Done for param elr =   0.0001
Predicted improvement: 97605502663.879837036
lambda =          1; f =  98765026100.7040558
lambda =    0.33333; f =  10898320767.3944359
lambda =    0.11111; f =   1202632062.9231424
lambda =   0.037037; f =    155049817.5736075
lambda =   0.012346; f =     17002808.4187044
lambda =  0.0041152; f =      3308215.4353359
lambda =  0.0013717; f =       685927.4577865
lambda = 0.00045725; f =       155903.4758305
lambda = 0.00015242; f =      4250197.9814868
lambda = 5.0805e-05; f =       340142.4322267
lambda = 1.6935e-05; f =         8433.6239761
lambda =  5.645e-06; f =         6981.9522355
lambda = 1.8817e-06; f =         6981.9522355
lambda = 6.2723e-07; f =         6981.9522355
lambda = 2.0908e-07; f =         6981.9522355
lambda = 6.9692e-08; f =         6981.9522355
lambda = 2.3231e-08; f =         6981.9522355
lambda = 7.7435e-09; f =         6981.9522355
lambda = 2.5812e-09; f =         6981.9522355

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =         6981.9522355
Norm of dx 6.5137e-12
Done for param rhow =   0.9584
Predicted improvement: 26492966108.269081116
lambda =          1; f =  27669932107.9476318
lambda =    0.33333; f =   3114711183.3066230
lambda =    0.11111; f =    545735067.2381730
lambda =   0.037037; f =    438176542.3265642
lambda =   0.012346; f =      3150948.2953303
lambda =  0.0041152; f =     22964251.4979783
lambda =  0.0013717; f =        71290.3885884
lambda = 0.00045725; f =     11865382.5797820
lambda = 0.00015242; f =     77363617.1805004
lambda = 5.0805e-05; f =     21208107.0683132
lambda = 1.6935e-05; f =     10929234.1728150
lambda =  5.645e-06; f =         6981.9522355
lambda = 1.8817e-06; f =         6981.9522355
lambda = 6.2723e-07; f =         6981.9522355
lambda = 2.0908e-07; f =         6981.9522355
lambda = 6.9692e-08; f =         6981.9522355
lambda = 2.3231e-08; f =         6981.9522355
lambda = 7.7435e-09; f =         6981.9522355
lambda = 2.5812e-09; f =         6981.9522355

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =         6981.9522355
Norm of dx 7.0526e-12
Done for param rhocm =   0.6148
Predicted improvement: 70182444.025914714
lambda =          1; f =    178961567.8208092
lambda =    0.33333; f =     33380876.8873504
lambda =    0.11111; f =     94420319.2814585
lambda =   0.037037; f =       291489.7287815
lambda =   0.012346; f =     15611812.3639651
lambda =  0.0041152; f =      1353545.3099722
lambda =  0.0013717; f =       479365.0822755
lambda = 0.00045725; f =     23626623.1529626
lambda = 0.00015242; f =      1965046.7647543
lambda = 5.0805e-05; f =     22063728.0322386
lambda = 1.6935e-05; f =     58344500.2481464
lambda =  5.645e-06; f =      3523875.1246085
lambda = 1.8817e-06; f =      8563946.1612732
lambda = 6.2723e-07; f =     15359041.3026630
lambda = 2.0908e-07; f =         6981.9522355
lambda = 6.9692e-08; f =         6981.9522355
lambda = 2.3231e-08; f =         6981.9522355
lambda = 7.7435e-09; f =         6981.9522355
lambda = 2.5812e-09; f =         6981.9522355

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =    109783318.1532190
lambda = -2.0908e-07; f =         6981.9522355
Norm of dx 7.3964e-13
Done for param nu1 =   0.0039
Sequence of univariate steps!!
Actual dxnorm 6.6613e-15
FVAL          6981.9522
Improvement   353.9752
Ftol          1e-06
Htol          1e-06
Elapsed time for iteration 2.7305 s.
 
