Multiple Linear Regression - Estimated Regression Equation

Werkl[t] = + 13.181305483574 -0.0167499771012351Infl[t] + 1.01704809359358`Yt-1`[t] + 0.0183717956239474`Yt-2`[t] -0.304695129059736`Yt-3`[t] + 0.159093233761395`Yt-4`[t] + 0.0623924437488247M1[t] + 0.205812380211141M2[t] -0.0775331339962117M3[t] -0.106742976619106M4[t] + 0.0578994848837974M5[t] + 0.227318039355702M6[t] + 0.177074745524402M7[t] + 0.165127828048767M8[t] + 0.204067321450513M9[t] + 0.271447808716314M10[t] + 0.142836066402679M11[t] -0.00332475677410949t + 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)	13.181305483574	6.770538	1.9469	0.058971	0.029485
Infl	-0.0167499771012351	0.004804	-3.487	0.00125	0.000625
`Yt-1`	1.01704809359358	0.151537	6.7115	0	0
`Yt-2`	0.0183717956239474	0.220522	0.0833	0.934042	0.467021
`Yt-3`	-0.304695129059736	0.221958	-1.3728	0.177875	0.088938
`Yt-4`	0.159093233761395	0.147438	1.0791	0.287366	0.143683
M1	0.0623924437488247	0.085715	0.7279	0.471132	0.235566
M2	0.205812380211141	0.092299	2.2298	0.031743	0.015871
M3	-0.0775331339962117	0.09766	-0.7939	0.432177	0.216089
M4	-0.106742976619106	0.100624	-1.0608	0.295473	0.147736
M5	0.0578994848837974	0.089024	0.6504	0.519361	0.25968
M6	0.227318039355702	0.089078	2.5519	0.014858	0.007429
M7	0.177074745524402	0.095133	1.8613	0.070444	0.035222
M8	0.165127828048767	0.087082	1.8962	0.065552	0.032776
M9	0.204067321450513	0.090057	2.266	0.029232	0.014616
M10	0.271447808716314	0.087615	3.0982	0.003653	0.001827
M11	0.142836066402679	0.093264	1.5315	0.133925	0.066962
t	-0.00332475677410949	0.003697	-0.8993	0.374174	0.187087

Multiple Linear Regression - Regression Statistics
Multiple R	0.996509035112443
R-squared	0.993030257060732
Adjusted R-squared	0.989912214166848
F-TEST (value)	318.478703102129
F-TEST (DF numerator)	17
F-TEST (DF denominator)	38
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.102457087339735
Sum Squared Residuals	0.398903280353396

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	105.8	105.811705379203	-0.0117053792030353
2	105.7	105.761465623687	-0.0614656236867831
3	105.6	105.538238344935	0.0617616550653452
4	105.4	105.565796742347	-0.165796742346709
5	105.4	105.261728213864	0.138271786136022
6	105.5	105.455457819068	0.0445421809319375
7	105.6	105.518474321476	0.0815256785244096
8	105.7	105.625175920699	0.074824079300987
9	105.9	105.842737984500	0.057262015499536
10	106.1	106.107530310004	-0.00753031000418853
11	106	105.982192853406	0.0178071465936141
12	105.8	105.754946792834	0.0450532071662002
13	105.8	105.636597224612	0.163402775388063
14	105.7	105.697956392603	0.00204360739653135
15	105.5	105.557285737623	-0.057285737623412
16	105.3	105.329560635946	-0.0295606359461054
17	105.2	105.061339221509	0.138660778491294
18	105.2	105.215658486752	-0.0156584867517507
19	105	105.122373727239	-0.122373727238677
20	105.1	105.006193158452	0.0938068415484304
21	105.1	105.195953923473	-0.0959539234729413
22	105.2	105.332835845600	-0.132835845599708
23	104.9	105.014191305346	-0.114191305346402
24	104.8	104.779987284535	0.0200127154652182
25	104.5	104.580769275428	-0.0807692754280743
26	104.5	104.474330773686	0.0256692263135833
27	104.4	104.380965211401	0.0190347885987440
28	104.4	104.318875022566	0.0811249774335682
29	104.2	104.253077820331	-0.0530778203313235
30	104.1	104.174206610681	-0.0742066106810689
31	103.9	103.964175116303	-0.0641751163027591
32	103.8	103.967070447466	-0.167070447465852
33	103.9	103.875856909242	0.0241430907584613
34	104.2	104.113284933038	0.086715066961893
35	104.1	104.114426143602	-0.0144261436018154
36	103.8	103.976443007382	-0.17644300738184
37	103.6	103.592759953810	0.00724004619013386
38	103.7	103.640656406459	0.059343593540604
39	103.5	103.592840711749	-0.0928407117491103
40	103.4	103.400419689951	-0.000419689951405759
41	103.1	103.268445238279	-0.168445238278550
42	103.1	103.063735969874	0.0362640301264214
43	103.1	103.016707228416	0.083292771584335
44	103.2	103.276259497012	-0.0762594970123955
45	103.3	103.285451182785	0.014548817214944
46	103.5	103.446348911358	0.0536510886420031
47	103.6	103.489189697645	0.110810302354604
48	103.5	103.388622915250	0.111377084750422
49	103.3	103.378168166947	-0.0781681669470875
50	103.2	103.225590803564	-0.0255908035639357
51	103.1	103.030669994292	0.0693300057084329
52	103.2	103.085347909189	0.114652090810652
53	103	103.055409506017	-0.0554095060174416
54	103	102.990941113626	0.00905888637446086
55	103.1	103.078269606567	0.0217303934326917
56	103.4	103.325300976371	0.0746990236288304

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.747515140273827	0.504969719452346	0.252484859726173
22	0.724596359982256	0.550807280035488	0.275403640017744
23	0.675975286593944	0.648049426812112	0.324024713406056
24	0.759624611212188	0.480750777575625	0.240375388787812
25	0.74028073563245	0.519438528735099	0.259719264367549
26	0.791306721315375	0.417386557369249	0.208693278684625
27	0.742245505581885	0.515508988836231	0.257754494418115
28	0.732950625853426	0.534098748293149	0.267049374146574
29	0.966131009154233	0.067737981691533	0.0338689908457665
30	0.949839455727235	0.100321088545529	0.0501605442727646
31	0.937656429036802	0.124687141926397	0.0623435709631984
32	0.934563929953443	0.130872140093114	0.0654360700465571
33	0.884327938712213	0.231344122575574	0.115672061287787
34	0.819234270766169	0.361531458467662	0.180765729233831
35	0.771134674933003	0.457730650133994	0.228865325066997

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	1	0.0666666666666667	OK