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• QR Code (size=3)
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• QR Code (size=6)

0.36	105.43	2.2	11	-0.3
-0.37	85.27	-9.23	12	-0.3
23.72	314.56	20.34	11	1.8
5.05	87.46	-6.56	20	0.9
1.27	195.26	11.9	12	0.0
4.91	212.21	13.26	16	0.3
12.79	217.77	14.91	15	0.7
0.74	130.73	-3.96	14	0.4
-11.82	71.85	-12.95	9	-0.9
7.16	269.85	14.99	12	1.0
-10.66	107.35	-8.35	18	-0.7
13.08	209.47	14.97	17	1.3
6.03	167.70	10.58	16	1.3
-1.73	152.65	6.04	9	0.6
8.09	117.22	-6.66	14	-0.5
7.51	155.51	4.38	14	0.8
-5.4	90.35	-9.34	11	0.1
9.54	192.45	2.55	17	-0.2
-18.85	143.11	1.98	9	0.5
0.59	109.68	-5.84	12	0.4
1.19	166.67	2.42	12	0.3
18.88	253.33	11.63	11	1.4
-3.51	163.66	-3.63	16	-1.2
-5.09	105.27	5.47	13	1.4
-3.45	130.23	-15.72	14	-0.7
2.94	163.24	6.64	12	0.1
-1.27	137.29	2.35	11	1.4
-18.16	95.87	-19.82	13	-2.2
9.65	268.54	8.12	10	0.6
-11.49	128.07	-9.68	12	-0.8
-8.79	115.78	-6.95	16	-1.1
13.59	280.66	14.48	11	0.9
-12.12	126.88	-5.52	12	-1.1
8.98	271.65	18.84	12	2.0
5.27	76.02	1.89	12	1.3
-7.61	71.94	-20.41	17	-1.2
1.43	170.41	15.92	13	0.4
-14.63	115.71	-9.08	13	-0.9
12.32	153.88	0.85	13	0.8
-13.24	86.94	-19.67	13	-1.7
1.18	168.01	-2.53	14	-0.6
2.26	179.02	-5.32	12	-0.1
10.8	176.14	1.37	13	0.2
-2.14	162.12	-8.64	15	-1.6
-15.22	158.13	-6.86	8	-1.7
1.83	162.17	12.73	12	0.7
-11.29	100.31	-13.04	16	-1.0
-4.09	199.50	1.55	14	-0.1
15.04	277.45	10.75	15	0.7
1.83	125.14	5.46	12	1.0
-12.46	121.18	-1.57	11	0.0
-16.72	90.79	-9.82	14	-0.6
6.69	192.63	10.52	9	0.7
1.84	155.01	12.55	11	2.9
-2.08	191.35	1.58	10	-0.2
-10.76	87.26	-12.61	19	0.0
6.55	129.68	7.22	11	0.8
5.69	246.73	8.04	11	1.9
-12.02	113.94	-13.99	12	-0.5
16.34	248.33	1.82	17	0.5
15.36	210.28	16.07	13	1.4
-2.71	115.63	-3.52	13	0.0
2.46	163.59	6.74	13	0.0
14.42	237.51	16.8	16	1.3
13.59	218.64	15.04	9	1.1
0.2	202.67	12.02	13	0.6
11.14	95.77	2.19	11	0.6
6.56	210.17	10.26	12	0.8
-6.26	125.72	-0.38	12	-0.5
-10.42	108.12	-16.18	11	-1.4
-6.61	114.78	-7.8	14	-0.3
-15.79	97.08	-18.48	17	-1.1
12.73	206.74	0.14	17	-0.9
5.06	187.36	4.17	14	0.8
-20.99	109.12	-11.91	12	-0.6
1.55	113.85	-1.06	14	0.3
7.3	176.89	0.8	13	-0.8
14.33	263.99	8.56	19	0.5
-0.46	109.70	-9.79	15	-0.3
20.73	289.70	20.44	15	2.0
17.84	247.85	10.21	13	1.3
8.5	204.71	1.1	16	-0.4
-15.4	95.63	-6.1	13	0.3
11.05	238.13	22.22	12	1.8
-7.66	177.93	-1.42	11	-0.2
6.46	214.23	2.46	15	0.3
6.32	189.69	2.74	15	0.6
-5.92	166.41	-6.33	15	-0.7
-11.63	119.08	-0.28	10	-0.1
-3.57	185.25	3.19	10	-0.4
6.26	148.86	-2.1	14	1.0
-4.98	119.51	-9.49	15	-0.3