Iteration 4
Near-singular H problem.
Correct for low angle: 1.88893e-54
Predicted improvement: 217780906163210179707748123059751909744040841724211934056643589858809348096.000000000
lambda =          1; f = 28311554675648913932579746993471322083512832491520.0000000
lambda =    0.33333; f = 3145728297294322544327658067440290161800903655424.0000000
lambda =    0.11111; f = 349525366366035847273032942449663539732212088832.0000000
lambda =   0.037037; f = 38836151818448434798984906035288046395642085376.0000000
lambda =   0.012346; f = 4315127979827603162303544988237893212458450944.0000000
lambda =  0.0041152; f = 479458664425289319484111957401881283817111552.0000000
lambda =  0.0013717; f = 53273184936143242314980839715476721679466496.0000000
lambda = 0.00045725; f = 5919242770682583442284568301459849552592896.0000000
lambda = 0.00015242; f = 657693641186953930729542187207392120274944.0000000
lambda = 5.0805e-05; f = 73077071242994874744566982856132414930944.0000000
lambda = 1.6935e-05; f = 8119674582554988903556577195927231528960.0000000
lambda =  5.645e-06; f = 902186064728331982860720559236300406784.0000000
lambda = 1.8817e-06; f = 100242896080925784268731587238958202880.0000000
lambda = 6.2723e-07; f = 11138099564547310937314559538654871552.0000000
lambda = 2.0908e-07; f = 1237566618283034319031024809242787840.0000000
lambda = 6.9692e-08; f = 137507402031448251521199176457125888.0000000
lambda = 2.3231e-08; f = 15278600225716474440882583351853056.0000000
lambda = 7.7435e-09; f = 1697622247301830653559384901156864.0000000
lambda = 2.5812e-09; f = 188624694144647858401886548787200.0000000

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f = 2717861034637585608420952561490042135416786600585803571355516034613248.0000000
lambda = -2.0908e-07; f = 301984559404176258530915260263938276765772841921470376959942173130752.0000000
lambda = -6.9692e-08; f = 33553839933797360728699316361020174537909632478853319993171420119040.0000000
lambda = -2.3231e-08; f = 3728204437088594971376512205960202397290728630328674609163524898816.0000000
lambda = -7.7435e-09; f = 414244937454288361331647397054840299155357390206112709233619238912.0000000
lambda = -2.5812e-09; f = 46027215272698707464183771819827996885566302341249420873038823424.0000000
Norm of dx 8.3117e+40
Predicted improvement:      291.842874332
lambda =          1; f =        16050.8219198
lambda =    0.33333; f =         8019.1118740
lambda =    0.11111; f =         7926.1448651
lambda =   0.037037; f =         8217.8391118
lambda =   0.012346; f =         6981.9522355
lambda =  0.0041152; f =         6981.9522355
lambda =  0.0013717; f =         6981.9522355
lambda = 0.00045725; f =         6981.9522355
lambda = 0.00015242; f =         6981.9522355
lambda = 5.0805e-05; f =         6981.9522355
lambda = 1.6935e-05; f =         6981.9522355
lambda =  5.645e-06; f =         6981.9522355
lambda = 1.8817e-06; f =         6981.9522355
lambda = 6.2723e-07; f =         6981.9522355
lambda = 2.0908e-07; f =         6981.9522355
lambda = 6.9692e-08; f =         6981.9522355
lambda = 2.3231e-08; f =         6981.9522355
lambda = 7.7435e-09; f =         6981.9522355
lambda = 2.5812e-09; f =         6981.9522355

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =         6981.9522355
Norm of dx  2.537e-15
Done for param ew =   0.9991
Predicted improvement:        1.042719403
lambda =          1; f =         7419.4273502
lambda =    0.33333; f =         7184.9586034
lambda =    0.11111; f =         6981.9522355
lambda =   0.037037; f =         6981.9522355
lambda =   0.012346; f =         6981.9522355
lambda =  0.0041152; f =         6981.9522355
lambda =  0.0013717; f =         6981.9522355
lambda = 0.00045725; f =         6981.9522355
lambda = 0.00015242; f =         6981.9522355
lambda = 5.0805e-05; f =         6981.9522355
lambda = 1.6935e-05; f =         6981.9522355
lambda =  5.645e-06; f =         6981.9522355
lambda = 1.8817e-06; f =         6981.9522355
lambda = 6.2723e-07; f =         6981.9522355
lambda = 2.0908e-07; f =         6981.9522355
lambda = 6.9692e-08; f =         6981.9522355
lambda = 2.3231e-08; f =         6981.9522355
lambda = 7.7435e-09; f =         6981.9522355
lambda = 2.5812e-09; f =         6981.9522355

