Multiple Linear Regression - Estimated Regression Equation
BEL_20[t] = -4162.90150693943 + 0.0638179802619133Nikkei[t] + 0.402353700913268DJ_Indust[t] + 0.0963455379503522Goudprijs[t] -14.9608458786253Conjunct_Seizoenzuiver[t] -4.07998337596778Cons_vertrouw[t] + 253.964447027001Alg_consumptie_index_BE[t] + 183.299513518561Gem_rente_kasbon_1j[t] + 24.9144552917607M1[t] + 12.0310172490279M2[t] -31.6485543535592M3[t] -80.3430192654199M4[t] -47.2277227733143M5[t] -4.40120853957415M6[t] + 22.0742445366294M7[t] + 66.1102220435124M8[t] -38.1169925279443M9[t] -97.3200633225077M10[t] -33.8625976909345M11[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)-4162.90150693943781.614662-5.3268e-064e-06
Nikkei0.06381798026191330.0663880.96130.3436180.171809
DJ_Indust0.4023537009132680.0875474.59596.4e-053.2e-05
Goudprijs0.09634553795035220.075181.28150.2092150.104607
Conjunct_Seizoenzuiver-14.96084587862539.679435-1.54560.1320270.066013
Cons_vertrouw-4.0799833759677810.704407-0.38110.7056110.352805
Alg_consumptie_index_BE253.96444702700160.7386454.18130.000210.000105
Gem_rente_kasbon_1j183.299513518561141.8737311.2920.2056140.102807
M124.9144552917607131.5662760.18940.8510.4255
M212.0310172490279127.4843190.09440.9254020.462701
M3-31.6485543535592131.943702-0.23990.8119660.405983
M4-80.3430192654199134.506658-0.59730.55450.27725
M5-47.2277227733143139.800103-0.33780.7377030.368851
M6-4.40120853957415135.633715-0.03240.9743150.487158
M722.0742445366294136.7246040.16150.8727540.436377
M866.1102220435124144.0433740.4590.6493650.324682
M9-38.1169925279443137.20537-0.27780.7829450.391473
M10-97.3200633225077139.431396-0.6980.4902320.245116
M11-33.8625976909345131.573216-0.25740.7985430.399272


Multiple Linear Regression - Regression Statistics
Multiple R0.984771263508163
R-squared0.969774441431463
Adjusted R-squared0.95277256473666
F-TEST (value)57.0392585971375
F-TEST (DF numerator)18
F-TEST (DF denominator)32
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation182.219833680821
Sum Squared Residuals1062530.16917331


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
11635.251702.40964936445-67.1596493644514
21833.421687.64855151646145.771448483541
31910.431586.36491561770324.065084382304
41959.671927.5505215492832.1194784507189
51969.62003.46099885971-33.8609988597131
62061.412211.58652924925-150.176529249245
72093.482343.10987712638-249.629877126381
82120.882421.29154667270-300.411546672704
92174.562345.92196602643-171.361966026433
102196.722371.07398158792-174.353981587924
112350.442601.41657562551-250.976575625514
122440.252489.95746899894-49.7074689989381
132408.642409.17600105041-0.536001050413833
142472.812657.24720643326-184.437206433259
152407.62557.32055684587-149.720556845872
162454.622575.85661019768-121.236610197682
172448.052533.46707675863-85.41707675863
182497.842476.6772424650421.1627575349631
192645.642541.20144422476104.438555775242
202756.762765.18444901326-8.42444901326227
212849.272788.8506629856360.4193370143661
222921.442763.5072366613157.932763338700
232981.852750.17518348297231.674816517032
243080.582969.58095841580110.999041584195
253106.223137.2079819904-30.9879819903986
263119.312920.71667528326198.593324716736
273061.262857.67357373134203.586426268661
283097.313017.4614463888279.8485536111783
293161.693142.0176000409319.672399959069
303257.163212.9650333327144.1949666672939
313277.013318.87335223401-41.8633522340062
323295.323140.80721653638154.512783463615
333363.993345.1065980941818.8834019058189
343494.173583.08036003983-88.9103600398321
353667.033674.5408706455-7.51087064549996
363813.063718.3467990922194.713200907786
373917.963684.52615498451233.433845015485
383895.513967.59024821335-72.0802482133481
393801.064119.84169116165-318.781691161654
403570.123560.851421864219.2685781357851
413701.613602.0043243407399.6056756592742
423862.273777.4511949530184.8188050469886
433970.13783.04532641486187.054673585145
444138.523984.19678777765154.323212222350
454199.754107.6907728937592.059227106248
464290.894185.55842171094105.331578289056
474443.914417.0973702460226.8126297539823
484502.644658.64477349304-156.004773493043
494356.984491.73021261022-134.750212610222
504591.274679.11731855367-87.8473185536699
514696.964756.10926264344-59.1492626434395


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.9888828685314540.02223426293709180.0111171314685459
230.9996355996498510.0007288007002971820.000364400350148591
240.9998066694118480.0003866611763041630.000193330588152082
250.9999895705300182.08589399635177e-051.04294699817588e-05
260.9999959610375248.07792495147865e-064.03896247573932e-06
270.9999964265234187.14695316385924e-063.57347658192962e-06
280.999954500479859.09990402996152e-054.54995201498076e-05
290.9999606191626887.8761674623162e-053.9380837311581e-05


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.875NOK
5% type I error level81NOK
10% type I error level81NOK