Multiple Linear Regression - Estimated Regression Equation
TWIB[t] = + 0.86793374916331 + 0.0509126058020017GI[t] + 1.47563767426721TWIB1[t] -0.787676888382591TWIB2[t] -0.140644079346224TWIB3[t] + 0.349848893647999TWIB4[t] -0.144827600397996M1[t] -0.118927642048376M2[t] + 0.608833924114353M3[t] -0.392339280785223M4[t] -0.00254042804679102M5[t] + 0.120655167502287M6[t] + 0.0123581916177904M7[t] + 0.162523576349744M8[t] + 0.0094586184244671M9[t] -0.104618718579842M10[t] -0.0223453107403499M11[t] -0.00703422898260113t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)0.867933749163310.6777241.28070.2080750.104037
GI0.05091260580200170.0287251.77240.0843410.042171
TWIB11.475637674267210.13641110.817600
TWIB2-0.7876768883825910.261456-3.01270.004590.002295
TWIB3-0.1406440793462240.262464-0.53590.5951770.297589
TWIB40.3498488936479990.1441112.42760.0200450.010022
M1-0.1448276003979960.102505-1.41290.165830.082915
M2-0.1189276420483760.105678-1.12540.2674870.133743
M30.6088339241143530.1073855.66972e-061e-06
M4-0.3923392807852230.140127-2.79990.0079910.003996
M5-0.002540428046791020.154355-0.01650.9869550.493477
M60.1206551675022870.122740.9830.331820.16591
M70.01235819161779040.100250.12330.902540.45127
M80.1625235763497440.1031431.57570.1233840.061692
M90.00945861842446710.111740.08460.9329850.466493
M10-0.1046187185798420.11305-0.92540.3605860.180293
M11-0.02234531074034990.106781-0.20930.835360.41768
t-0.007034228982601130.002396-2.93550.0056250.002812


Multiple Linear Regression - Regression Statistics
Multiple R0.986273371796106
R-squared0.97273516391406
Adjusted R-squared0.960537737244034
F-TEST (value)79.7492118812622
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.147760839247470
Sum Squared Residuals0.829664093374437


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
17.27.21835750945546-0.0183575094554583
27.47.44473681581612-0.0447368158161163
38.88.639576831597640.160423168402361
49.39.29694961231830.00305038768170033
59.39.171519424348060.128480575651940
68.78.80254923842953-0.102549238429532
78.28.2111193192758-0.0111193192757963
88.38.25377969658470.0462203034153002
98.58.72456042948277-0.224560429482775
108.68.69040393415576-0.0904039341557564
118.58.59213895090523-0.0921389509052281
128.28.36760960757275-0.167609607572754
138.17.907729535545230.19227046445477
147.98.07456638246013-0.174566382460129
158.68.591233468644340.00876653135565657
168.78.682617524265950.0173824757340503
178.78.649624759504930.0503752404950684
188.58.488050239479990.0119497605200076
198.48.323695099119030.0763049008809687
208.58.52705653622358-0.0270565362235826
218.78.606143839709330.0938561602906723
228.78.645486748942620.0545132510573787
238.68.478502428762170.121497571237829
248.58.373470858909560.126529141090445
258.38.23296525083050.0670347491695038
2688.03935302095656-0.039353020956557
278.28.44891269152266-0.248912691522662
288.17.970371046093350.129628953906652
298.18.025351230400410.0746487695995913
3088.0922880624217-0.0922880624216998
317.97.9134272767921-0.0134272767921033
327.97.9527774645882-0.0527774645881952
3387.89060163503340.109398364966601
3487.901224615623230.098775384376769
357.97.852528695116660.0474713048833375
3687.706211601513070.293788398486931
377.77.78511855428105-0.0851185542810496
387.27.29658970046427-0.096589700464271
397.57.471843230306350.0281567696936487
407.37.3671611349039-0.067161134903898
4177.18895378945042-0.188953789450422
4276.828297863686390.171702136313610
4377.10271825161851-0.102718251618515
447.27.23843789476294-0.038437894762935
457.37.27869409577450.0213059042255011
467.17.16288470127839-0.0628847012783912
476.86.87682992521594-0.0768299252159383
486.46.65270793200462-0.252707932004621
496.16.25582914988777-0.155829149887766
506.56.144754080302930.355245919697073
517.77.6484337779290.0515662220709958
527.97.9829006824185-0.0829006824185043
537.57.56455079629618-0.0645507962961778
546.96.888814595982390.0111854040176149
556.66.549040053194550.0509599468054454
566.96.827948407840590.0720515921594126


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.01390351178174990.02780702356349990.98609648821825
220.1489726478648480.2979452957296970.851027352135152
230.07959532106467930.1591906421293590.92040467893532
240.05084526120968930.1016905224193790.94915473879031
250.03493664921214560.06987329842429120.965063350787854
260.01862543357084940.03725086714169880.98137456642915
270.3333185788903940.6666371577807870.666681421109606
280.2381438674257340.4762877348514680.761856132574266
290.1606728254858810.3213456509717630.839327174514119
300.2036861789177090.4073723578354170.796313821082291
310.1546230161123080.3092460322246160.845376983887692
320.1382157638116010.2764315276232020.861784236188399
330.1027795374362830.2055590748725660.897220462563717
340.09724512301095460.1944902460219090.902754876989045
350.07799576924259290.1559915384851860.922004230757407


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.133333333333333NOK
10% type I error level30.2NOK