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Master regressie 1 aug 2008 zonder trend

*The author of this computation has been verified*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sun, 14 Dec 2008 11:14:53 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/14/t1229278709sfrfd3vf5e9ekwt.htm/, Retrieved Sun, 14 Dec 2008 19:18:29 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Dec/14/t1229278709sfrfd3vf5e9ekwt.htm/},
    year = {2008},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

8310 0 7649 0 7279 0 6857 0 6496 0 6280 0 8962 0 11205 0 10363 0 9175 0 8234 0 8121 0 7438 0 6876 0 6489 0 6319 0 5952 0 6055 0 9107 0 11493 0 10213 0 9238 0 8218 0 7995 0 7581 0 7051 0 6668 0 6433 0 6135 0 6365 0 10095 0 12029 0 12184 0 11331 0 9961 0 9739 0 9080 0 8507 0 8097 0 7772 0 7440 0 7902 0 13539 0 14992 0 15436 0 14156 0 12846 0 12302 0 11691 0 10648 0 10064 0 10016 0 9691 0 10260 0 16882 0 18573 0 18227 0 16346 0 14694 0 14453 0 13949 0 13277 0 12726 0 12279 0 11819 0 12207 0 18637 0 20519 0 19974 0 17802 0 15997 0 15430 0 14452 0 13614 0 13080 0 12290 0 11890 0 12292 0 18700 0 20388 0 19170 0 17530 0 15564 0 15163 0 13406 0 12763 0 12083 0 12054 0 11770 0 12266 0 17549 0 18655 0 17279 0 14788 0 13138 0 12494 0 11767 0 10928 0 10104 0 9760 0 9536 0 9978 0 14846 0 15565 0 13587 0 11804 0 10611 0 10915 0 9988 0 9376 0 9319 0 8852 0 8392 0 9050 0 13250 0 14037 1 12486 1 11182 1 10287 1

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

Multiple Linear Regression - Estimated Regression Equation

NWWZM[t] = + 11845.7777777778 -2204.36111111111Dummy[t] -1079.57777777778M1[t] -1776.87777777778M2[t] -2254.87777777778M3[t] -2582.57777777778M4[t] -2933.67777777778M5[t] -2580.27777777778M6[t] + 2310.92222222222M7[t] + 4120.25833333333M8[t] + 3266.55833333333M9[t] + 1709.85833333333M10[t] + 329.658333333333M11[t] + 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)	11845.7777777778	982.637865	12.0551	0	0
Dummy	-2204.36111111111	1553.686885	-1.4188	0.158893	0.079446
M1	-1079.57777777778	1354.472824	-0.797	0.427206	0.213603
M2	-1776.87777777778	1354.472824	-1.3119	0.192402	0.096201
M3	-2254.87777777778	1354.472824	-1.6648	0.098913	0.049457
M4	-2582.57777777778	1354.472824	-1.9067	0.059267	0.029633
M5	-2933.67777777778	1354.472824	-2.1659	0.032559	0.016279
M6	-2580.27777777778	1354.472824	-1.905	0.059489	0.029745
M7	2310.92222222222	1354.472824	1.7061	0.09091	0.045455
M8	4120.25833333333	1363.354708	3.0221	0.003147	0.001574
M9	3266.55833333333	1363.354708	2.396	0.018329	0.009164
M10	1709.85833333333	1363.354708	1.2542	0.212544	0.106272
M11	329.658333333333	1363.354708	0.2418	0.809403	0.404701

Multiple Linear Regression - Regression Statistics
Multiple R	0.64228052981112
R-squared	0.412524278974453
Adjusted R-squared	0.346017593575334
F-TEST (value)	6.20274903942093
F-TEST (DF numerator)	12
F-TEST (DF denominator)	106
p-value	3.58258531729660e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2947.9135961937
Sum Squared Residuals	921160624.486111

