Multiple Linear Regression - Estimated Regression Equation

saldo_zichtrek[t] = + 4.25594081528187 + 0.720234616005331crisis[t] + 0.856141359397383`Yt-1`[t] + 0.0821221882076306`Yt-2`[t] -0.250361502036348`Yt-3`[t] + 0.228921383100153`Yt-4`[t] -1.70906219387124M1[t] -1.01532730724768M2[t] -0.767054591316031M3[t] -1.18454400203984M4[t] -0.537866020905931M5[t] + 0.613409664166027M6[t] -0.685453433357246M7[t] -2.43197864185542M8[t] -1.44235537582931M9[t] -1.1955274997821M10[t] -2.55721615948579M11[t] -0.00737361908607257t + 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)	4.25594081528187	2.781155	1.5303	0.13402	0.06701
crisis	0.720234616005331	0.45011	1.6001	0.117641	0.05882
`Yt-1`	0.856141359397383	0.15499	5.5239	2e-06	1e-06
`Yt-2`	0.0821221882076306	0.194523	0.4222	0.675218	0.337609
`Yt-3`	-0.250361502036348	0.192694	-1.2993	0.201479	0.10074
`Yt-4`	0.228921383100153	0.160764	1.424	0.162412	0.081206
M1	-1.70906219387124	0.635516	-2.6893	0.010482	0.005241
M2	-1.01532730724768	0.596114	-1.7032	0.09648	0.04824
M3	-0.767054591316031	0.660432	-1.1614	0.252521	0.12626
M4	-1.18454400203984	0.57887	-2.0463	0.04751	0.023755
M5	-0.537866020905931	0.604839	-0.8893	0.37931	0.189655
M6	0.613409664166027	0.608885	1.0074	0.319937	0.159969
M7	-0.685453433357246	0.714932	-0.9588	0.343582	0.171791
M8	-2.43197864185542	0.705807	-3.4457	0.001377	0.000689
M9	-1.44235537582931	0.686764	-2.1002	0.042227	0.021113
M10	-1.1955274997821	0.627557	-1.9051	0.064166	0.032083
M11	-2.55721615948579	0.609255	-4.1973	0.000151	7.6e-05
t	-0.00737361908607257	0.013092	-0.5632	0.576502	0.288251

Multiple Linear Regression - Regression Statistics
Multiple R	0.951284299126547
R-squared	0.904941817764685
Adjusted R-squared	0.86350619986724
F-TEST (value)	21.8397085329934
F-TEST (DF numerator)	17
F-TEST (DF denominator)	39
p-value	8.32667268468867e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.797997887006061
Sum Squared Residuals	24.8352244789794

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	30.996	30.5766312390722	0.419368760927771
2	31.033	31.4863003063172	-0.453300306317151
3	31.198	31.8446963943952	-0.64669639439516
4	30.937	31.5276895146551	-0.590689514655115
5	31.649	32.0558786602024	-0.406878660202423
6	33.115	33.7550799262958	-0.640079926295762
7	34.106	33.8655338208098	0.240466179190174
8	33.926	32.8424563378618	1.08354366213825
9	33.382	33.5479456914060	-0.16594569140605
10	32.851	33.3943675540844	-0.543367554084434
11	32.948	31.7979459040885	1.15005409591149
12	36.112	34.4822180825613	1.62978191743874
13	36.113	35.4909881081682	0.622011891831774
14	35.21	36.2921978004303	-1.08219780043035
15	35.193	34.990144953646	0.202855046353993
16	34.383	35.2006280794017	-0.817628079401719
17	35.349	35.3713672208601	-0.0223672208600803
18	37.058	37.0733230041708	-0.0153230041708290
19	38.076	37.5084630577169	0.56753694228308
20	36.63	36.3391874223678	0.290812577632183
21	36.045	35.9603273003092	0.0846726996907642
22	35.638	35.7165488125198	-0.0785488125198321
23	35.114	34.5460602202944	0.567939779705613
24	35.465	36.4293021164978	-0.964302116497802
25	35.254	34.9383180162834	0.315681983716615
26	35.299	35.5108767691942	-0.211876769194184
27	35.916	35.5651427535416	0.350857246458405
28	36.683	35.8053921233471	0.87760787665294
29	37.288	37.0924576187510	0.195542381248963
30	38.536	38.6731413410107	-0.137141341010666
31	38.977	38.4342701861058	0.542729813894208
32	36.407	37.1845321810047	-0.777532181004741
33	34.955	35.8287607015273	-0.873760701527295
34	34.951	34.7893281446608	0.161671855339216
35	32.68	34.0419832733365	-1.36198327333654
36	34.791	34.4223972441813	0.368602755818647
37	34.178	33.9953839492391	0.182616050760897
38	35.213	34.8979457883645	0.315054211635549
39	34.871	34.9262166989959	-0.0552166989958623
40	35.299	34.9302744295397	0.368725570460320
41	35.443	35.5084685425946	-0.0654685425945736
42	37.108	37.1333605260916	-0.0253605260916374
43	36.419	37.078978932089	-0.659978932089023
44	34.471	34.9338584469193	-0.462858446919319
45	33.868	33.8078753163541	0.0601246836459072
46	34.385	33.9247554887349	0.46024451126505
47	33.643	33.9990106022806	-0.356010602280565
48	34.627	35.6610825567596	-1.03408255675958
49	32.919	34.4586786872371	-1.53967868723706
50	35.5	34.0676793356939	1.43232066430613
51	36.11	35.9617991994214	0.148200800578625
52	37.086	36.9240158530564	0.161984146943574
53	37.711	37.4118279575919	0.299172042408114
54	40.427	39.6090952024311	0.817904797568894
55	39.884	40.5747540032784	-0.690754003278439
56	38.512	38.6459656118464	-0.133965611846371
57	38.767	37.8720909904033	0.894909009596673

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.309358967795888	0.618717935591775	0.690641032204112
22	0.161614061327992	0.323228122655983	0.838385938672008
23	0.13988710029793	0.27977420059586	0.86011289970207
24	0.449071255069568	0.898142510139137	0.550928744930432
25	0.498216936491179	0.996433872982359	0.501783063508821
26	0.571828988376596	0.856342023246807	0.428171011623404
27	0.538646435073217	0.922707129853567	0.461353564926783
28	0.554832542794564	0.890334914410872	0.445167457205436
29	0.474816950012249	0.949633900024497	0.525183049987751
30	0.352333913567286	0.704667827134572	0.647666086432714
31	0.555047418014722	0.889905163970557	0.444952581985278
32	0.565303876646763	0.869392246706474	0.434696123353237
33	0.442962996934555	0.88592599386911	0.557037003065445
34	0.309141875570677	0.618283751141354	0.690858124429323
35	0.615149794255518	0.769700411488964	0.384850205744482
36	0.669753866055279	0.660492267889443	0.330246133944721

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK