R Under development (unstable) (2025-10-03 r88899 ucrt) -- "Unsuffered Consequences" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library( micEcon ) If you have questions, suggestions, or comments regarding one of the 'micEcon' packages, please use a forum or 'tracker' at micEcon's R-Forge site: https://r-forge.r-project.org/projects/micecon/ > options( digits = 3 ) > > data( germanFarms ) > # output quantity: > germanFarms$qOutput <- germanFarms$vOutput / germanFarms$pOutput > # value of labor input > germanFarms$vLabor <- germanFarms$pLabor + germanFarms$qLabor > # total variable cost > germanFarms$cost <- germanFarms$vLabor + germanFarms$vVarInput > # a time trend to account for technical progress: > germanFarms$time <- c(1:20) > > # non-hom in prices, without land and trend > estResult <- translogCostEst( cName = "cost", yName = "qOutput", + pNames = c( "pLabor", "pVarInput" ), + data = germanFarms, homPrice = FALSE ) > print( estResult ) $call translogCostEst(cName = "cost", yName = "qOutput", pNames = c("pLabor", "pVarInput"), data = germanFarms, homPrice = FALSE) $nExog [1] 3 $nShifter [1] 0 $est Call: lm(formula = as.formula(estFormula), data = estData) Coefficients: (Intercept) a_1 a_2 a_3 b_1_1 b_1_2 -68.146 3.794 -0.875 29.024 3.420 -1.115 b_1_3 b_2_2 b_2_3 b_3_3 -3.692 0.240 1.463 -3.580 $residuals 1 2 3 4 5 6 7 8 -0.000239 0.012568 -0.002784 -0.001256 -0.021106 -0.010217 0.014931 -0.008427 9 10 11 12 13 14 15 16 -0.010933 -0.001578 0.025050 0.003195 0.021669 -0.000570 -0.002251 -0.008776 17 18 19 20 0.014768 -0.015429 -0.025287 0.016674 $fitted 1 2 3 4 5 6 7 8 9 10 11 54236 66628 71556 73500 78960 83453 89674 91221 90653 92566 91374 12 13 14 15 16 17 18 19 20 98244 100254 105557 102113 102960 104056 106912 112949 116738 $coef a_0 a_1 a_2 a_3 b_1_1 b_1_2 b_1_3 b_2_2 b_2_3 b_3_3 -68.146 3.794 -0.875 29.024 3.420 -1.115 -3.692 0.240 1.463 -3.580 $coefCov a_0 a_1 a_2 a_3 b_1_1 b_1_2 b_1_3 b_2_2 b_2_3 a_0 465.81 -7.815 20.841 -234.916 -4.78 3.339 2.2191 -7.1371 5.3510 a_1 -7.81 22.239 -13.504 -2.476 5.31 -3.019 -6.7188 0.1108 7.4934 a_2 20.84 -13.504 11.460 -12.467 -4.33 2.322 4.7954 -0.1684 -5.8252 a_3 -234.92 -2.476 -12.467 132.690 3.02 -1.666 -0.7507 3.2715 -1.5928 b_1_1 -4.78 5.312 -4.334 3.019 4.16 -2.248 -2.8601 0.4098 3.6052 b_1_2 3.34 -3.019 2.322 -1.666 -2.25 1.380 1.2298 -0.3215 -1.9883 b_1_3 2.22 -6.719 4.795 -0.751 -2.86 1.230 3.3150 0.0199 -3.0335 b_2_2 -7.14 0.111 -0.168 3.271 0.41 -0.322 0.0199 0.2928 -0.0839 b_2_3 5.35 7.493 -5.825 -1.593 3.61 -1.988 -3.0335 -0.0839 4.5828 b_3_3 36.20 -5.088 7.730 -24.250 -3.92 2.698 1.5105 -0.5606 -4.7395 b_3_3 a_0 36.199 a_1 -5.088 a_2 7.730 a_3 -24.250 b_1_1 -3.916 b_1_2 2.698 b_1_3 1.510 b_2_2 -0.561 b_2_3 -4.739 b_3_3 13.036 $r2 [1] 0.995 $r2bar [1] 0.99 $nObs [1] 20 $yName [1] "qOutput" $model.matrix (Intercept) a_1 a_2 a_3 b_1_1 b_1_2 b_1_3 b_2_2 b_2_3 b_3_3 1 1 6.85 9.02 4.41 23.5 61.8 30.2 40.7 39.8 9.74 2 1 6.87 9.55 4.48 23.6 65.6 30.8 45.6 42.8 10.03 3 1 6.98 9.82 4.47 24.3 68.5 31.2 48.2 43.9 9.97 4 1 7.07 9.67 4.48 25.0 68.3 31.6 46.8 43.3 10.02 5 1 7.07 9.70 4.54 25.0 68.6 32.1 47.1 44.1 10.33 6 1 7.08 9.74 4.61 25.1 68.9 32.6 47.4 44.8 10.60 7 1 7.12 9.83 4.68 25.4 70.0 33.3 48.4 46.0 10.94 8 1 7.16 9.87 4.70 25.6 70.7 33.6 48.7 46.4 11.04 9 1 7.10 9.82 4.71 25.2 69.7 33.4 48.2 46.3 11.11 10 1 7.15 9.89 4.72 25.6 70.8 33.8 48.9 46.7 11.13 11 1 7.22 9.87 4.69 26.0 71.2 33.8 48.7 46.3 11.00 12 1 7.34 9.96 4.62 26.9 73.1 33.9 49.6 46.0 10.68 13 1 7.36 10.00 4.61 27.1 73.6 33.9 50.0 46.1 10.62 14 1 7.44 9.99 4.65 27.6 74.3 34.6 49.9 46.5 10.83 15 1 7.41 10.02 4.68 27.4 74.2 34.7 50.2 46.9 10.95 16 1 7.39 10.13 4.68 27.3 74.9 34.6 51.3 47.3 10.93 17 1 7.41 10.20 4.72 27.5 75.6 35.0 52.1 48.2 11.14 18 1 7.47 10.20 4.72 27.9 76.2 35.2 52.0 48.1 11.12 19 1 7.54 10.26 4.71 28.4 77.4 35.6 52.6 48.4 11.11 20 1 7.60 10.35 4.73 28.9 78.6 35.9 53.6 49.0 11.19 attr(,"assign") [1] 0 1 2 3 4 5 6 7 8 9 $r2nonLog [,1] [1,] -94794 $cName [1] "cost" $pNames [1] "pLabor" "pVarInput" $dataLogged [1] FALSE $homPrice [1] FALSE attr(,"class") [1] "translogCostEst" > summary( estResult$est ) Call: lm(formula = as.formula(estFormula), data = estData) Residuals: Min 1Q Median 3Q Max -0.02529 -0.00914 -0.00142 0.01312 0.02505 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -68.146 21.583 -3.16 0.01 * a_1 3.794 4.716 0.80 0.44 a_2 -0.875 3.385 -0.26 0.80 a_3 29.024 11.519 2.52 0.03 * b_1_1 3.420 2.039 1.68 0.12 b_1_2 -1.115 1.175 -0.95 0.36 b_1_3 -3.692 1.821 -2.03 0.07 . b_2_2 0.240 0.541 0.44 0.67 b_2_3 1.463 2.141 0.68 0.51 b_3_3 -3.580 3.610 -0.99 0.34 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.0192 on 10 degrees of freedom Multiple R-squared: 0.995, Adjusted R-squared: 0.99 F-statistic: 214 on 9 and 10 DF, p-value: 3.07e-10 > > # non-hom in prices, without land, with trend > estResultTrend <- translogCostEst( cName = "cost", yName = "qOutput", + pNames = c( "pLabor", "pVarInput" ), + shifterNames = "time", data = germanFarms, homPrice = FALSE ) > print( estResultTrend ) $call translogCostEst(cName = "cost", yName = "qOutput", pNames = c("pLabor", "pVarInput"), data = germanFarms, shifterNames = "time", homPrice = FALSE) $nExog [1] 3 $nShifter [1] 1 $est Call: lm(formula = as.formula(estFormula), data = estData) Coefficients: (Intercept) a_1 a_2 a_3 b_1_1 b_1_2 -68.47181 3.84846 -0.86765 29.05835 3.40910 -1.11088 b_1_3 b_2_2 b_2_3 b_3_3 d_1 -3.69547 0.23717 1.46083 -3.57697 -0.00137 $residuals 1 2 3 4 5 6 7 8 -0.000238 0.012622 -0.002796 -0.001310 -0.021103 -0.010197 0.014743 -0.008638 9 10 11 12 