Microeconomic Theory and Linear Regression (Part 1)

Mauricio Vargas S. 帕夏

5 years ago

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Introduction

Last week I found a lot of materials I used as teaching assistant.

Among those materials I found some class notes from Arne Henningsen, the author of micEcon R package.

I’ll use that package and some others to show some concepts from Microeconomic Theory.

Packages installation:

#install.packages(c("micEcon","lmtest","bbmle","miscTools"))
library(micEcon)
library(lmtest)
library(stats4) #this is a base package so I don't install this
library(bbmle)
library(miscTools)

From package’s help: “The appleProdFr86 data frame includes cross-sectional production data of 140 French apple producers from the year 1986. These data have been extracted from a panel data set that was used in Ivaldi et al. (1996).”

This data frame contains the following columns:

Variable	Description
vCap	costs of capital (including land)
vLab	costs of labour (including remuneration of unpaid family labour)
vMat	costs of intermediate materials (e.g. seedlings, fertilizer, pesticides, fuel)
qApples	quantity index of produced apples
qOtherOut	quantity index of all other outputs
qOut	quantity index of all outputs (not in the original data set, calculated as $580,000 \times (qApples + qOtherOut)$)
pCap	price index of capital goods
pLab	price index of labour
pMat	price index of materials
pOut	price index of the aggregate output (not in the original data set, artificially generated)
adv	dummy variable indicating the use of an advisory service (not in the original data set, artificially generated)

Now I’ll examine appleProdFr86 dataset:

data("appleProdFr86", package = "micEcon")
data = appleProdFr86
rm(appleProdFr86)
str(data)

'data.frame':   140 obs. of  11 variables:
 $ vCap     : int  219183 130572 81301 34007 38702 122400 89061 92134 65779 94047 ...
 $ vLab     : int  323991 187956 134147 105794 83717 523842 168140 204859 180598 142240 ...
 $ vMat     : int  303155 262017 90592 59833 104159 577468 343768 126547 190622 82357 ...
 $ qApples  : num  1.392 0.864 3.317 0.438 1.831 ...
 $ qOtherOut: num  0.977 1.072 0.405 0.436 0.015 ...
 $ qOut     : num  1374064 1122979 2158518 507389 1070816 ...
 $ pCap     : num  2.608 3.292 2.194 1.602 0.866 ...
 $ pLab     : num  0.9 0.753 0.956 1.268 0.938 ...
 $ pMat     : num  8.89 6.42 3.74 3.17 7.22 ...
 $ pOut     : num  0.66 0.716 0.937 0.597 0.825 ...
 $ adv      : num  0 0 1 1 1 1 1 0 0 0 ...

Linear Production Function

A linear production function is of the form:

$$y = \beta_0 + \sum_i \beta_i x_i$$

And from that I’ll estimate the function coefficients and a prediction of the output.

The dataset does not provide units of capital, labour or materials, but it provides information about costs and price index. Then I create the variables for the units from that:

data$qCap = data$vCap/data$pCap
data$qLab = data$vLab/data$pLab
data$qMat = data$vMat/data$pMat

Let $\rho$ be the linear correlation coefficient. The hypothesis testing of Linear Correlation Between Variables is:

$$H_0:\: \rho = 0\\ H_1:\: \rho\neq 0$$

To test this I’ll use the statistic:

$$ t = r \sqrt{\frac{n-2}{1-r^2}} $$

that follows a t-Student distribution with $n-2$ degrees of freedom.

And the correlation test for each input is:

cor.test(data$qOut, data$qCap, alternative="greater")

    Pearson's product-moment correlation

data:  data$qOut and data$qCap
t = 8.7546, df = 138, p-value = 3.248e-15
alternative hypothesis: true correlation is greater than 0
95 percent confidence interval:
 0.4996247 1.0000000
sample estimates:
      cor 
0.5975547 

cor.test(data$qOut, data$qLab, alternative="greater")

    Pearson's product-moment correlation

data:  data$qOut and data$qLab
t = 20.203, df = 138, p-value < 2.2e-16
alternative hypothesis: true correlation is greater than 0
95 percent confidence interval:
 0.8243685 1.0000000
sample estimates:
      cor 
0.8644853 

cor.test(data$qOut, data$qMat, alternative="greater")