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	8310	10766.2	-2456.20000000001
2	7649	10068.9	-2419.9
3	7279	9590.9	-2311.9
4	6857	9263.2	-2406.2
5	6496	8912.1	-2416.1
6	6280	9265.5	-2985.5
7	8962	14156.7	-5194.7
8	11205	15966.0361111111	-4761.03611111111
9	10363	15112.3361111111	-4749.33611111111
10	9175	13555.6361111111	-4380.63611111111
11	8234	12175.4361111111	-3941.43611111111
12	8121	11845.7777777778	-3724.77777777778
13	7438	10766.2	-3328.2
14	6876	10068.9	-3192.9
15	6489	9590.9	-3101.9
16	6319	9263.2	-2944.2
17	5952	8912.1	-2960.1
18	6055	9265.5	-3210.5
19	9107	14156.7	-5049.7
20	11493	15966.0361111111	-4473.03611111111
21	10213	15112.3361111111	-4899.33611111111
22	9238	13555.6361111111	-4317.63611111111
23	8218	12175.4361111111	-3957.43611111111
24	7995	11845.7777777778	-3850.77777777778
25	7581	10766.2	-3185.2
26	7051	10068.9	-3017.9
27	6668	9590.9	-2922.9
28	6433	9263.2	-2830.2
29	6135	8912.1	-2777.1
30	6365	9265.5	-2900.5
31	10095	14156.7	-4061.7
32	12029	15966.0361111111	-3937.03611111111
33	12184	15112.3361111111	-2928.33611111111
34	11331	13555.6361111111	-2224.63611111111
35	9961	12175.4361111111	-2214.43611111111
36	9739	11845.7777777778	-2106.77777777778
37	9080	10766.2	-1686.2
38	8507	10068.9	-1561.9
39	8097	9590.9	-1493.9
40	7772	9263.2	-1491.2
41	7440	8912.1	-1472.1
42	7902	9265.5	-1363.5
43	13539	14156.7	-617.7
44	14992	15966.0361111111	-974.03611111111
45	15436	15112.3361111111	323.663888888889
46	14156	13555.6361111111	600.363888888888
47	12846	12175.4361111111	670.563888888888
48	12302	11845.7777777778	456.222222222222
49	11691	10766.2	924.8
50	10648	10068.9	579.1
51	10064	9590.9	473.1
52	10016	9263.2	752.8
53	9691	8912.1	778.9
54	10260	9265.5	994.499999999999
55	16882	14156.7	2725.3
56	18573	15966.0361111111	2606.96388888889
57	18227	15112.3361111111	3114.66388888889
58	16346	13555.6361111111	2790.36388888889
59	14694	12175.4361111111	2518.56388888889
60	14453	11845.7777777778	2607.22222222222
61	13949	10766.2	3182.8
62	13277	10068.9	3208.1
63	12726	9590.9	3135.1
64	12279	9263.2	3015.8
65	11819	8912.1	2906.9
66	12207	9265.5	2941.5
67	18637	14156.7	4480.3
68	20519	15966.0361111111	4552.96388888888
69	19974	15112.3361111111	4861.66388888889
70	17802	13555.6361111111	4246.36388888889
71	15997	12175.4361111111	3821.56388888889
72	15430	11845.7777777778	3584.22222222222
73	14452	10766.2	3685.8
74	13614	10068.9	3545.1
75	13080	9590.9	3489.1
76	12290	9263.2	3026.8
77	11890	8912.1	2977.9
78	12292	9265.5	3026.5
79	18700	14156.7	4543.3
80	20388	15966.0361111111	4421.96388888888
81	19170	15112.3361111111	4057.66388888889
82	17530	13555.6361111111	3974.36388888889
83	15564	12175.4361111111	3388.56388888889
84	15163	11845.7777777778	3317.22222222222
85	13406	10766.2	2639.8
86	12763	10068.9	2694.1
87	12083	9590.9	2492.1
88	12054	9263.2	2790.8
89	11770	8912.1	2857.9
90	12266	9265.5	3000.5
91	17549	14156.7	3392.3
92	18655	15966.0361111111	2688.96388888889
93	17279	15112.3361111111	2166.66388888889
94	14788	13555.6361111111	1232.36388888889
95	13138	12175.4361111111	962.563888888888
96	12494	11845.7777777778	648.222222222222
97	11767	10766.2	1000.8
98	10928	10068.9	859.1
99	10104	9590.9	513.1
100	9760	9263.2	496.8
101	9536	8912.1	623.9
102	9978	9265.5	712.499999999999
103	14846	14156.7	689.3
104	15565	15966.0361111111	-401.036111111109
105	13587	15112.3361111111	-1525.33611111111
106	11804	13555.6361111111	-1751.63611111111
107	10611	12175.4361111111	-1564.43611111111
108	10915	11845.7777777778	-930.777777777778
109	9988	10766.2	-778.199999999999
110	9376	10068.9	-692.9
111	9319	9590.9	-271.900000000000
112	8852	9263.2	-411.2
113	8392	8912.1	-520.1
114	9050	9265.5	-215.500000000001
115	13250	14156.7	-906.700000000001
116	14037	13761.675	275.325000000000
117	12486	12907.975	-421.975
118	11182	11351.275	-169.275
119	10287	9971.075	315.925000000001