13 14 15 16 -0.010794 -0.001516 0.025129 0.003191 0.021719 -0.000597 -0.002218 -0.008694 17 18 19 20 0.014804 -0.015409 -0.025310 0.016612 $fitted 1 2 3 4 5 6 7 8 9 10 11 54236 66625 71557 73504 78960 83451 89691 91240 90641 92560 91366 12 13 14 15 16 17 18 19 20 98244 100249 105560 102110 102951 104052 106910 112951 116746 $coef a_0 a_1 a_2 a_3 b_1_1 b_1_2 b_1_3 b_2_2 -68.47181 3.84846 -0.86765 29.05835 3.40910 -1.11088 -3.69547 0.23717 b_2_3 b_3_3 d_1 1.46083 -3.57697 -0.00137 $coefCov a_0 a_1 a_2 a_3 b_1_1 b_1_2 b_1_3 b_2_2 b_2_3 a_0 845.884 -63.290 16.0864 -295.592 5.390 -0.4484 5.7913 -5.3519 8.3451 a_1 -63.290 33.791 -13.8284 3.000 4.122 -2.6629 -8.0182 -0.3057 7.9266 a_2 16.086 -13.828 12.8846 -13.107 -5.046 2.6699 5.2564 -0.2426 -6.5239 a_3 -295.592 3.000 -13.1067 151.071 2.227 -1.4126 -1.1844 3.3633 -2.0225 b_1_1 5.390 4.122 -5.0463 2.227 4.966 -2.6337 -3.0694 0.5393 4.0839 b_1_2 -0.448 -2.663 2.6699 -1.413 -2.634 1.5861 1.3243 -0.3899 -2.2396 b_1_3 5.791 -8.018 5.2564 -1.184 -3.069 1.3243 3.7169 0.0482 -3.3461 b_2_2 -5.352 -0.306 -0.2426 3.363 0.539 -0.3899 0.0482 0.3456 -0.0744 b_2_3 8.345 7.927 -6.5239 -2.023 4.084 -2.2396 -3.3461 -0.0744 5.1094 b_3_3 36.841 -5.091 8.6617 -26.587 -4.461 3.0401 1.6440 -0.6494 -5.2906 d_1 1.382 -0.230 -0.0297 -0.146 0.045 -0.0175 0.0140 0.0108 0.0101 b_3_3 d_1 a_0 36.8411 1.38151 a_1 -5.0907 -0.22977 a_2 8.6617 -0.02974 a_3 -26.5871 -0.14552 b_1_1 -4.4608 0.04502 b_1_2 3.0401 -0.01750 b_1_3 1.6440 0.01399 b_2_2 -0.6494 0.01085 b_2_3 -5.2906 0.01010 b_3_3 14.5182 -0.01422 d_1 -0.0142 0.00581 $r2 [1] 0.995 $r2bar [1] 0.989 $nObs [1] 20 $yName [1] "qOutput" $shifterNames [1] "time" $model.matrix (Intercept) a_1 a_2 a_3 b_1_1 b_1_2 b_1_3 b_2_2 b_2_3 b_3_3 d_1 1 1 6.85 9.02 4.41 23.5 61.8 30.2 40.7 39.8 9.74 0.000 2 1 6.87 9.55 4.48 23.6 65.6 30.8 45.6 42.8 10.03 0.693 3 1 6.98 9.82 4.47 24.3 68.5 31.2 48.2 43.9 9.97 1.099 4 1 7.07 9.67 4.48 25.0 68.3 31.6 46.8 43.3 10.02 1.386 5 1 7.07 9.70 4.54 25.0 68.6 32.1 47.1 44.1 10.33 1.609 6 1 7.08 9.74 4.61 25.1 68.9 32.6 47.4 44.8 10.60 1.792 7 1 7.12 9.83 4.68 25.4 70.0 33.3 48.4 46.0 10.94 1.946 8 1 7.16 9.87 4.70 25.6 70.7 33.6 48.7 46.4 11.04 2.079 9 1 7.10 9.82 4.71 25.2 69.7 33.4 48.2 46.3 11.11 2.197 10 1 7.15 9.89 4.72 25.6 70.8 33.8 48.9 46.7 11.13 2.303 11 1 7.22 9.87 4.69 26.0 71.2 33.8 48.7 46.3 11.00 2.398 12 1 7.34 9.96 4.62 26.9 73.1 33.9 49.6 46.0 10.68 2.485 13 1 7.36 10.00 4.61 27.1 73.6 33.9 50.0 46.1 10.62 2.565 14 1 7.44 9.99 4.65 27.6 74.3 34.6 49.9 46.5 10.83 2.639 15 1 7.41 10.02 4.68 27.4 74.2 34.7 50.2 46.9 10.95 2.708 16 1 7.39 10.13 4.68 27.3 74.9 34.6 51.3 47.3 10.93 2.773 17 1 7.41 10.20 4.72 27.5 75.6 35.0 52.1 48.2 11.14 2.833 18 1 7.47 10.20 4.72 27.9 76.2 35.2 52.0 48.1 11.12 2.890 19 1 7.54 10.26 4.71 28.4 77.4 35.6 