Multiple Linear Regression - Estimated Regression Equation
BEL_20[t] = + 688.098795765917 + 0.0462179954610761Nikkei[t] + 0.278030419928214DJ_Indust[t] -0.178777803993867Goudprijs[t] -57.5548412693366Conjunct_Seizoenzuiver[t] + 33.6760247240671Cons_vertrouw[t] -131.109005569773Alg_consumptie_index_BE[t] + 131.838648549116Gem_rente_kasbon_1j[t] -34.4247599390837M1[t] + 56.988069043629M2[t] -72.544498687564M3[t] -24.1245880172990M4[t] -68.2147043679782M5[t] -14.5616582959767M6[t] -131.285788333769M7[t] -116.524230423104M8[t] + 65.1267127103127M9[t] + 96.69510720441M10[t] + 87.6952746740044M11[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)688.098795765917899.1433520.76530.4497110.224855
Nikkei0.04621799546107610.027091.70610.0976820.048841
DJ_Indust0.2780304199282140.062054.48088.9e-054.5e-05
Goudprijs-0.1787778039938670.051491-3.4720.0015020.000751
Conjunct_Seizoenzuiver-57.554841269336612.588776-4.57196.9e-053.4e-05
Cons_vertrouw33.67602472406718.6863693.87690.0004940.000247
Alg_consumptie_index_BE-131.10900556977375.27088-1.74180.0911450.045572
Gem_rente_kasbon_1j131.838648549116130.1853681.01270.31880.1594
M1-34.4247599390837119.646909-0.28770.7754170.387708
M256.988069043629118.5865930.48060.6340980.317049
M3-72.544498687564120.302055-0.6030.5507460.275373
M4-24.1245880172990126.555262-0.19060.8500240.425012
M5-68.2147043679782127.073369-0.53680.5951090.297555
M6-14.5616582959767124.993304-0.11650.9079850.453992
M7-131.285788333769126.340208-1.03910.3065260.153263
M8-116.524230423104124.034076-0.93950.3545360.177268
M965.1267127103127124.0061790.52520.6030720.301536
M1096.69510720441129.1429180.74870.4594790.22974
M1187.6952746740044122.4603060.71610.4791160.239558


Multiple Linear Regression - Regression Statistics
Multiple R0.94922276490454
R-squared0.901023857413022
Adjusted R-squared0.845349777207846
F-TEST (value)16.1839019898036
F-TEST (DF numerator)18
F-TEST (DF denominator)32
p-value2.85611534422969e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation169.602470518423
Sum Squared Residuals920479.936190486


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
13484.743224.64088077143260.099119228574
23411.133209.95278468158201.177215318422
33288.183212.8860912095675.2939087904361
43280.373134.99638214319145.373617856807
53173.953193.38303085782-19.4330308578158
63165.263009.90491731214155.355082687857
73092.713301.27426870604-208.564268706039
83053.053214.76467480129-161.714674801295
93181.963272.38150022858-90.4215002285755
102999.932922.0846279528877.8453720471175
113249.573148.76806201861100.801937981386
123210.523040.79870095976169.721299040245
133030.293213.49249969773-183.202499697735
142803.473002.84415934495-199.374159344950
152767.632851.11637451454-83.4863745145386
162882.63039.84780185671-157.247801856710
172863.362886.97695304247-23.6169530424729
182897.062878.3400345359318.7199654640731
193012.612847.7530188292164.856981170799
203142.953051.6034157817591.3465842182463
213032.932854.62949975961178.300500240390
223045.782960.2463970426085.5336029574046
233110.523027.6165701499182.903429850085
243013.243041.02513331141-27.7851333114125
252987.13102.68362683346-115.583626833456
262995.553106.40613188762-110.856131887622
272833.182807.5166480640125.6633519359916
282848.963012.44410974976-163.484109749757
292794.832966.54983242192-171.719832421920
302845.263073.86057512869-228.600575128694
312915.022965.5539739007-50.5339739006996
322892.632834.7690818421057.8609181579048
332604.422603.851266363380.568733636618269
342641.652528.84685263005112.80314736995
352659.812558.59725077912101.212749220876
362638.532756.04708943989-117.517089439890
372720.252648.1031385232772.1468614767347
382745.882729.8726447462316.0073552537680
392735.72709.9278565209025.7721434790966
402811.72636.34170625034175.35829374966
412799.432584.66018367779214.769816322209
422555.282500.7544730232454.5255269767635
432304.982210.7387385640694.2412614359392
442214.952202.4428275748612.5071724251435
452065.812154.25773364843-88.4477336484332
461940.492216.67212237447-276.182122374472
4720422326.91811705235-284.918117052346
481995.372019.78907628894-24.4190762889422
491946.811980.26985417412-33.4598541741173
501765.91672.8542793396293.0457206603824
511635.251678.49302969099-43.2430296909856


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.1886795589311620.3773591178623230.811320441068838
230.3476281230173320.6952562460346640.652371876982668
240.4532082079337190.9064164158674370.546791792066281
250.3185107498930350.6370214997860710.681489250106965
260.4299945729703010.8599891459406020.570005427029699
270.2827889799483180.5655779598966370.717211020051681
280.3742126344319920.7484252688639840.625787365568008
290.2669282334834210.5338564669668430.733071766516578


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