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =         6981.9522355
Norm of dx 5.9596e-16
Done for param em1 =   1.9875
Predicted improvement: 8567741713293104906240.000000000
lambda =          1; f =         6981.9522355
lambda =    0.33333; f =         6981.9522355
lambda =    0.11111; f =         6981.9522355
lambda =   0.037037; f =         6981.9522355
lambda =   0.012346; f =         6981.9522355
lambda =  0.0041152; f =         6981.9522355
lambda =  0.0013717; f =         6981.9522355
lambda = 0.00045725; f =         6981.9522355
lambda = 0.00015242; f =         6981.9522355
lambda = 5.0805e-05; f =         6981.9522355
lambda = 1.6935e-05; f =         6981.9522355
lambda =  5.645e-06; f =         6981.9522355
lambda = 1.8817e-06; f =         6981.9522355
lambda = 6.2723e-07; f =         6981.9522355
lambda = 2.0908e-07; f =         6981.9522355
lambda = 6.9692e-08; f =         6981.9522355
lambda = 2.3231e-08; f =         6981.9522355
lambda = 7.7435e-09; f =         6981.9522355
lambda = 2.5812e-09; f =         6981.9522355

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =   6743385615.1239471
lambda = -2.0908e-07; f =    751954328.7023270
lambda = -6.9692e-08; f =     83037839.8240447
lambda = -2.3231e-08; f =      9305496.8715944
lambda = -7.7435e-09; f =      1162908.6675455
lambda = -2.5812e-09; f =       195419.2704901
Norm of dx      5e-07
Done for param elr =   0.0001
Predicted improvement: 97605502663.879837036
lambda =          1; f =  98765026100.7040558
lambda =    0.33333; f =  10898320767.3944359
lambda =    0.11111; f =   1202632062.9231424
lambda =   0.037037; f =    155049817.5736075
lambda =   0.012346; f =     17002808.4187044
lambda =  0.0041152; f =      3308215.4353359
lambda =  0.0013717; f =       685927.4577865
lambda = 0.00045725; f =       155903.4758305
lambda = 0.00015242; f =      4250197.9814868
lambda = 5.0805e-05; f =       340142.4322267
lambda = 1.6935e-05; f =         8433.6239761
lambda =  5.645e-06; f =         6981.9522355
lambda = 1.8817e-06; f =         6981.9522355
lambda = 6.2723e-07; f =         6981.9522355
lambda = 2.0908e-07; f =         6981.9522355
lambda = 6.9692e-08; f =         6981.9522355
lambda = 2.3231e-08; f =         6981.9522355
lambda = 7.7435e-09; f =         6981.9522355
lambda = 2.5812e-09; f =         6981.9522355

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =         6981.9522355
Norm of dx 6.5137e-12
Done for param rhow =   0.9584
Predicted improvement: 26492966108.269081116
lambda =          1; f =  27669932107.9476318
lambda =    0.33333; f =   3114711183.3066230
lambda =    0.11111; f =    545735067.2381730
lambda =   0.037037; f =    438176542.3265642
lambda =   0.012346; f =      3150948.2953303
lambda =  0.0041152; f =     22964251.4979783
lambda =  0.0013717; f =        71290.3885884
lambda = 0.00045725; f =     11865382.5797820
lambda = 0.00015242; f =     77363617.1805004
lambda = 5.0805e-05; f =     21208107.0683132
lambda = 1.6935e-05; f =     10929234.1728150
lambda =  5.645e-06; f =         6981.9522355
lambda = 1.8817e-06; f =         6981.9522355
lambda = 6.2723e-07; f =         6981.9522355
lambda = 2.0908e-07; f =         6981.9522355
lambda = 6.9692e-08; f =         6981.9522355
lambda = 2.3231e-08; f =         6981.9522355
lambda = 7.7435e-09; f =         6981.9522355
lambda = 2.5812e-09; f =         6981.9522355