52.6 48.4 11.11 2.944 20 1 7.60 10.35 4.73 28.9 78.6 35.9 53.6 49.0 11.19 2.996 attr(,"assign") [1] 0 1 2 3 4 5 6 7 8 9 10 $r2nonLog [,1] [1,] -94794 $cName [1] "cost" $pNames [1] "pLabor" "pVarInput" $dataLogged [1] FALSE $homPrice [1] FALSE attr(,"class") [1] "translogCostEst" > summary( estResultTrend$est ) Call: lm(formula = as.formula(estFormula), data = estData) Residuals: Min 1Q Median 3Q Max -0.02531 -0.00907 -0.00141 0.01315 0.02513 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -68.47181 29.08408 -2.35 0.043 * a_1 3.84846 5.81304 0.66 0.525 a_2 -0.86765 3.58951 -0.24 0.814 a_3 29.05835 12.29110 2.36 0.042 * b_1_1 3.40910 2.22850 1.53 0.160 b_1_2 -1.11088 1.25942 -0.88 0.401 b_1_3 -3.69547 1.92793 -1.92 0.088 . b_2_2 0.23717 0.58787 0.40 0.696 b_2_3 1.46083 2.26039 0.65 0.534 b_3_3 -3.57697 3.81027 -0.94 0.372 d_1 -0.00137 0.07624 -0.02 0.986 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.0203 on 9 degrees of freedom Multiple R-squared: 0.995, Adjusted R-squared: 0.989 F-statistic: 174 on 10 and 9 DF, p-value: 4.74e-09 > > # non-hom in prices, with land, without trend > estResultLand <- translogCostEst( cName = "cost", yName = "qOutput", + pNames = c( "pLabor", "pVarInput" ), fNames = "land", + data = germanFarms, homPrice = FALSE ) > print( estResultLand ) $call translogCostEst(cName = "cost", yName = "qOutput", pNames = c("pLabor", "pVarInput"), data = germanFarms, fNames = "land", homPrice = FALSE) $nExog [1] 4 $nShifter [1] 0 $est Call: lm(formula = as.formula(estFormula), data = estData) Coefficients: (Intercept) a_1 a_2 a_3 a_4 b_1_1 -82.798 4.376 6.464 16.081 4.059 12.153 b_1_2 b_1_3 b_1_4 b_2_2 b_2_3 b_2_4 -7.580 1.417 -6.987 0.579 1.708 10.388 b_3_3 b_3_4 b_4_4 -1.689 -10.554 -2.161 $residuals 1 2 3 4 5 6 7 8 0.000091 -0.000138 -0.000155 0.004541 -0.009732 -0.001842 0.024742 -0.007198 9 10 11 12 13 14 15 16 -0.014700 0.006169 0.002493 -0.004934 0.005583 -0.002369 0.006991 -0.001629 17 18 19 20 -0.003897 -0.003716 -0.011691 0.011390 $fitted 1 2 3 4 5 6 7 8 9 10 11 54218 67480 71368 73075 78067 82757 88799 91108 90995 91851 93458 12 13 14 15 16 17 18 19 20 99046 101879 105747 101173 102227 106016 105667 111423 117357 $coef a_0 a_1 a_2 a_3 a_4 b_1_1 b_1_2 b_1_3 b_1_4 b_2_2 -82.798 4.376 6.464 16.081 4.059 12.153 -7.580 1.417 -6.987 0.579 b_2_3 b_2_4 b_3_3 b_3_4 b_4_4 1.708 10.388 -1.689 -10.554 -2.161 $coefCov a_0 a_1 a_2 a_3 a_4 b_1_1 b_1_2 b_1_3 b_1_4 b_2_2 a_0 1852.4 269.6 18.08 -851.9 -570.8 -302.8 -70.52 220.9 478.6 -40.08 a_1 269.6 371.5 76.44 -505.5 -484.7 -182.1 -82.19 163.8 298.5 -17.89 a_2 18.1 76.4 46.43 -124.5 -139.2 -37.5 -20.04 41.3 60.4 -3.41 a_3 -851.9 -505.5 -124.47 959.1 635.0 311.8 121.47 -290.0 -480.5 34.88 a_4 -570.8 -484.7 -139.24 635.0 917.9 251.6 109.75 -204.0 -441.5 24.21 b_1_1 -302.8 -182.1 -37.54 311.8 251.6 148.4 27.76 -104.2 -204.0 14.28 b_1_2 -70.5 -82.2 -20.04 121.5 109.7 27.8 27.89 -41.5 -60.4 3.24 b_1_3 220.9 163.8 41.27 -290.0 -204.0 -104.2 -41.53 101.1 159.0 -10.73 b_1_4 478.6 298.5 60.36 -480.5 -441.5 -204.0 -60.44 159.0 310.4 -20.26 b_2_2 -40.1 -17.9 -3.41 34.9 24.2 14.3 3.24 -10.7 -20.3 1.65 b_2_3 86.1 53.0 -1.13 -85.2 -44.0 -23.7 -15.84 27.4 44.3 -3.06 b_2_4 146.9 133.8 40.66 -209.6 -205.3 -58.0 -41.84 71.0 110.8 -6.60 b_3_3 -72.2 -57.2 9.03 78.5 30.7 28.2 18.31 -34.3 -50.5 3.09 b_3_4 -378.1 -280.8 -60.98 484.2 338.6 163.1 75.14 -165.6 -261.5 17.57 b_4_4 -777.9 -505.9 -124.07 796.4 817.8 310.6 117.40 -262.7 -503.9 31.69 b_2_3 b_2_4 b_3_3 b_3_4 b_4_4 a_0 86.06 146.9 -72.21 -378.1 -777.9 a_1 52.96 133.8 -57.18 -280.8 -505.9 a_2 -1.13 40.7 9.03 -61.0 -124.1 a_3 -85.22 -209.6 78.47 484.2 796.4 a_4 -44.03 -205.3 30.71 338.6 817.8 b_1_1 -23.67 -58.0 28.21 163.1 310.6 b_1_2 -15.84 -41.8 18.31 75.1 117.4 b_1_3 27.36 71.0 -34.30 -165.6 -262.7 b_1_4 44.30 110.8 -50.51 -261.5 -503.9 b_2_2 -3.06 -6.6 3.09 17.6 31.7 b_2_3 17.26 19.8 -23.41 -52.5 -68.5 b_2_4 19.81 70.1 -19.13 -123.0 -214.8 b_3_3 -23.41 -19.1 40.95 63.5 68.7 b_3_4 -52.52 -123.0 63.54 281.2 437.6 b_4_4 -68.46 -214.8 68.66 437.6 873.3 $r2 [1] 0.998 $r2bar [1] 0.992 $nObs [1] 20 $yName [1] "qOutput" $model.matrix (Intercept) a_1 a_2 a_3 a_4 b_1_1 b_1_2 b_1_3 b_1_4 b_2_2 b_2_3 b_2_4 1 1 6.85 9.02 4.41 3.15 23.5 61.8 30.2 21.6 40.7 39.8 28.4 2 1 6.87 9.55 4.48 3.19 23.6 65.6 30.8 21.9 45.6 42.8 30.4 3 1 6.98 9.82 4.47 3.21 24.3 68.5 31.2 22.4 48.2 43.9 31.5 4 1 7.07 9.67 4.48 3.22 25.0 68.3 31.6 22.7 46.8 43.3 31.1 5 1 7.07 9.70 4.54 3.22 25.0 68.6 32.1 22.7 47.1 44.1 31.2 6 1 7.08 9.74 4.61 3.23 25.1 68.9 32.6 22.8 47.4 44.8 31.4 7 1 7.12 9.83 4.68 3.25 25.4 70.0 33.3 23.2 48.4 46.0 32.0 8 1 7.16 9.87 4.70 3.26 25.6 70.7 33.6 23.3 48.7 46.4 32.1 9 1 7.10 9.82 4.71 3.23 25.2 69.7 33.4 22.9 48.2 46.3 31.7 10 1 7.15 9.89 4.72 3.26 25.6 70.8 33.8 23.3 48.9 46.7 32.3 11 1 7.22 9.87 4.69 3.29 26.0 71.2 33.8 23.7 48.7 46.3 32.5 12 1 7.34 9.96 4.62 3.40 26.9 73.1 33.9 24.9 49.6 46.0 33.8 13 1 7.36 10.00 4.61 3.44 27.1 73.6 33.9 25.3 50.0 46.1 34.4 14 1 7.44 9.99 4.65 3.47 27.6 74.3 34.6 25.8 49.9 46.5 34.7 15 1 7.41 10.02 4.68 3.45 27.4 74.2 34.7 25.6 50.2 46.9 34.6 16 1 7.39 10.13 4.68 3.46 27.3 74.9 34.6 25.6 51.3 47.3 35.1 17 1 7.41 10.20 4.72 3.51 27.5 75.6 35.0 26.0 52.1 48.2 35.8 18 1 7.47 10.20 4.72 3.56 27.9 76.2 35.2 26.6 52.0 48.1 36.3 19 1 7.54 10.26 4.71 3.62 28.4 77.4 35.6 27.3 52.6 48.4 37.1 20 1 7.60 10.35 4.73 3.67 28.9 78.6 35.9 27.9 53.6 49.0 38.0 b_3_3 b_3_4 b_4_4 1 9.74 13.9 4.97 2 10.03 14.3 5.08 3 9.97 14.3 5.14 4 10.02 14.4 5.17 5 10.33 14.6 5.17 6 10.60 14.9 5.21 7 10.94 15.2 5.30 8 11.04 15.3 5.30 9 11.11 15.2 5.20 10 11.13 15.4 5.32 11 11.00 15.4 5.40 12 10.68 15.7 5.77 13 10.62 15.8 5.91 14 10.83 16.2 6.03 15 10.95 16.1 5.95 16 10.93 16.2 5.99 17 11.14 16.6 6.17 18 11.12 16.8 6.33 19 11.11 17.1 6.55 20 11.19 17.4 6.75 attr(,"assign") [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 $r2nonLog [,1] [1,] -94817 $cName [1] "cost" $pNames [1] "pLabor" "pVarInput" $fNames [1] "land" $dataLogged [1] FALSE $homPrice [1] FALSE attr(,"class") [1] "translogCostEst" > summary( estResultLand$est ) Call: lm(formula = as.formula(estFormula), data = estData) Residuals: 1 2 3 4 5 6 7 8 0.000091 -0.000138 -0.000155 0.004541 -0.009732 -0.001842 0.024742 -0.007198 9 10 11 12 13 14 15 16 -0.014700 0.006169 0.002493 -0.004934 0.005583 -0.002369 0.006991 -0.001629 17 18 19 20 -0.003897 -0.003716 -0.011691 0.011390 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -82.798 43.040 -1.92 0.11 a_1 4.376 19.274 0.23 0.83 a_2 6.464 6.814 0.95 0.39 a_3 16.081 30.969 0.52 0.63 a_4 4.059 30.296 0.13 0.90 b_1_1 12.153 12.182 1.00 0.36 b_1_2 -7.580 5.281 -1.44 0.21 b_1_3 1.417 10.056 0.14 0.89 b_1_4 -6.987 17.618 -0.40 0.71 b_2_2 0.579 1.283 0.45 0.67 b_2_3 1.708 4.154 0.41 0.70 b_2_4 10.388 8.373 1.24 0.27 b_3_3 -1.689 6.399 -0.26 0.80 b_3_4 -10.554 16.770 -0.63 0.56 b_4_4 -2.161 29.552 -0.07 0.94 Residual standard error: 0.017 on 5 degrees of freedom Multiple R-squared: 0.998, Adjusted R-squared: 0.992 F-statistic: 176 on 14 and 5 DF, p-value: 9.11e-06 > > # non-hom in prices, with land + trend > estResultLandTrend <- translogCostEst( cName = "cost", yName = "qOutput", + pNames = c( "pLabor", "pVarInput" ), fNames = "land", + shifterNames = "time", data = germanFarms, homPrice = FALSE ) > print( estResultLandTrend ) $call translogCostEst(cName = "cost", yName = "qOutput", pNames = c("pLabor", "pVarInput"), data = germanFarms, fNames = "land", shifterNames = "time", homPrice = FALSE) $nExog [1] 4 $nShifter [1] 1 $est Call: lm(formula = as.formula(estFormula), data = estData) Coefficients: (Intercept) a_1 a_2 a_3 a_4 b_1_1 -92.6836 13.5942 10.7723 7.2136 -10.9818 7.3703 b_1_2 b_1_3 b_1_4 b_2_2 b_2_3 b_2_4 -8.7457 4.5453 -0.2242 -0.0473 1.0261 14.3993 b_3_3 b_3_4 b_4_4 d_1 0.5834 -15.7335 -16.9446 -0.0896 $residuals 1 2 3 4 5 6 7 8 8.17e-05 -1.90e-04 1.16e-04 1.22e-03 -4.16e-03 -2.07e-04 1.47e-02 -1.60e-02 9 10 11 12 13 14 15 16 -1.14e-02 1.41e-02 4.91e-03 -1.81e-03 4.51e-03 -3.07e-03 5.10e-03 -1.78e-03 17 18 19 20 -4.61e-03 -7.19e-04 -1.01e-02 9.32e-03 $fitted 1 2 3 4 5 6 7 8 9 10 11 54219 67484 71349 73318 77633 82622 89695 91915 90696 91126 93233 12 13 14 15 16 17 18 19 20 98737 101989 105821 101365 102242 106092 105351 111246 117600 $coef a_0 a_1 a_2 a_3 a_4 b_1_1 b_1_2 b_1_3 -92.6836 13.5942 10.7723 7.2136 -10.9818 7.3703 -8.7457 4.5453 b_1_4 b_2_2 b_2_3 b_2_4 b_3_3 b_3_4 b_4_4 d_1 -0.2242 -0.0473 1.0261 14.3993 0.5834 -15.7335 -16.9446 -0.0896 $coefCov a_0 a_1 a_2 a_3 a_4 b_1_1 b_1_2 b_1_3 a_0 1863.67 183.28 -18.179 -746.312 -424.918 -251.853 -58.2174 186.695 a_1 183.28 428.34 106.707 -554.541 -582.093 -212.041 -88.0467 181.662 a_2 -18.18 106.71 60.170 -151.648 -188.077 -53.321 -23.4715 50.956 a_3 -746.31 -554.54 -151.648 988.331 722.305 335.393 125.4881 -302.141 a_4 -424.92 -582.09 -188.077 722.305 1071.934 302.127 120.2231 -235.511 b_1_1 -251.85 -212.04 -53.321 335.393 302.127 161.876 31.3658 -112.730 b_1_2 -58.22 -88.05 -23.471 125.488 120.223 31.366 27.9635 -43.003 b_1_3 186.70 181.66 50.956 -302.141 -235.511 -112.730 -43.0026 105.463 b_1_4 404.64 339.27 82.404 -512.333 -509.690 -223.297 -64.7325 170.624 b_2_2 -33.38 -22.03 -5.536 38.192 31.164 16.243 3.7307 -11.958 b_2_3 88.41 45.70 -3.540 -76.935 -33.795 -20.054 -14.5790 24.544 b_2_4 108.23 159.59 53.552 -231.332 -247.922 -71.771 -44.1521 78.768 b_3_3 -88.23 -37.52 16.858 58.659 0.998 18.063 15.4034 -27.056 b_3_4 -321.02 -310.00 -77.301 504.140 390.801 177.608 77.3272 -172.872 b_4_4 -626.26 -600.47 -172.554 875.657 972.426 357.857 127.3313 -291.330 d_1 0.74 -0.69 -0.322 0.663 1.125 0.358 0.0872 -0.234 b_1_4 b_2_2 b_2_3 b_2_4 b_3_3 b_3_4 b_4_4 d_1 a_0 404.638 -33.3804 88.414 108.23 -88.226 -321.021 -626.26 0.7396 a_1 339.268 -22.0309 45.702 159.59 -37.519 -310.004 -600.47 -0.6897 a_2 82.404 -5.5357 -3.540 53.55 16.858 -77.301 -172.55 -0.3224 a_3 -512.333 38.1916 -76.935 -231.33 58.659 504.140 875.66 0.6634 a_4 -509.690 31.1639 -33.795 -247.92 0.998 390.801 972.43 1.1254 b_1_1 -223.297 16.2434 -20.054 -71.77 18.063 177.608 357.86 0.3578 b_1_2 -64.732 3.7307 -14.579 -44.15 15.403 77.327 127.33 0.0872 b_1_3 170.624 -11.9582 24.544 78.77 -27.056 -172.872 -291.33 -0.2341 b_1_4 336.784 -23.0328 38.772 129.25 -35.758 -280.776 -568.25 -0.5060 b_2_2 -23.033 1.9125 -2.591 -8.45 1.784 19.617 38.22 0.0469 b_2_3 38.772 -2.5907 16.989 16.77 -23.814 -47.577 -57.45 0.0510 b_2_4 129.251 -8.4459 16.772 80.88 -10.795 -135.726 -256.20 -0.3001 b_3_3 -35.758 1.7838 -23.814 -10.79 43.706 51.297 37.99 -0.1700 b_3_4 -280.776 19.6167 -47.577 -135.73 51.297 292.956 484.90 0.3876 b_4_4 -568.246 38.2229 -57.447 -256.20 37.994 484.904 1022.67 1.1061 d_1 -0.506 0.0469 0.051 -0.30 -0.170 0.388 1.11 0.0067 $r2 [1] 0.998 $r2bar [1] 0.993 $nObs [1] 20 $yName [1] "qOutput" $shifterNames [1] "time" $model.matrix (Intercept) a_1 a_2 a_3 a_4 b_1_1 b_1_2 b_1_3 b_1_4 b_2_2 b_2_3 b_2_4 1 1 6.85 9.02 4.41 3.15 23.5 61.8 30.2 21.6 40.7 39.8 28.4 2 1 6.87 9.55 4.48 3.19 23.6 65.6 30.8 21.9 45.6 42.8 30.4 3 1 6.98 9.82 4.47 3.21 24.3 68.5 31.2 22.4 48.2 43.9 31.5 4 1 7.07 9.67 4.48 3.22 25.0 68.3 31.6 22.7 46.8 43.3 31.1 5 1 7.07 9.70 4.54 3.22 25.0 68.6 32.1 22.7 47.1 44.1 31.2 6 1 7.08 9.74 4.61 3.23 25.1 