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.0145674206465287	0.0291348412930575	0.985432579353471
17	0.00340782946780171	0.00681565893560341	0.996592170532198
18	0.000648005867894499	0.00129601173578900	0.999351994132106
19	0.000125502615528903	0.000251005231057807	0.999874497384471
20	2.52336265450165e-05	5.0467253090033e-05	0.999974766373455
21	4.90513728718034e-06	9.81027457436069e-06	0.999995094862713
22	8.92149544212527e-07	1.78429908842505e-06	0.999999107850456
23	1.56055249665775e-07	3.12110499331551e-07	0.99999984394475
24	2.76900642762412e-08	5.53801285524824e-08	0.999999972309936
25	5.81286686707123e-09	1.16257337341425e-08	0.999999994187133
26	1.09209414645593e-09	2.18418829291186e-09	0.999999998907906
27	2.06721240270933e-10	4.13442480541867e-10	0.999999999793279
28	3.65454071074413e-11	7.30908142148826e-11	0.999999999963455
29	6.14071417002784e-12	1.22814283400557e-11	0.99999999999386
30	1.20879170708984e-12	2.41758341417967e-12	0.999999999998791
31	1.01537495187918e-11	2.03074990375836e-11	0.999999999989846
32	1.12219022787337e-11	2.24438045574674e-11	0.999999999988778
33	1.72157789135548e-09	3.44315578271097e-09	0.999999998278422
34	5.73988663443821e-08	1.14797732688764e-07	0.999999942601134
35	2.26477593890587e-07	4.52955187781173e-07	0.999999773522406
36	6.02012819181911e-07	1.20402563836382e-06	0.99999939798718
37	8.48166700041896e-07	1.69633340008379e-06	0.9999991518333
38	1.15092730362800e-06	2.30185460725601e-06	0.999998849072696
39	1.44975221208565e-06	2.89950442417131e-06	0.999998550247788
40	1.71272997707351e-06	3.42545995414702e-06	0.999998287270023
41	2.05596219091400e-06	4.11192438182800e-06	0.99999794403781
42	4.24106916753249e-06	8.48213833506499e-06	0.999995758930832
43	0.000486087610010037	0.000972175220020074	0.99951391238999
44	0.00365231763039322	0.00730463526078644	0.996347682369607
45	0.0289324613426527	0.0578649226853055	0.971067538657347
46	0.0826517307731452	0.165303461546290	0.917348269226855
47	0.152855778685849	0.305711557371699	0.84714422131415
48	0.216146882670219	0.432293765340438	0.783853117329781
49	0.279092551671796	0.558185103343592	0.720907448328204
50	0.317530331294977	0.635060662589953	0.682469668705023
51	0.344189205584127	0.688378411168255	0.655810794415873
52	0.375245023764772	0.750490047529545	0.624754976235228
53	0.402947961911792	0.805895923823584	0.597052038088208
54	0.447283455769386	0.894566911538771	0.552716544230614
55	0.662883302574435	0.67423339485113	0.337116697425565
56	0.789186361479652	0.421627277040697	0.210813638520348
57	0.870632155315175	0.258735689369649	0.129367844684825
58	0.903775375864996	0.192449248270008	0.0962246241350038
59	0.919037770074775	0.161924459850450	0.0809622299252252