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =         6981.9522355
Norm of dx 7.0526e-12
Done for param rhocm =   0.6148
Predicted improvement: 70182444.025914714
lambda =          1; f =    178961567.8208092
lambda =    0.33333; f =     33380876.8873504
lambda =    0.11111; f =     94420319.2814585
lambda =   0.037037; f =       291489.7287815
lambda =   0.012346; f =     15611812.3639651
lambda =  0.0041152; f =      1353545.3099722
lambda =  0.0013717; f =       479365.0822755
lambda = 0.00045725; f =     23626623.1529626
lambda = 0.00015242; f =      1965046.7647543
lambda = 5.0805e-05; f =     22063728.0322386
lambda = 1.6935e-05; f =     58344500.2481464
lambda =  5.645e-06; f =      3523875.1246085
lambda = 1.8817e-06; f =      8563946.1612732
lambda = 6.2723e-07; f =     15359041.3026630
lambda = 2.0908e-07; f =         6981.9522355
lambda = 6.9692e-08; f =         6981.9522355
lambda = 2.3231e-08; f =         6981.9522355
lambda = 7.7435e-09; f =         6981.9522355
lambda = 2.5812e-09; f =         6981.9522355

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =    109783318.1532190
lambda = -2.0908e-07; f =         6981.9522355
Norm of dx 7.3964e-13
Done for param nu1 =   0.0039
Sequence of univariate steps!!
Try diagonal Hessian
Near-singular H problem.
Correct for low angle: 3.64099e-33
Predicted improvement: 46896466488209030216837053359178362466562099858928304128.000000000
lambda =          1; f = 1315058532724.7980957
lambda =    0.33333; f = 146116915615.3565979
lambda =    0.11111; f =  16234983942.5885029
lambda =   0.037037; f =   1803814943.9301512
lambda =   0.012346; f =    200403969.4437752
lambda =  0.0041152; f =     22264610.3631026
lambda =  0.0013717; f =      2477153.5304473
lambda = 0.00045725; f =       280482.3115142
lambda = 0.00015242; f =        37052.7635683
lambda = 5.0805e-05; f =        10220.0466639
lambda = 1.6935e-05; f =         7310.3038896
lambda =  5.645e-06; f =         7010.4462653
lambda = 1.8817e-06; f =         6983.1915704
lambda = 6.2723e-07; f = 126028244985930537114256208297984.0000000
lambda = 2.0908e-07; f = 14003138331770033158163995295744.0000000
lambda = 6.9692e-08; f = 1555904259085550545346718859264.0000000
lambda = 2.3231e-08; f = 172878251009502910862359265280.0000000
lambda = 7.7435e-09; f = 19208694556610505819440545792.0000000
lambda = 2.5812e-09; f = 2134299395178637747006996480.0000000

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f = 126028244985930663215045774671872.0000000
lambda = -2.0908e-07; f = 14003138331770073690560641630208.0000000
lambda = -6.9692e-08; f = 1555904259085563493195647549440.0000000
lambda = -2.3231e-08; f = 172878251009507062618265747456.0000000
lambda = -7.7435e-09; f = 19208694556611900000184565760.0000000
lambda = -2.5812e-09; f = 2134299395179100366524383232.0000000
Norm of dx 1.7898e+22
Try gradient direction
Near-singular H problem.
Correct for low angle: 1.90827e-27
Predicted improvement: 6865303339301713727080141656687783977612921091888814342799360.000000000
lambda =          1; f = 28134783625129663397888.0000000
lambda =    0.33333; f = 3126087069355660017664.0000000
lambda =    0.11111; f = 347343007671786930176.0000000
lambda =   0.037037; f = 38593667507621683200.0000000
lambda =   0.012346; f = 4288185274802709504.0000000
lambda =  0.0041152; f = 476465029259665024.0000000
lambda =  0.0013717; f = 52940558381977160.0000000
lambda = 0.00045725; f = 5882284123117496.0000000
lambda = 0.00015242; f = 653587077612763.5000000
lambda = 5.0805e-05; f = 72620770679535.3125000
lambda = 1.6935e-05; f = 8068969283461.1367188
lambda =  5.645e-06; f = 896550401251.4345703
lambda = 1.8817e-06; f =  99616134938.7587128
lambda = 6.2723e-07; f =  11068271474.2251434
lambda = 2.0908e-07; f =   1229749427.4945052
lambda = 6.9692e-08; f =    136623460.9673161
lambda = 2.3231e-08; f =     15179403.5008114
lambda = 7.7435e-09; f =      1690413.7574459
lambda = 2.5812e-09; f =       193235.2591541

