Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 0.62534313143448 + 0.134177451152066X[t] + 0.763994640040319`Y(t-1)`[t] + 0.0704456425436845`Y(t-2)`[t] + 0.0782248700521776`Y(t-3)`[t] -0.146998416229665`Y(t-4)`[t] + 3.86274414161116M1[t] + 2.64053282326136M2[t] + 0.873677019898664M3[t] + 1.00749157087014M4[t] + 0.226587076443826M5[t] + 1.87144092960793M6[t] + 0.964855849929924M7[t] + 3.12046893188267M8[t] + 0.988941013633353M9[t] + 2.26002196920389M10[t] -1.84545590981347M11[t] -0.0211274789115458t + 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)	0.62534313143448	3.092271	0.2022	0.84079	0.420395
X	0.134177451152066	0.180865	0.7419	0.462614	0.231307
`Y(t-1)`	0.763994640040319	0.169359	4.5111	5.8e-05	2.9e-05
`Y(t-2)`	0.0704456425436845	0.206742	0.3407	0.735127	0.367563
`Y(t-3)`	0.0782248700521776	0.20344	0.3845	0.702689	0.351344
`Y(t-4)`	-0.146998416229665	0.16191	-0.9079	0.369505	0.184753
M1	3.86274414161116	2.442532	1.5815	0.121852	0.060926
M2	2.64053282326136	2.55431	1.0338	0.307618	0.153809
M3	0.873677019898664	2.457133	0.3556	0.72408	0.36204
M4	1.00749157087014	2.443923	0.4122	0.682418	0.341209
M5	0.226587076443826	2.423242	0.0935	0.925981	0.46299
M6	1.87144092960793	2.368799	0.79	0.434283	0.217142
M7	0.964855849929924	2.426073	0.3977	0.693018	0.346509
M8	3.12046893188267	2.380822	1.3107	0.197636	0.098818
M9	0.988941013633353	2.489186	0.3973	0.693316	0.346658
M10	2.26002196920389	2.505296	0.9021	0.372543	0.186271
M11	-1.84545590981347	2.554184	-0.7225	0.474284	0.237142
t	-0.0211274789115458	0.034949	-0.6045	0.548997	0.274499

Multiple Linear Regression - Regression Statistics
Multiple R	0.919899890899325
R-squared	0.84621580927659
Adjusted R-squared	0.779181674858694
F-TEST (value)	12.6236553455171
F-TEST (DF numerator)	17
F-TEST (DF denominator)	39
p-value	6.12756512197166e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.47962494480970
Sum Squared Residuals	472.203800505133

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	19	21.630118286506	-2.63011828650599
2	22	20.7120567694659	1.28794323053411
3	23	21.0253305950230	1.97466940497697
4	20	21.791920132027	-1.79192013202699
5	14	19.0030244912684	-5.00302449126839
6	14	15.8175370920065	-1.81753709200653
7	14	13.8707937299254	0.129206270074637
8	15	15.8695834004208	-0.869583400420844
9	11	15.1750647090654	-4.17506470906540
10	17	13.5736627192589	3.42633728074114
11	16	14.1360756390991	1.86392436090094
12	20	15.2933628399365	4.70663716006354
13	24	23.5267166784808	0.473283321519236
14	23	24.6877591343557	-1.68775913435569
15	20	23.2263230516496	-3.22632305164961
16	21	20.5270556898373	0.472944310162739
17	19	19.9200710315876	-0.920071031587584
18	23	19.6094629660353	3.39053703396467
19	23	22.3709949584498	0.629005041550219
20	23	24.1886245827972	-1.18862458279716
21	23	22.6697009885347	0.330299011465254
22	27	23.5195092318880	3.48049076811203
23	26	22.2878694927379	3.71213050726213
24	17	23.5226438928525	-6.52264389285255
25	24	20.5563319463566	3.44366805364337
26	26	23.4009795468591	2.59902045314085
27	24	23.2246748244987	0.775325175501328
28	27	23.8074059928824	3.19259400711764
29	27	24.1094967945771	2.89050320542290
30	26	25.4806957787818	0.519304221218178
31	24	24.3847487483438	-0.384748748343841
32	23	24.6542348665694	-1.65423486656941
33	23	21.3306202426158	1.66937975738421
34	24	22.3664993017043	1.63350069829566
35	17	19.1257363304165	-2.12573633041646
36	21	15.8463818300399	5.15361816996009
37	19	22.3022469349168	-3.30224693491677
38	22	19.3730660783435	2.62693392165645
39	22	20.8902153933064	1.10978460669364
40	18	20.6710621647024	-2.67106216470244
41	16	17.0331149361694	-1.03311493616941
42	14	16.5939226430905	-2.59392264309049
43	12	13.4160751368201	-1.41607513682008
44	14	14.2461353739316	-0.246135373931552
45	16	13.8059734957318	2.19402650426818
46	8	16.5403287471488	-8.54032874714883
47	3	6.45031853774661	-3.45031853774661
48	0	3.33761143717108	-3.33761143717108
49	5	2.98458615373985	2.01541384626015
50	1	5.82613847097573	-4.82613847097573
51	1	1.63345613552232	-0.633456135522325
52	3	2.20255602055096	0.797443979449044
53	6	1.93429274639753	4.06570725360247
54	7	6.49838152008584	0.501618479914164
55	8	6.95738742646093	1.04261257353907
56	14	10.0414217762810	3.95857822371897
57	14	14.0186405640523	-0.0186405640522542

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.373070549844811	0.746141099689622	0.626929450155189
22	0.349474674790675	0.698949349581351	0.650525325209325
23	0.272016363830697	0.544032727661394	0.727983636169303
24	0.729319665539629	0.541360668920743	0.270680334460371
25	0.695398224588654	0.609203550822693	0.304601775411346
26	0.621722670211397	0.756554659577205	0.378277329788603
27	0.529954654909137	0.940090690181725	0.470045345090863
28	0.439054755848634	0.878109511697268	0.560945244151366
29	0.383031002312029	0.766062004624058	0.616968997687971
30	0.272605390944969	0.545210781889939	0.72739460905503
31	0.178907154429273	0.357814308858546	0.821092845570727
32	0.151589693533477	0.303179387066954	0.848410306466523
33	0.198508483621600	0.397016967243199	0.8014915163784
34	0.192881983911404	0.385763967822808	0.807118016088596
35	0.158903622051204	0.317807244102407	0.841096377948797
36	0.121153536403580	0.242307072807159	0.87884646359642

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