Multiple Linear Regression - Estimated Regression Equation
Werkloosheid_BRUSSELS_HOOFDSTEDELIJK_GEWEST[t] = + 55099.3198568333 + 1.39221268056014Werkloosheid_ANTWERPEN[t] + 1.56839704665183`Werkloosheid_VLAAMS-BRABANT`[t] -0.604469418701903`Werkloosheid_WAALS-BRABANT`[t] -0.110600027769685`Werkloosheid_WEST-VLAANDEREN`[t] -1.48452419259375`Werkloosheid_OOST-VLAANDEREN`[t] -0.311664423472494Werkloosheid_HENEGOUWEN[t] -0.799533063933319Werkloosheid_LUIK[t] -0.563834774426426Werkloosheid_LIMBURG[t] + 0.548357386305045Werkloosheid_LUXEMBURG[t] + 3.26102090831611Werkloosheid_NAMEN[t] -698.991165962642M1[t] -364.007190931476M2[t] + 286.38116729001M3[t] + 772.013289599035M4[t] + 983.526566830001M5[t] + 1106.30871097402M6[t] -710.371784887113M7[t] -3019.54286585955M8[t] -108.535939172714M9[t] + 377.126472073552M10[t] -145.786478238283M11[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)55099.319856833310210.5458275.39631e-061e-06
Werkloosheid_ANTWERPEN1.392212680560140.3120484.46153.6e-051.8e-05
`Werkloosheid_VLAAMS-BRABANT`1.568397046651830.8316631.88590.0641580.032079
`Werkloosheid_WAALS-BRABANT`-0.6044694187019030.810589-0.74570.458750.229375
`Werkloosheid_WEST-VLAANDEREN`-0.1106000277696850.609999-0.18130.8567340.428367
`Werkloosheid_OOST-VLAANDEREN`-1.484524192593750.548857-2.70480.0088830.004441
Werkloosheid_HENEGOUWEN-0.3116644234724940.244649-1.27390.2076030.103801
Werkloosheid_LUIK-0.7995330639333190.208938-3.82670.0003120.000156
Werkloosheid_LIMBURG-0.5638347744264260.51597-1.09280.2788650.139432
Werkloosheid_LUXEMBURG0.5483573863050450.8985490.61030.5439870.271994
Werkloosheid_NAMEN3.261020908316110.6681614.88068e-064e-06
M1-698.991165962642748.865436-0.93340.3543550.177177
M2-364.007190931476703.319382-0.51760.6066710.303336
M3286.38116729001787.1775420.36380.7172810.35864
M4772.0132895990351032.8826610.74740.457720.22886
M5983.5265668300011258.461720.78150.4375630.218782
M61106.308710974021152.7843890.95970.3410660.170533
M7-710.3717848871131293.277904-0.54930.5848520.292426
M8-3019.542865859551516.482589-1.99110.0510240.025512
M9-108.5359391727141168.409006-0.09290.9262990.463149
M10377.126472073552844.0852430.44680.6566370.328319
M11-145.786478238283735.520082-0.19820.8435520.421776


Multiple Linear Regression - Regression Statistics
Multiple R0.987312088364096
R-squared0.974785159829872
Adjusted R-squared0.965959965770327
F-TEST (value)110.454813033329
F-TEST (DF numerator)21
F-TEST (DF denominator)60
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1166.19345328749
Sum Squared Residuals81600430.2294359


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
19768797566.6029367588120.397063241208
29851297435.56924593031076.43075406967
39867398354.8998198192318.100180180751
49602897366.3547611624-1338.35476116235
59801496625.76563663381388.23436336621
69558094536.33773887091043.66226112909
79783897217.6202130466620.379786953448
89776097277.048607919482.951392081015
999913100358.527048843-445.527048843429
109758896646.1168653381941.883134661933
119394295609.7792955674-1667.77929556736
129365694468.5462825843-812.546282584303
139336593786.3174151296-421.317415129643
149288193475.007489501-594.007489500985
159312093458.4944223476-338.494422347586
169106391647.5044406959-584.50444069591
179093090160.1033982523769.89660174772
189194692742.7116373835-796.711637383509
199462496054.6361712005-1430.63617120054
209548495587.250661342-103.250661341982
219586296209.9253437352-347.925343735188
229553095325.1820882014204.817911798651
239457494487.590055489886.4099445101977
249467793732.0031658299944.996834170071
259384593223.1432319244621.856768075558
269153392775.411581774-1242.41158177404
279121491411.5579913244-197.557991324375
289092291567.5607632546-645.560763254625
298956389631.5929356913-68.5929356912898
308994590074.7734360082-129.77343600815
319185092066.1285904346-216.12859043465
329250592943.2206017462-438.220601746175
339243792906.5361568489-469.53615684893
349387693278.8142593115597.185740688534
359356193105.4685789363455.531421063694
369411993106.42849590931012.57150409073
379526495104.1023320078159.897667992232
389608995677.2042625037411.795737496309
399716096998.2481902049161.751809795088
409864496356.21899783792287.78100216214
419626696911.5827938564-645.582793856353
429793898406.3278417535-468.32784175355
4399757101657.510948195-1900.51094819494
44101550101621.039611969-71.0396119685326
45102449103435.561775264-986.561775263887
46102416102556.449268266-140.449268265797
47102491103004.901738172-513.901738171574
48102495104823.173716918-2328.17371691845
49104552104478.26090551473.7390944856174
50104798104574.311745512223.688254487921
51104947104444.004707795502.995292204881
52103950103850.31305673499.6869432655323
53102858103111.462340955-253.462340955265
54106952105799.9860124741152.0139875256
55110901108349.5440631132551.45593688705
56107706109258.713469389-1552.71346938894
57111267109604.8437487641662.1562512363
58107643109113.119500397-1470.11950039665
59105387105856.865730294-469.865730293832
60105718105019.31211595698.687884049846
61106039106744.446372533-705.446372533141
62106203106812.770118233-609.77011823264
63105558106543.692465168-985.692465168096
64105230105136.59680523893.4031947623158
65104864104335.632991932528.367008068196
66104374104114.432692069259.567307930744
67107450105701.9260392411748.07396075863
68108173106054.1644351692118.83556483072
69108629107665.018172194963.981827805991
70107847106268.6785077361578.32149226443
71107394105284.3946015412109.60539845887
72106278105793.536222808484.463777192104
73107733107582.126806132150.873193868168
74107573106838.725556546734.274443453763
75107500106961.102403341538.897596659338
76106382106294.45117507787.5488249229008
77104412106130.859902679-1718.85990267922
78105871106931.43064144-1060.43064144022
79108767110139.633974769-1372.633974769
80109728110164.562612466-436.562612466103
81109769110145.587754351-376.587754350859
82109609111320.639510751-1711.63951075111


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
250.1995852980243740.3991705960487490.800414701975626
260.09609046984721850.1921809396944370.903909530152782
270.04891677300504840.09783354601009670.951083226994952
280.08417391106524680.1683478221304940.915826088934753
290.1159327177489410.2318654354978810.884067282251059
300.08871216814177780.1774243362835560.911287831858222
310.09395466647233810.1879093329446760.906045333527662
320.06171987331698660.1234397466339730.938280126683013
330.04962371834806630.09924743669613270.950376281651934
340.028328662574470.05665732514893990.97167133742553
350.05337680374502210.1067536074900440.946623196254978
360.03083062540888960.06166125081777930.96916937459111
370.01915100495999920.03830200991999840.980848995040001
380.01032966115181580.02065932230363150.989670338848184
390.005682829785235350.01136565957047070.994317170214765
400.04574621299221880.09149242598443760.954253787007781
410.06098775887913510.121975517758270.939012241120865
420.04178541130504380.08357082261008770.958214588694956
430.1340249072558430.2680498145116860.865975092744157
440.2445409130896970.4890818261793930.755459086910304
450.2865520486050730.5731040972101460.713447951394927
460.237393205132980.474786410265960.76260679486702
470.2427459337745330.4854918675490660.757254066225467
480.2990728828683590.5981457657367180.700927117131641
490.3144089229653480.6288178459306950.685591077034652
500.258684432167840.5173688643356790.74131556783216
510.2268161315650160.4536322631300320.773183868434984
520.3209465270695510.6418930541391010.679053472930449
530.7300392345933560.5399215308132880.269960765406644
540.6987115332235110.6025769335529790.301288466776489
550.7661105991493160.4677788017013670.233889400850684
560.7721257868243210.4557484263513570.227874213175679
570.7998358555082460.4003282889835080.200164144491754


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0909090909090909NOK
10% type I error level90.272727272727273NOK