-11.31	103.15	-7.63	5	-0.4
-2.18	236.28	6.7	12	-0.1
5.88	176.61	-10.9	12	-0.8
11.53	274.79	14.39	14	0.8
-12.22	141.73	-18.17	15	-2.5
2.13	135.65	-10.91	13	-1.3
18.81	229.36	12.42	13	1.7
-1.51	164.01	-4.84	11	-0.1
6.45	143.27	-2.23	19	0.0
13.94	253.27	8.13	18	-0.4
1.09	260.72	3.98	13	0.2
9.4	265.43	9.96	15	0.4
11.39	196.17	18.96	14	2.0
-14.24	113.10	-6.43	12	0.4
3.04	197.78	-3.56	17	0.3
-15.45	80.84	-3.18	12	-1.0
-14.27	92.73	-8.62	5	-0.7
10.16	132.36	-1.79	13	0.8
-7.26	102.78	-14.67	13	-1.1
-13.59	126.54	-11.38	15	-0.8
-4.74	124.34	-15.23	17	-1.7
8.73	256.97	14.4	18	0.9
11.39	181.67	9.84	14	0.0
-1.23	115.24	3.63	15	1.1
-2.27	191.52	-6.97	18	-1.0
-0.95	201.67	4.83	10	0.0
5.04	219.78	6.71	16	-0.2
-3.7	159.37	-7.64	16	-1.8
9.73	217.20	6.52	10	0.0
6.55	175.97	-1.54	14	-0.1
7.59	199.34	8.07	14	0.5
9.62	115.28	1.99	16	0.3
4.77	128.92	-1.79	16	1.0
10.85	185.98	12.7	13	1.9
-17.03	98.16	-6.69	10	-0.9
2.2	83.02	-14.88	15	-1.0
-9.23	68.33	-9.86	13	-0.6
20.34	311.91	20.06	12	2.3
-0.3	86.84	-15.82	14	-1.4
11.9	190.38	12.6	12	0.5
13.26	190.27	17.5	10	0.3
14.91	218.75	-1.32	15	-0.6
-3.96	103.08	-1.86	12	-0.2
-12.95	47.56	-12.36	17	-1.0
14.99	270.74	18.8	14	0.9
-8.35	89.26	2.29	12	0.9
14.97	203.11	16.87	9	2.7
10.58	164.82	3.88	14	0.7
6.04	156.13	1.28	10	0.1
-6.66	98.96	4.89	13	1.4
4.38	150.20	10.23	13	0.6
-9.34	72.56	-1.48	10	-0.3
2.55	204.97	-8.58	14	-1.9
1.98	143.22	8.42	15	1.2
-5.84	89.13	-11.97	19	-0.5
2.42	167.71	0.41	13	1.4
11.63	251.31	14.98	12	1.4
-3.63	160.06	-1.99	15	-1.1
5.47	109.84	-0.23	13	0.5
-15.72	127.69	-11.97	14	-0.9
6.64	156.61	7.86	14	0.2
2.35	140.09	4.3	9	1.0
-19.82	77.36	-22.84	15	-2.4
8.12	276.42	4.67	14	-0.5
-9.68	103.32	-3.53	10	0.5
-6.95	99.52	-23.53	18	-3.0
14.48	283.34	23.36	13	3.6
-5.52	96.32	5.04	13	0.9
18.84	270.99	12.82	15	1.0
-20.41	48.64	-20.2	10	-0.9
15.92	167.69	7.21	11	0.6
-9.08	102.86	-11.13	13	-0.7
0.85	155.24	4.93	17	0.5
-19.67	67.11	-19.3	15	-2.1
-2.53	168.34	-3.86	16	-0.8
-5.32	174.54	4.41	18	-0.1
1.37	186.17	7.31	13	0.7
-8.64	159.59	-3.57	16	-1.3
-6.86	164.99	-8.58	16	-1.5
12.73	159.79	10.01	10	1.2
-13.04	73.01	-9.6	14	-0.5
1.55	197.10	-3.51	16	-1.0
10.75	275.37	14.59	12	1.8
5.46	93.69	-15.66	13	-0.2
-1.57	107.49	-2.01	17	0.3
-9.82	66.96	-6.81	13	-0.8
10.52	194.42	9.99	13	2.5
12.55	155.70	3.07	13	1.5
1.58	186.49	0.64	17	0.7
-12.61	57.22	-20.4	12	-2.3
7.22	123.34	-7.41	17	-0.6
8.04	242.36	6.59	13	1.0
-13.99	82.18	-23.48	15	-2.7
1.82	252.35	5.53	15	0.1
16.07	204.42	14.74	11	1.4
-3.52	93.72	-3.71	11	0.4
6.74	161.75	5.27	14	-0.5
