Multiple Linear Regression - Estimated Regression Equation

wisselkoers[t] = + 8.46828148995742e-15 -2.46149433910034e-17consumptieprijzen[t] -4.29728240716339e-17`Yt-1`[t] + 4.15699978358523e-17`Yt-2`[t] -2.25683481723919e-16`Yt-3`[t] + 1`Yt-4`[t] -8.47467574596834e-17M1[t] -8.20573878030883e-17M2[t] -7.1915385565832e-17M3[t] -2.74872398299474e-16M4[t] -4.20111500162079e-16M5[t] -1.30278565279883e-15M6[t] -2.20954492161706e-16M7[t] -1.98972849676541e-16M8[t] -1.22427412989515e-16M9[t] -1.63618939147031e-16M10[t] -1.05726305286780e-16M11[t] -1.29912862524235e-17t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	8.46828148995742e-15	0	0.6909	0.493727	0.246863
consumptieprijzen	-2.46149433910034e-17	0	-0.2095	0.835144	0.417572
`Yt-1`	-4.29728240716339e-17	0	-0.8997	0.373795	0.186898
`Yt-2`	4.15699978358523e-17	0	0.5979	0.553387	0.276693
`Yt-3`	-2.25683481723919e-16	0	-3.2892	0.002135	0.001068
`Yt-4`	1	0	22246664107269784	0	0
M1	-8.47467574596834e-17	0	-0.201	0.841749	0.420875
M2	-8.20573878030883e-17	0	-0.1911	0.849451	0.424726
M3	-7.1915385565832e-17	0	-0.1749	0.862074	0.431037
M4	-2.74872398299474e-16	0	-0.6744	0.504034	0.252017
M5	-4.20111500162079e-16	0	-0.9773	0.334444	0.167222
M6	-1.30278565279883e-15	0	-3.0967	0.003617	0.001808
M7	-2.20954492161706e-16	0	-0.5336	0.596648	0.298324
M8	-1.98972849676541e-16	0	-0.491	0.626158	0.313079
M9	-1.22427412989515e-16	0	-0.2982	0.767153	0.383577
M10	-1.63618939147031e-16	0	-0.3786	0.707054	0.353527
M11	-1.05726305286780e-16	0	-0.2474	0.805877	0.402939
t	-1.29912862524235e-17	0	-0.5281	0.600398	0.300199

Multiple Linear Regression - Regression Statistics
Multiple R	1
R-squared	1
Adjusted R-squared	1
F-TEST (value)	4.63354400231108e+32
F-TEST (DF numerator)	17
F-TEST (DF denominator)	39
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.90015688374994e-16
Sum Squared Residuals	1.35766219886161e-29

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	100	100	4.07763816357643e-16
2	97.82	97.82	3.5574583317214e-16
3	94.05	94.05	1.63465453130326e-16
4	91.12	91.12	-4.18880846466341e-16
5	93.13	93.13	-9.62488669471354e-17
6	93.88	93.88	-2.81669334016397e-15
7	92.55	92.55	4.68571786694248e-16
8	94.43	94.43	7.33814311449185e-16
9	96.25	96.25	1.29124121501585e-17
10	100.44	100.44	-1.01829584700990e-16
11	101.5	101.5	3.63047501482589e-17
12	99.4	99.4	6.7355343269277e-17
13	99.69	99.69	6.9409120967337e-18
14	101.69	101.69	-1.35191297428201e-16
15	103.67	103.67	-1.14553867389374e-16
16	103.05	103.05	4.11428458161613e-16
17	100.95	100.95	1.69673394116565e-16
18	102.35	102.35	8.42383538803831e-16
19	101.65	101.65	-1.20860021245171e-16
20	99.57	99.57	-3.66619628508846e-16
21	95.68	95.68	-2.68185866743424e-17
22	96.58	96.58	2.05121508807187e-17
23	96.33	96.33	1.09289807473371e-18
24	95.37	95.37	-1.78863587485951e-16
25	96	96	-1.65730977624316e-16
26	96.88	96.88	-2.55204407373543e-17
27	94.85	94.85	5.56941512185107e-17
28	92.47	92.47	1.57391526335182e-16
29	93.99	93.99	1.39688041369639e-16
30	93.45	93.45	1.11252863561220e-15
31	92.27	92.27	1.52828716138662e-16
32	90.4	90.4	2.12554073285373e-17
33	90.43	90.43	3.52126995878016e-17
34	91.05	91.05	-9.37796514406058e-17
35	89.08	89.08	-1.98513154521503e-17
36	89.69	89.69	-3.48655124153472e-16
37	87.92	87.92	-6.07306509384967e-17
38	85.88	85.88	1.25994483880687e-17
39	83.21	83.21	2.78351701157183e-16
40	83.86	83.86	-1.23854471677458e-16
41	83.01	83.01	-1.75557563617057e-16
42	82.85	82.85	8.42212237815844e-16
43	78.69	78.69	1.01916953661347e-16
44	77.57	77.57	-2.07661662884899e-16
45	78.54	78.54	-1.03610811708113e-16
46	78.56	78.56	1.75097085260877e-16
47	77.48	77.48	-1.75463327708429e-17
48	81.59	81.59	4.60163368370146e-16
49	85.02	85.02	-1.88243099891565e-16
50	91.71	91.71	-2.07633543394654e-16
51	95.96	95.96	-3.82957438116646e-16
52	90.85	90.85	-2.60846663529970e-17
53	92.29	92.29	-3.755500492201e-17
54	95.57	95.57	1.95689279320945e-17
55	93.62	93.62	-6.02457435249086e-16
56	92.63	92.63	-1.80788427383978e-16
57	89.51	89.51	8.2304286644496e-17

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.0873313436743844	0.174662687348769	0.912668656325616
22	0.130367523215853	0.260735046431705	0.869632476784147
23	8.19721403014708e-08	1.63944280602942e-07	0.99999991802786
24	3.58082185052652e-05	7.16164370105305e-05	0.999964191781495
25	0.0105148868032906	0.0210297736065811	0.98948511319671
26	0.000251359355445590	0.000502718710891181	0.999748640644554
27	0.000127610726390705	0.000255221452781410	0.99987238927361
28	0.99882077326239	0.00235845347521842	0.00117922673760921
29	0.00466261860829687	0.00932523721659375	0.995337381391703
30	0.187487610382707	0.374975220765413	0.812512389617293
31	0.343335544950471	0.686671089900941	0.656664455049529
32	0.99608394778951	0.00783210442098223	0.00391605221049111
33	0.595298776653818	0.809402446692364	0.404701223346182
34	0.000373897393448865	0.00074779478689773	0.999626102606551
35	0.677868890386611	0.644262219226778	0.322131109613389
36	0.88695889516119	0.22608220967762	0.11304110483881

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	8	0.5	NOK
5% type I error level	9	0.5625	NOK
10% type I error level	9	0.5625	NOK