60	0.93136742709985	0.1372651458003	0.06863257290015
61	0.945892448077646	0.108215103844708	0.0541075519223539
62	0.956562862646199	0.086874274707602	0.043437137353801
63	0.963425874272881	0.0731482514542378	0.0365741257271189
64	0.967328612028232	0.065342775943536	0.032671387971768
65	0.969332864902846	0.0613342701943082	0.0306671350971541
66	0.971017580740707	0.0579648385185867	0.0289824192592933
67	0.983195005487442	0.0336099890251168	0.0168049945125584
68	0.989311206361313	0.0213775872773736	0.0106887936386868
69	0.994283533002975	0.0114329339940495	0.00571646699702476
70	0.996089427079844	0.00782114584031187	0.00391057292015594
71	0.996811673795159	0.00637665240968242	0.00318832620484121
72	0.997150874046621	0.00569825190675772	0.00284912595337886
73	0.997507908676843	0.00498418264631389	0.00249209132315695
74	0.997675883663737	0.00464823267252682	0.00232411633626341
75	0.997784555514912	0.00443088897017544	0.00221544448508772
76	0.997473605740592	0.00505278851881557	0.00252639425940778
77	0.997039953637126	0.00592009272574772	0.00296004636287386
78	0.996458899708335	0.00708220058332931	0.00354110029166465
79	0.997696765527878	0.00460646894424486	0.00230323447212243
80	0.998259477430697	0.00348104513860517	0.00174052256930258
81	0.998872522916368	0.00225495416726424	0.00112747708363212
82	0.99941743610121	0.00116512779758013	0.000582563898790064
83	0.999593085240391	0.00081382951921742	0.00040691475960871
84	0.999715341523435	0.000569316953129206	0.000284658476564603
85	0.99967892529691	0.00064214940618169	0.000321074703090845
86	0.999654411295569	0.000691177408862767	0.000345588704431383
87	0.9995836616198	0.00083267676039962	0.00041633838019981
88	0.99959265847228	0.000814683055440472	0.000407341527720236
89	0.999626450707462	0.000747098585075168	0.000373549292537584
90	0.999662611067725	0.000674777864550654	0.000337388932275327
91	0.99985444003792	0.00029111992415875	0.000145559962079375
92	0.999890917987482	0.000218164025036872	0.000109082012518436
93	0.999969518864104	6.0962271790986e-05	3.0481135895493e-05
94	0.999985443640253	2.91127194948222e-05	1.45563597474111e-05
95	0.99999087820128	1.82435974383206e-05	9.12179871916032e-06
96	0.99998392177641	3.21564471809689e-05	1.60782235904845e-05
97	0.999980047812078	3.99043758447803e-05	1.99521879223902e-05
98	0.99996988687296	6.02262540818435e-05	3.01131270409217e-05
99	0.999886887354232	0.000226225291535230	0.000113112645767615
100	0.999619370045578	0.000761259908843239	0.000380629954421620
101	0.9990079529564	0.00198409408719948	0.00099204704359974
102	0.996664651341586	0.00667069731682702	0.00333534865841351
103	0.997736369094703	0.00452726181059313	0.00226363090529656