16.8	232.98	10.42	15	0.7
15.04	207.30	7.72	16	0.4
12.02	187.78	19.97	9	1.2
2.19	72.99	4.26	15	0.1
10.26	218.53	5.22	11	0.4
-0.38	97.73	-3.6	18	-1.8
-16.18	85.04	-3.9	15	-0.3
-7.8	97.62	-14.1	14	-0.9
-18.48	74.57	-19.36	11	-2.3
0.14	211.04	6.22	15	0.6
4.17	169.67	-5.66	15	-0.8
-11.91	83.29	3.05	9	0.5
-1.06	89.14	3.56	15	1.4
0.8	170.72	1.2	17	-0.1
8.56	264.70	9.5	11	1.0
-9.79	81.07	-5.77	11	-0.1
20.44	289.77	15.65	12	1.8
10.21	249.34	13.19	14	0.4
1.1	210.07	16.83	9	1.1
22.22	241.14	21.27	15	2.9
-1.42	179.88	8.11	14	0.1
2.46	202.03	3.4	15	0.4
2.74	181.17	3.72	14	0.0
-6.33	157.63	-12.43	13	-1.6
-0.28	94.00	0.15	12	0.1
3.19	193.96	-5.68	16	-0.4
-2.1	137.42	-4.68	11	0.4
-9.49	79.51	-3.28	14	0.3
-7.63	76.29	-2.44	14	0.2
6.7	238.05	16.06	14	0.9
-10.9	171.42	-13.56	14	-1.1
14.39	269.46	13.72	14	1.1
-18.17	141.93	-7.44	14	-0.1
-10.91	117.37	-21.13	15	-1.3
12.42	229.70	18.96	11	1.4
-4.84	161.16	2.81	15	-0.2
-2.23	131.09	-10.3	13	-0.7
8.13	245.30	4.29	10	-0.8
3.98	263.66	7.45	13	-0.2
9.96	246.79	13.38	15	0.9
18.96	201.16	1.94	15	0.2
-6.43	84.70	-10.53	11	-0.3
-3.56	205.61	5.32	12	0.4
-3.18	63.51	-18.17	16	-2.3
-8.62	62.59	-3.01	19	0.6
-1.79	131.37	-0.03	16	0.6
-14.67	80.17	-5.33	10	-0.7
-11.38	122.07	-3.22	13	-0.3
-15.23	124.11	-11.77	16	-0.8
14.4	232.51	17.1	16	1.0
9.84	181.38	6.96	11	0.1
3.63	79.77	6.7	7	0.8
-6.97	186.64	8.01	10	0.3
4.83	212.04	-4.94	15	-1.3
6.71	214.32	6.41	12	0.5
-7.64	151.13	0	10	-0.6
6.52	218.21	15.12	12	1.3
-1.54	164.89	5.09	14	-0.4
8.07	191.19	-3.74	14	-0.4
1.99	102.85	-0.52	11	1.0
-1.79	115.25	-19.16	12	-1.1
12.7	184.54	14.2	9	2.2
-6.69	75.90	-9.36	16	0.2
-14.88	64.56	-7.35	12	-0.1
-9.86	73.21	-15.31	14	-0.6
20.06	302.35	24.51	13	2.7
-15.82	95.16	-11.66	12	-0.6
12.6	182.14	9.38	17	0.3
17.5	183.88	9	14	1.2
-1.32	212.88	2.4	11	0.4
-1.86	106.70	4.94	12	1.0
-12.36	51.07	-14.23	17	-1.0
18.8	270.00	-0.92	15	-0.7
2.29	93.67	-1.6	14	-0.6
16.87	197.19	11.22	13	0.9
3.88	151.05	7.85	13	1.2
1.28	157.62	-6.74	14	-0.5
4.89	106.16	-0.27	19	0.6
10.23	161.53	8.77	14	0.8
-1.48	72.98	-8.78	16	0.0
-8.58	217.44	-0.65	9	0.0
8.42	145.14	6.65	11	1.0
-11.97	95.55	-7.91	14	0.1
0.41	170.14	8.71	14	1.3
14.98	243.53	14.75	13	0.7
-1.99	165.56	-12.66	17	-2.7
-0.23	115.01	-12.54	16	-0.1
-11.97	138.08	-5.35	13	0.1
7.86	154.00	-0.28	14	-1.3
4.3	138.09	-4.23	15	-0.3
-22.84	81.63	-16.61	13	-1.4
4.67	290.40	17.35	11	0.9
-3.53	89.91	-9.83	12	-1.1
-23.53	106.75	-9.1	8	-0.2
23.36	277.99	13.56	10	3.1
5.04	102.97	6.33	13	1.5
12.82	265.42	17.93	13	1.9
7.21	172.40	5.18	16	0.5
