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Data X:
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
Names of X columns:
SRS 4YrRecruitAvg lySRS RetStart lyYPPdiff
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, mywarning) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,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$sigma,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') if(n < 200) { a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(x[i]-mysum$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') } }
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