68.9 32.6 22.8 47.4 44.8 31.4 7 1 7.12 9.83 4.68 3.25 25.4 70.0 33.3 23.2 48.4 46.0 32.0 8 1 7.16 9.87 4.70 3.26 25.6 70.7 33.6 23.3 48.7 46.4 32.1 9 1 7.10 9.82 4.71 3.23 25.2 69.7 33.4 22.9 48.2 46.3 31.7 10 1 7.15 9.89 4.72 3.26 25.6 70.8 33.8 23.3 48.9 46.7 32.3 11 1 7.22 9.87 4.69 3.29 26.0 71.2 33.8 23.7 48.7 46.3 32.5 12 1 7.34 9.96 4.62 3.40 26.9 73.1 33.9 24.9 49.6 46.0 33.8 13 1 7.36 10.00 4.61 3.44 27.1 73.6 33.9 25.3 50.0 46.1 34.4 14 1 7.44 9.99 4.65 3.47 27.6 74.3 34.6 25.8 49.9 46.5 34.7 15 1 7.41 10.02 4.68 3.45 27.4 74.2 34.7 25.6 50.2 46.9 34.6 16 1 7.39 10.13 4.68 3.46 27.3 74.9 34.6 25.6 51.3 47.3 35.1 17 1 7.41 10.20 4.72 3.51 27.5 75.6 35.0 26.0 52.1 48.2 35.8 18 1 7.47 10.20 4.72 3.56 27.9 76.2 35.2 26.6 52.0 48.1 36.3 19 1 7.54 10.26 4.71 3.62 28.4 77.4 35.6 27.3 52.6 48.4 37.1 20 1 7.60 10.35 4.73 3.67 28.9 78.6 35.9 27.9 53.6 49.0 38.0 b_3_3 b_3_4 b_4_4 d_1 1 9.74 13.9 4.97 0.000 2 10.03 14.3 5.08 0.693 3 9.97 14.3 5.14 1.099 4 10.02 14.4 5.17 1.386 5 10.33 14.6 5.17 1.609 6 10.60 14.9 5.21 1.792 7 10.94 15.2 5.30 1.946 8 11.04 15.3 5.30 2.079 9 11.11 15.2 5.20 2.197 10 11.13 15.4 5.32 2.303 11 11.00 15.4 5.40 2.398 12 10.68 15.7 5.77 2.485 13 10.62 15.8 5.91 2.565 14 10.83 16.2 6.03 2.639 15 10.95 16.1 5.95 2.708 16 10.93 16.2 5.99 2.773 17 11.14 16.6 6.17 2.833 18 11.12 16.8 6.33 2.890 19 11.11 17.1 6.55 2.944 20 11.19 17.4 6.75 2.996 attr(,"assign") [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 $r2nonLog [,1] [1,] -94821 $cName [1] "cost" $pNames [1] "pLabor" "pVarInput" $fNames [1] "land" $dataLogged [1] FALSE $homPrice [1] FALSE attr(,"class") [1] "translogCostEst" > summary( estResultLandTrend$est ) Call: lm(formula = as.formula(estFormula), data = estData) Residuals: 1 2 3 4 5 6 7 8 8.17e-05 -1.90e-04 1.16e-04 1.22e-03 -4.16e-03 -2.07e-04 1.47e-02 -1.60e-02 9 10 11 12 13 14 15 16 -1.14e-02 1.41e-02 4.91e-03 -1.81e-03 4.51e-03 -3.07e-03 5.10e-03 -1.78e-03 17 18 19 20 -4.61e-03 -7.19e-04 -1.01e-02 9.32e-03 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -92.6836 43.1702 -2.15 0.098 . a_1 13.5942 20.6965 0.66 0.547 a_2 10.7723 7.7569 1.39 0.237 a_3 7.2136 31.4377 0.23 0.830 a_4 -10.9818 32.7404 -0.34 0.754 b_1_1 7.3703 12.7231 0.58 0.593 b_1_2 -8.7457 5.2881 -1.65 0.174 b_1_3 4.5453 10.2695 0.44 0.681 b_1_4 -0.2242 18.3517 -0.01 0.991 b_2_2 -0.0473 1.3829 -0.03 0.974 b_2_3 1.0261 4.1218 0.25 0.816 b_2_4 14.3993 8.9934 1.60 0.185 b_3_3 0.5834 6.6111 0.09 0.934 b_3_4 -15.7335 17.1159 -0.92 0.410 b_4_4 -16.9446 31.9792 -0.53 0.624 d_1 -0.0896 0.0819 -1.09 0.335 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.0167 on 4 degrees of freedom Multiple R-squared: 0.998, Adjusted R-squared: 0.993 F-statistic: 171 on 15 and 4 DF, p-value: 7.68e-05 > > proc.time() user system elapsed 0.93 0.21 1.09