-11.13	108.80	-13.75	18	-1.1
4.93	160.76	-5.54	14	0.2
-19.3	81.68	-17.38	11	-1.9
-3.86	171.91	-10.35	12	-1.3
4.41	167.56	-5.53	19	-0.5
7.31	192.48	-2.62	14	-0.8
-3.57	157.46	5.4	9	-0.6
-8.58	162.69	-6.7	11	-1.8
10.01	152.84	16.92	8	0.8
-9.6	85.36	2.75	11	0.2
-3.51	185.47	-5.29	13	-0.7
14.59	266.47	14.76	13	1.0
-15.66	95.75	6.33	6	0.1
-2.01	103.02	3.45	13	0.9
-6.81	66.77	-0.03	17	-0.2
9.99	194.64	5.88	15	0.6
3.07	151.05	-6.04	15	0.2
0.64	187.41	-6.59	12	-0.6
-20.4	34.67	-20.97	9	-2.5
-7.41	131.23	-11.09	13	-0.5
6.59	238.91	4.07	19	0.4
-23.48	91.94	-11.7	13	-0.8
5.53	251.99	10.01	12	1.2
14.74	205.17	6.21	15	0.5
-3.71	85.67	-2.45	17	-0.3
5.27	161.06	-3.25	16	-0.4
10.42	227.24	8.43	18	0.3
7.72	208.26	6.15	13	0.4
19.97	193.73	5.54	12	-0.6
4.26	58.43	-1.76	11	-0.4
5.22	221.86	9.79	13	1.0
-3.6	98.29	-3.6	11	0.3
-3.9	93.79	-14.57	18	-0.8
-14.1	105.73	-9.91	11	-1.1
-19.36	82.84	-19	14	-1.6
6.22	212.09	4.52	15	1.3
-5.66	168.92	1.46	11	-0.2
3.05	77.61	-8.41	16	-0.6
3.56	90.76	4.64	12	1.7
1.2	165.57	8.97	15	0.1
9.5	257.70	16.98	14	1.2
-5.77	78.80	-2.75	11	0.4
15.65	274.16	13.81	13	1.0
13.19	257.67	14.32	11	0.7
16.83	207.19	13.5	14	1.7
21.27	243.87	23.43	15	1.7
8.11	180.64	12.14	15	0.7
3.4	208.73	9.7	13	0.5
3.72	186.34	2.05	14	0.8
-12.43	161.66	-1.58	13	-0.2
0.15	95.74	-3.52	17	-0.9
-5.68	194.09	3.91	10	0.6
-4.68	138.05	2.69	16	0.6
-3.28	80.57	7.9	13	1.2
-2.44	51.83	-15.26	17	-1.1
16.06	231.51	15.97	12	1.1
-13.56	166.49	-6.13	10	-0.3
13.72	276.71	10.53	15	1.3
-7.44	147.73	1.64	9	-0.2
-21.13	127.29	-18.04	13	-1.2
18.96	220.73	15.58	15	0.8
2.81	156.99	6.27	13	0.3
-10.3	124.55	-7.52	15	-1.2
4.29	242.29	4.07	13	0.3
7.45	281.36	11.18	19	0.4
13.38	234.04	20.05	10	1.9
1.94	196.76	7.49	15	0.6
-10.53	57.30	-8.03	13	-0.6
5.32	207.36	6.33	13	1.1
-18.17	74.34	-9.29	11	-0.7
-3.01	42.15	-7.49	19	0.2
-0.03	133.65	-0.18	13	-0.5
-5.33	92.60	-3.45	7	-0.1
-3.22	122.71	-14.23	17	-1.7
-11.77	128.95	6.24	10	0.6
17.1	238.17	8.75	13	0.4
6.96	187.85	2.51	13	-0.4
6.7	82.73	10.71	15	2.5
8.01	183.71	9.88	13	0.9
-4.94	201.56	-3.84	14	0.2
6.41	211.02	3.13	13	0.4
0	151.10	-6.2	16	-1.4
15.12	226.46	4.9	17	-0.3
5.09	164.06	-6.89	15	-0.8
-3.74	193.76	4.03	9	0.2
-0.52	99.43	-1.5	13	0.7
-19.16	107.94	-9.54	10	0.2
14.2	179.45	9.77	15	1.1
-9.36	86.16	-10.78	14	-0.7

par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
(k <- length(x[n,]))
head(x)
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
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,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}
}