Multiple Linear Regression - Estimated Regression Equation
Sterftes[t] = + 9484.31288744492 + 8.56703263873167Dummy1[t] -4.01529240421240D1_D2[t] + 984.790891776835M1[t] -452.326462021062M2[t] -59.6938158189554M3[t] -795.561169616849M4[t] -998.553523414743M5[t] -1263.04587721264M6[t] -1088.91323101053M7[t] -1326.78058480842M8[t] -1578.52293860632M9[t] -1006.76529240421M10[t] -968.632646202106M11[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)9484.31288744492146.49264564.742600
Dummy18.56703263873167315.1232380.02720.9783770.489189
D1_D2-4.015292404212404.200461-0.95590.3419240.170962
M1984.790891776835199.7649334.92974e-062e-06
M2-452.326462021062199.53295-2.26690.0260270.013014
M3-59.6938158189554199.322828-0.29950.7653290.382665
M4-795.561169616849199.134636-3.99510.000147e-05
M5-998.553523414743198.968437-5.01873e-061e-06
M6-1263.04587721264198.824285-6.352600
M7-1088.91323101053198.702229-5.480100
M8-1326.78058480842198.602308-6.680600
M9-1578.52293860632198.524558-7.951300
M10-1006.76529240421198.469003-5.07272e-061e-06
M11-968.632646202106198.435663-4.88135e-063e-06


Multiple Linear Regression - Regression Statistics
Multiple R0.887804582769805
R-squared0.788196977187067
Adjusted R-squared0.754618449180139
F-TEST (value)23.4732438844382
F-TEST (DF numerator)13
F-TEST (DF denominator)82
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation396.849096941053
Sum Squared Residuals12914114.8709202


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
11200810469.10377922171538.89622077827
291699031.98642542385137.013574576145
387889424.61907162596-636.619071625962
484178688.75171782807-271.751717828069
582478485.75936403017-238.759364030174
681978221.26701023228-24.2670102322804
782368395.39965643439-159.399656434387
882538157.532302636595.4676973635074
977337905.7899488386-172.789948838599
1083668477.5475950407-111.547595040705
1186268515.68024124281110.319758757189
1288639484.31288744492-621.312887444917
131010210469.1037792218-367.103779221751
1484639031.98642542386-568.986425423856
1591149424.61907162596-310.619071625962
1685638688.75171782807-125.751717828068
1788728485.75936403017386.240635969826
1883018221.2670102322879.7329897677194
1983018395.39965643439-94.3996564343863
2082788157.5323026365120.467697363507
2177367905.7899488386-169.789948838599
2279738477.5475950407-504.547595040705
2382688515.68024124281-247.680241242812
2494769484.31288744492-8.3128874449172
251110010469.1037792218630.896220778248
2689629031.98642542386-69.9864254238556
2791739424.61907162596-251.619071625962
2887388688.7517178280749.2482821719321
2984598485.75936403017-26.7593640301742
3080788221.26701023228-143.267010232280
3184118395.3996564343915.6003435656137
3282918157.5323026365133.467697363507
3378107905.7899488386-95.7899488385991
3486168477.5475950407138.452404959295
3583128515.68024124281-203.680241242812
3696929484.31288744492207.687112555083
37991110469.1037792218-558.103779221752
3889159031.98642542386-116.986425423856
3994529424.6190716259627.3809283740382
4091128688.75171782807423.248282171932
4184728485.75936403017-13.7593640301742
4282308221.267010232288.73298976771944
4383848395.39965643439-11.3996564343863
4486258157.5323026365467.467697363507
4582217905.7899488386315.210051161401
4686498477.5475950407171.452404959295
4786258515.68024124281109.319758757189
48104439484.31288744492958.687112555083
491035710280.921484054176.0785159459237
5085868839.78883785197-253.788837851968
5188929228.40619164986-336.406191649861
5283298488.52354544776-159.523545447755
5381018281.51589924565-180.515899245649
5479228013.00825304354-91.0082530435424
5581208183.12560684144-63.1256068414367
5678387941.24296063933-103.24296063933
5777357685.4853144372249.5146855627757
5884068253.22766823512152.772331764882
5982098287.34502203301-78.3450220330117
6094519251.9623758309199.037624169095
611004110232.7379752035-191.737975203528
6294118791.60532900142619.394670998581
63104059180.222682799311224.77731720069
6484678440.340036597226.6599634027937
6584648233.3323903951230.6676096049
6681027964.824744193137.175255807006
6776278134.94209799089-507.942097990888
6875137893.05945178878-380.059451788782
6975107637.30180558668-127.301805586675
7082918205.0441593845785.9558406154309
7180648239.16151318246-175.161513182463
7293839203.77886698036179.221133019644
73970610184.5544663530-478.554466352979
7485798743.42182015087-164.42182015087
7594749132.03917394876341.960826051236
7683188392.15652774666-74.1565277466577
7782138185.1488815445527.8511184554486
7880597916.64123534244142.358764657555
7991118086.758589140341024.24141085966
8077087844.87594293823-136.875942938233
8176807589.1182967361390.8817032638736
8280148156.86065053402-142.860650534020
8380078190.97800433191-183.978004331914
8487189155.5953581298-437.595358129808
85948610136.3709575024-650.37095750243
8691138695.23831130032417.761688699679
8790259083.85566509822-58.8556650982152
8884768343.97301889611132.026981103891
8979528136.965372694-184.965372694003
9077597868.4577264919-109.457726491897
9178358038.57508028979-203.575080289790
9276007796.69243408768-196.692434087684
9376517540.93478788558110.065212114422
9483198108.67714168347210.322858316528
9588128142.79449548137669.205504518635
9686309107.41184927926-477.411849279259


