Multiple Linear Regression - Estimated Regression Equation
BEL_20[t] = + 305.04884420521 -0.0879426162502108Nikkei[t] + 0.273261952991123DJ_Indust[t] + 0.166092429940536Goudprijs[t] -12.1581355295989Conjunct_Seizoenzuiver[t] + 4.00557381579969Cons_vertrouw[t] -120.445196382601Alg_consumptie_index_BE[t] + 176.262121934634Gem_rente_kasbon_1j[t] + 23.0984480449475M1[t] -0.344166273616615M2[t] -3.60816622271175M3[t] + 58.2622039406631M4[t] -80.4908785385934M5[t] -53.38569645481M6[t] + 22.6521134077669M7[t] + 18.2095822005571M8[t] + 39.156207113846M9[t] -25.2026804165444M10[t] + 39.8361573537659M11[t] -40.488723353944t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)305.04884420521498.9106740.61140.5453730.272687
Nikkei-0.08794261625021080.021636-4.06460.0003050.000153
DJ_Indust0.2732619529911230.0342977.967600
Goudprijs0.1660924299405360.0492143.37490.0020.001
Conjunct_Seizoenzuiver-12.15813552959898.737176-1.39150.1739670.086984
Cons_vertrouw4.005573815799695.9142480.67730.5032550.251627
Alg_consumptie_index_BE-120.44519638260141.617129-2.89410.0069030.003452
Gem_rente_kasbon_1j176.26212193463472.1328742.44360.0204330.010216
M123.098448044947566.4614070.34750.7305290.365265
M2-0.34416627361661565.876174-0.00520.9958650.497932
M3-3.6081662227117566.967874-0.05390.9573770.478689
M458.262203940663170.5957090.82530.4155110.207755
M5-80.490878538593470.2419-1.14590.2606020.130301
M6-53.3856964548169.225537-0.77120.4464360.223218
M722.652113407766972.0857310.31420.7554450.377722
M818.209582200557170.319670.2590.7973820.398691
M939.15620711384668.5989380.57080.5722510.286125
M10-25.202680416544472.76848-0.34630.7314270.365713
M1139.836157353765967.9069330.58660.5617010.280851
t-40.4887233539444.713977-8.589100


Multiple Linear Regression - Regression Statistics
Multiple R0.985248679887391
R-squared0.970714961219847
Adjusted R-squared0.952766066483625
F-TEST (value)54.0821580094763
F-TEST (DF numerator)19
F-TEST (DF denominator)31
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation93.7311522966766
Sum Squared Residuals272351.396236746


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
13484.743411.0853048353973.6546951646108
23411.133350.1865529582960.9434470417099
33288.183328.13620403934-39.9562040393389
43280.373313.65826589738-33.2882658973784
53173.953276.21163982841-102.261639828406
63165.263101.8871885731863.372811426821
73092.713197.98333796861-105.273337968608
83053.053102.66451989766-49.6145198976608
93181.963052.95095077135129.009049228647
102999.933090.62607476728-90.6960747672763
113249.573101.83676566391147.733234336092
123210.523088.89717595945121.622824040547
133030.293078.02623258245-47.7362325824522
142803.472887.8359606803-84.3659606803034
152767.632727.5332280585340.0967719414666
162882.62904.14803695639-21.5480369563949
172863.362985.15762528836-121.79762528836
182897.062904.93779648363-7.8777964836316
193012.612960.9793290789551.6306709210538
203142.953142.165539770540.784460229456097
213032.933088.267771848-55.3377718479959
223045.783004.9953603184540.7846396815455
233110.523108.85038956021.66961043979904
243013.243053.14084719075-39.9008471907476
252987.12992.29622324266-5.19622324265982
262995.552991.401671685374.14832831463412
272833.182838.5395809222-5.35958092219993
282848.962822.4342650841726.525734915829
292794.832770.9665837652123.8634162347896
302845.262946.40903365762-101.149033657624
312915.022976.75646767886-61.7364676788609
322892.632875.2172351970117.4127648029865
332604.422666.27251486875-61.8525148687522
342641.652543.3297453991898.3202546008187
352659.812687.91873151619-28.108731516187
362638.532673.66675380822-35.1367538082208
372720.252617.68200215537102.567997844628
382745.882765.8691425113-19.9891425113007
392735.72724.780910389510.9190896105037
402811.72783.3894320620628.3105679379443
412799.432599.23415111802200.195848881977
422555.282509.6259812855745.6540187144348
432304.982189.60086527358115.379134726415
442214.952183.5327051347831.4172948652181
452065.812077.6287625119-11.818762511899
461940.491988.89881951509-48.4088195150879
4720422163.2941132597-121.294113259704
481995.372041.95522304158-46.5852230415791
491946.812070.10023718413-123.290237184127
501765.91726.6366721647439.2633278352601
511635.251640.95007659043-5.70007659043152


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
230.7548723230453580.4902553539092830.245127676954642
240.5906977225452580.8186045549094850.409302277454742
250.4744954636489020.9489909272978030.525504536351099
260.3140796191353050.6281592382706090.685920380864696
270.2597794499361760.5195588998723520.740220550063824
280.131683858124940.2633677162498790.86831614187506


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK