Multiple Linear Regression - Estimated Regression Equation

Y[t] = -2.83103972160239 -0.00497969791234237X[t] + 0.932713845855733Y1[t] -0.0849261973825332Y2[t] -0.295020473860288Y3[t] + 0.179390247778269Y4[t] + 0.299672934182992`Y5 `[t] + 0.066291966934211M1[t] + 0.070275643501231M2[t] + 0.0678099255876927M3[t] + 0.155510564164373M4[t] + 0.181984794020036M5[t] + 0.128406954400737M6[t] + 0.123356529101140M7[t] + 0.144114792469188M8[t] + 0.102750256297642M9[t] + 0.080114755808093M10[t] + 0.0357874509842374M11[t] -0.000149240410139286t + 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)	-2.83103972160239	4.34225	-0.652	0.518558	0.259279
X	-0.00497969791234237	0.001643	-3.0301	0.004506	0.002253
Y1	0.932713845855733	0.157745	5.9128	1e-06	0
Y2	-0.0849261973825332	0.224609	-0.3781	0.707572	0.353786
Y3	-0.295020473860288	0.243782	-1.2102	0.234096	0.117048
Y4	0.179390247778269	0.239424	0.7493	0.458569	0.229285
`Y5 `	0.299672934182992	0.185883	1.6122	0.115661	0.057831
M1	0.066291966934211	0.049712	1.3335	0.190735	0.095367
M2	0.070275643501231	0.051292	1.3701	0.179141	0.089571
M3	0.0678099255876927	0.050229	1.35	0.185442	0.092721
M4	0.155510564164373	0.048629	3.1979	0.002883	0.001442
M5	0.181984794020036	0.051174	3.5562	0.001076	0.000538
M6	0.128406954400737	0.053728	2.3899	0.022209	0.011104
M7	0.123356529101140	0.055083	2.2395	0.031396	0.015698
M8	0.144114792469188	0.065772	2.1911	0.034997	0.017499
M9	0.102750256297642	0.061791	1.6629	0.105024	0.052512
M10	0.080114755808093	0.051751	1.5481	0.130347	0.065174
M11	0.0357874509842374	0.050554	0.7079	0.483566	0.241783
t	-0.000149240410139286	0.003603	-0.0414	0.967193	0.483596

Multiple Linear Regression - Regression Statistics
Multiple R	0.997724269607044
R-squared	0.99545371816291
Adjusted R-squared	0.993180577244363
F-TEST (value)	437.919932742148
F-TEST (DF numerator)	18
F-TEST (DF denominator)	36
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0701871577428226
Sum Squared Residuals	0.177344536032571

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	103.91	103.878451238188	0.0315487618122744
2	103.91	103.884020946423	0.0259790535770017
3	103.92	103.889063308894	0.0309366911055615
4	104.05	104.039747577144	0.0102524228558393
5	104.23	104.229303562892	0.000696437108322132
6	104.3	104.354399586063	-0.0543995860629370
7	104.31	104.375591629482	-0.0655916294822722
8	104.31	104.382258159374	-0.0722581593735686
9	104.34	104.399454869934	-0.0594548699339316
10	104.55	104.477661211202	0.0723387887978133
11	104.65	104.644298097635	0.00570190236534622
12	104.73	104.677944404502	0.0520555954984488
13	104.75	104.748161359175	0.00183864082505709
14	104.75	104.776534341010	-0.0265343410101180
15	104.76	104.817538286797	-0.0575382867965953
16	104.94	104.953332896976	-0.0133328969762984
17	105.29	105.173760786601	0.116239213399018
18	105.38	105.421292876465	-0.0412928764651777
19	105.43	105.415019746652	0.0149802533483907
20	105.43	105.415614368471	0.0143856315290806
21	105.42	105.457540305292	-0.0375403052918494
22	105.52	105.530214142131	-0.0102141421309762
23	105.69	105.603847044932	0.086152955067867
24	105.72	105.733423090087	-0.0134230900865648
25	105.74	105.762393006709	-0.0223930067093367
26	105.74	105.730689745631	0.00931025436913267
27	105.74	105.772013647190	-0.0320136471895232
28	105.95	105.916464349409	0.033535650590646
29	106.17	106.142771753015	0.0272282469853371
30	106.34	106.282898646098	0.0571013539019569
31	106.37	106.354626331666	0.0153736683335060
32	106.37	106.356567066317	0.0134329336831226
33	106.36	106.356781677287	0.00321832271262681
34	106.44	106.381867414091	0.0581325859094093
35	106.29	106.457730039545	-0.167730039544940
36	106.23	106.273587383882	-0.0435873838823813
37	106.23	106.232766994951	-0.00276699495084360
38	106.23	106.268422316619	-0.0384223166185613
39	106.23	106.269120579096	-0.0391205790960142
40	106.34	106.310419048302	0.0295809516981365
41	106.44	106.443770825346	-0.00377082534608349
42	106.44	106.471483399234	-0.0314833992339631
43	106.48	106.408407888759	0.0715921112406273
44	106.5	106.455560405839	0.0444395941613654
45	106.57	106.476223147487	0.0937768525131542
46	106.4	106.520257232576	-0.120257232576246
47	106.37	106.294124817888	0.0758751821117265
48	106.25	106.245045121530	0.00495487847049753
49	106.21	106.218227400977	-0.00822740097715119
50	106.21	106.180332650317	0.0296673496825448
51	106.24	106.142264178023	0.0977358219765712
52	106.19	106.250036128168	-0.0600361281683234
53	106.08	106.220393072147	-0.140393072146593
54	106.13	106.059925492140	0.0700745078601208
55	106.09	106.126354403440	-0.0363544034402519

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
22	0.171047795996972	0.342095591993943	0.828952204003028
23	0.0790724928354266	0.158144985670853	0.920927507164573
24	0.0553682055472368	0.110736411094474	0.944631794452763
25	0.0903657768176242	0.180731553635248	0.909634223182376
26	0.204462626340326	0.408925252680652	0.795537373659674
27	0.233771117010724	0.467542234021448	0.766228882989276
28	0.146088471827494	0.292176943654988	0.853911528172506
29	0.158435154434980	0.316870308869960	0.84156484556502
30	0.159355091175182	0.318710182350364	0.840644908824818
31	0.08838058442943	0.17676116885886	0.91161941557057
32	0.306315355309148	0.612630710618297	0.693684644690852
33	0.339060398319434	0.678120796638869	0.660939601680566

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