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.9989938309159930.002012338168014820.00100616908400741
180.997193355488070.005613289023858560.00280664451192928
190.993406561588320.01318687682336080.00659343841168039
200.9861686596021860.02766268079562880.0138313403978144
210.9744623916164070.0510752167671870.0255376083835935
220.969316858192920.06136628361415940.0306831418070797
230.9567699387477650.08646012250447030.0432300612522352
240.9515150313499980.0969699373000030.0484849686500015
250.9532728167849320.0934543664301360.046727183215068
260.9311638643148370.1376722713703270.0688361356851633
270.9181273774329810.1637452451340370.0818726225670185
280.889468838985960.2210623220280790.110531161014039
290.8475959701073570.3048080597852870.152404029892643
300.8052824184400490.3894351631199030.194717581559951
310.7513448052840870.4973103894318250.248655194715913
320.6869266629157670.6261466741684660.313073337084233
330.6262578592196510.7474842815606970.373742140780349
340.6004786962232160.7990426075535680.399521303776784
350.5506651927995820.8986696144008350.449334807200418
360.5408253666819820.9183492666360360.459174633318018
370.7732440213625840.4535119572748320.226755978637416
380.7430162899951570.5139674200096860.256983710004843
390.74806459984650.5038708003070010.251935400153501
400.7399272231764720.5201455536470570.260072776823529
410.6900623543040010.6198752913919980.309937645695999
420.6427998610124670.7144002779750650.357200138987533
430.6112587463357970.7774825073284070.388741253664203
440.5771480642138830.8457038715722330.422851935786117
450.5493738114784160.9012523770431670.450626188521584
460.5257984623999510.9484030752000980.474201537600049
470.563247584557620.873504830884760.43675241544238
480.6843135574437080.6313728851125840.315686442556292
490.6681935516811140.6636128966377730.331806448318886
500.6526556855365890.6946886289268220.347344314463411
510.7255065486229650.5489869027540710.274493451377035
520.6783697662867580.6432604674264830.321630233713242
530.6299790391124960.7400419217750090.370020960887504
540.5744318366163950.851136326767210.425568163383605
550.5218057853771660.9563884292456680.478194214622834
560.4576532174968420.9153064349936850.542346782503158
570.3943912289234740.7887824578469470.605608771076526
580.3341585442151900.6683170884303790.66584145578481
590.2917750935243550.5835501870487110.708224906475645
600.2490500528178260.4981001056356520.750949947182174
610.2332897501436180.4665795002872360.766710249856382
620.2651056547856720.5302113095713450.734894345214328
630.608798714095160.782402571809680.39120128590484
640.5558450030319340.8883099939361330.444154996968066
650.5161256238461880.9677487523076240.483874376153812
660.4537891523621770.9075783047243540.546210847637823
670.6477066736468470.7045866527063060.352293326353153
680.628645578018360.742708843963280.37135442198164
690.5707676261407580.8584647477184840.429232373859242
700.4801637139269660.9603274278539310.519836286073034
710.4803793896621310.9607587793242620.519620610337869
720.4794449559454550.958889911890910.520555044054545
730.4231178678105260.8462357356210530.576882132189474
740.4247457365582140.8494914731164290.575254263441786
750.3578954050029340.7157908100058690.642104594997066
760.2707743305689460.5415486611378920.729225669431054
770.1816501945671910.3633003891343810.81834980543281
780.1126188410788030.2252376821576060.887381158921197
790.6074963162719490.7850073674561030.392503683728051


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0317460317460317NOK
5% type I error level40.0634920634920635NOK
10% type I error level90.142857142857143NOK