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f = 2700891314871213142089929953963583085740032.0000000
lambda = -2.0908e-07; f = 300099034985690366314714984959568605347840.0000000
lambda = -6.9692e-08; f = 33344337220632258625454306365715001638912.0000000
lambda = -2.3231e-08; f = 3704926357848029071974317266920882044928.0000000
lambda = -7.7435e-09; f = 411658484205336588738656493851539144704.0000000
lambda = -2.5812e-09; f = 45739831578370735230317265674378936320.0000000
Norm of dx 2.6202e+27
No further improvement is possible!
Actual dxnorm 0
FVAL          6981.9522
Improvement   0
Ftol          1e-06
Htol          1e-06
Gradient norm  5.240344774650505e+33
Minimum Hessian eigenvalue -3672.5214
Maximum Hessian eigenvalue 1.123211270809813e+20
Estimation successful.

Stage 1 Iteration 1: value of minimized moment distance objective function: 6981.9522355420.

Computing standard errors using numerical derivatives of moments
[Warning: Cannot compute the Jacobian using finite differences for parameter SE_elr due to hitting the lower bound - no standard
errors available.
] 
[> In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('mom.standard_errors', 'D:\Dynare\5.2\matlab\+mom\standard_errors.m', 96)" style="font-weight:bold">mom.standard_errors</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\+mom\standard_errors.m',96,0)">line 96</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('mom.run', 'D:\Dynare\5.2\matlab\+mom\run.m', 940)" style="font-weight:bold">mom.run</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\+mom\run.m',940,0)">line 940</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('ClimateDSGE_EZ_LRR_SMM5.driver', 'E:\Dropbox Folder\Dropbox\Climate Policy Uncertainty and DSGE\Model Quantitative\Climate DSGE Code 2\+ClimateDSGE_EZ_LRR_SMM5\driver.m', 888)" style="font-weight:bold">ClimateDSGE_EZ_LRR_SMM5.driver</a> (<a href="matlab: opentoline('E:\Dropbox Folder\Dropbox\Climate Policy Uncertainty and DSGE\Model Quantitative\Climate DSGE Code 2\+ClimateDSGE_EZ_LRR_SMM5\driver.m',888,0)">line 888</a>)
In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('dynare', 'D:\Dynare\5.2\matlab\dynare.m', 281)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('D:\Dynare\5.2\matlab\dynare.m',281,0)">line 281</a>)] 

RESULTS FROM SMM (STAGE 1) ESTIMATION
parameters
        Estimate    s.d. t-stat

rhow      0.9584     NaN     NaN 
rhocm     0.6148     NaN     NaN 
nu1       0.0039     NaN     NaN 

standard deviation of shocks
        Estimate    s.d. t-stat

ew        0.9991     NaN     NaN 
em1       1.9875     NaN     NaN 
elr       0.0001     NaN     NaN 



Comparison of data moments and model moments (SMM)
Moment           Data       Model
E[dc1]      0.0017504   0.0011452
E[dy1]      0.0015760   0.0011844
E[dca1]     0.0000771   0.0002511
E[hml1]    -0.0030000  -0.0030000
E[div]      0.0016044   0.0016913
E[rm]       0.0841659   0.0077859

==== Method of Moments Estimation (SMM) Completed ====

Total computing time : 0h00m14s