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	62	0.704545454545455	NOK
5% type I error level	66	0.75	NOK
10% type I error level	72	0.818181818181818	NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)

library(lmtest)

n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

x1 <- cbind(x[,par1], x[,1:k!=par1])

mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])

colnames(x1) <- mycolnames #colnames(x)[par1]

x <- x1

if (par3 == 'First Differences'){

x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))

for (i in 1:n-1) {

for (j in 1:k) {

x2[i,j] <- x[i+1,j] - x[i,j]

}

}

x <- x2

}

if (par2 == 'Include Monthly Dummies'){

x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))

for (i in 1:11){

x2[seq(i,n,12),i] <- 1

}

x <- cbind(x, x2)

}

if (par2 == 'Include Quarterly Dummies'){

x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))

for (i in 1:3){

x2[seq(i,n,4),i] <- 1

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

x <- cbind(x, c(1:n))

colnames(x)[k+1] <- 't'

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

for (myalt in c('greater', 'two.sided', 'less')) {

j <- j + 1

gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value

}

if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1

if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1

if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1

}

gqarr

}

bitmap(file='test0.png')

plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')

points(x[,1]-mysum$resid)

grid()

dev.off()

bitmap(file='test1.png')

plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')

grid()

dev.off()

bitmap(file='test2.png')

hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')

grid()

dev.off()

bitmap(file='test3.png')

densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')

dev.off()

bitmap(file='test4.png')

qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')

qqline(mysum$resid)

grid()

dev.off()

(myerror <- as.ts(mysum$resid))

bitmap(file='test5.png')

dum <- cbind(lag(myerror,k=1),myerror)

dum

dum1 <- dum[2:length(myerror),]

dum1

z <- as.data.frame(dum1)

z

plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')

grid()

dev.off()

bitmap(file='test7.png')

pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')

grid()

dev.off()

bitmap(file='test8.png')

opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))

plot(mylm, las = 1, sub='Residual Diagnostics')

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)

a<-table.row.end(a)

myeq <- colnames(x)[1]

myeq <- paste(myeq, '[t] = ', sep='')

for (i in 1:k){

if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')

myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')

if (rownames(mysum$coefficients)[i] != '(Intercept)') {

myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')

if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')

}

}

myeq <- paste(myeq, ' + e[t]')

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable1.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Variable',header=TRUE)

a<-table.element(a,'Parameter',header=TRUE)

a<-table.element(a,'S.D.',header=TRUE)

a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)

a<-table.element(a,'2-tail p-value',header=TRUE)

a<-table.element(a,'1-tail p-value',header=TRUE)

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)

a<-table.element(a,mysum$coefficients[i,1])

a<-table.element(a, round(mysum$coefficients[i,2],6))

a<-table.element(a, round(mysum$coefficients[i,3],4))

a<-table.element(a, round(mysum$coefficients[i,4],6))

a<-table.element(a, round(mysum$coefficients[i,4]/2,6))

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable2.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple R',1,TRUE)

a<-table.element(a, sqrt(mysum$r.squared))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'R-squared',1,TRUE)

a<-table.element(a, mysum$r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Adjusted R-squared',1,TRUE)

a<-table.element(a, mysum$adj.r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (value)',1,TRUE)

a<-table.element(a, mysum$fstatistic[1])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[2])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[3])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'p-value',1,TRUE)

a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Residual Standard Deviation',1,TRUE)

a<-table.element(a, mysum$sigma)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Sum Squared Residuals',1,TRUE)

a<-table.element(a, sum(myerror*myerror))

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable3.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Time or Index', 1, TRUE)

a<-table.element(a, 'Actuals', 1, TRUE)

a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)

a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,i, 1, TRUE)

a<-table.element(a,x[i])

a<-table.element(a,x[i]-mysum$resid[i])

a<-table.element(a,mysum$resid[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable4.tab')

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'p-values',header=TRUE)

a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'breakpoint index',header=TRUE)

a<-table.element(a,'greater',header=TRUE)

a<-table.element(a,'2-sided',header=TRUE)

a<-table.element(a,'less',header=TRUE)

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

a<-table.element(a,mypoint,header=TRUE)

a<-table.element(a,gqarr[mypoint-kp3+1,1])

a<-table.element(a,gqarr[mypoint-kp3+1,2])

a<-table.element(a,gqarr[mypoint-kp3+1,3])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable5.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Description',header=TRUE)

a<-table.element(a,'# significant tests',header=TRUE)

a<-table.element(a,'% significant tests',header=TRUE)

a<-table.element(a,'OK/NOK',header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'1% type I error level',header=TRUE)

a<-table.element(a,numsignificant1)

a<-table.element(a,numsignificant1/numgqtests)

if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'5% type I error level',header=TRUE)

a<-table.element(a,numsignificant5)

a<-table.element(a,numsignificant5/numgqtests)

if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'10% type I error level',header=TRUE)

a<-table.element(a,numsignificant10)

a<-table.element(a,numsignificant10/numgqtests)

if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable6.tab')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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