    Pearson's product-moment correlation

data:  data$qOut and data$qMat
t = 15.792, df = 138, p-value < 2.2e-16
alternative hypothesis: true correlation is greater than 0
95 percent confidence interval:
 0.7463428 1.0000000
sample estimates:
      cor 
0.8023509

With the units of inputs I write the production function and estimate its coefficients:

prodLin = lm(qOut ~ qCap + qLab + qMat, data = data)
data$qOutLin = fitted(prodLin)
summary(prodLin)

Call:
lm(formula = qOut ~ qCap + qLab + qMat, data = data)

Residuals:
     Min       1Q   Median       3Q      Max 
-3888955  -773002    86119   769073  7091521 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.616e+06  2.318e+05  -6.972 1.23e-10 ***
qCap         1.788e+00  1.995e+00   0.896    0.372    
qLab         1.183e+01  1.272e+00   9.300 3.15e-16 ***
qMat         4.667e+01  1.123e+01   4.154 5.74e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1541000 on 136 degrees of freedom
Multiple R-squared:  0.7868,    Adjusted R-squared:  0.7821 
F-statistic: 167.3 on 3 and 136 DF,  p-value: < 2.2e-16

I can also use Log-Liklihood to obtain the estimates:

LogLikelihood = function(beta0, betacap, betalab, betamat, mu, sigma) {
  R = data$qOut - (beta0 + betacap*data$qCap + betalab*data$qLab + betamat*data$qMat)
  R = suppressWarnings(dnorm(R, mu, sigma, log = TRUE))
  -sum(R)
}

prodLin2 = mle2(LogLikelihood, start = list(beta0 = 0, betacap = 10, betalab = 20, betamat = 30, 
                                            mu = 0, sigma=1), control = list(maxit= 10000))
summary(prodLin)

Call:
lm(formula = qOut ~ qCap + qLab + qMat, data = data)

Residuals:
     Min       1Q   Median       3Q      Max 
-3888955  -773002    86119   769073  7091521 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.616e+06  2.318e+05  -6.972 1.23e-10 ***
qCap         1.788e+00  1.995e+00   0.896    0.372    
qLab         1.183e+01  1.272e+00   9.300 3.15e-16 ***
qMat         4.667e+01  1.123e+01   4.154 5.74e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1541000 on 136 degrees of freedom
Multiple R-squared:  0.7868,    Adjusted R-squared:  0.7821 
F-statistic: 167.3 on 3 and 136 DF,  p-value: < 2.2e-16

Quadratic Production Function

A quadratic production function is of the form:

$$y = \beta_0 + \sum_i \beta_i x_i + \frac{1}{2} \sum_i \sum_j \beta_{ij} x_i x_j$$

And from that I’ll estimate the function coefficients and a prediction of the output.

With the arrangements made for the linear case now I have it ready to obtain the coefficients and the predicted output:

prodQuad = lm(qOut ~ qCap + qLab + qMat + I(0.5*qCap^2) + I(0.5*qLab^2) + I(0.5*qMat^2)
               + I(qCap*qLab) + I(qCap*qMat) + I(qLab*qMat), data = data)
data$qOutQuad = fitted(prodQuad)
summary(prodQuad)

Call:
lm(formula = qOut ~ qCap + qLab + qMat + I(0.5 * qCap^2) + I(0.5 * 
    qLab^2) + I(0.5 * qMat^2) + I(qCap * qLab) + I(qCap * qMat) + 
    I(qLab * qMat), data = data)

Residuals:
     Min       1Q   Median       3Q      Max 
-3928802  -695518  -186123   545509  4474143 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)     -2.911e+05  3.615e+05  -0.805 0.422072    
qCap             5.270e+00  4.403e+00   1.197 0.233532    
qLab             6.077e+00  3.185e+00   1.908 0.058581 .  
qMat             1.430e+01  2.406e+01   0.595 0.553168    
I(0.5 * qCap^2)  5.032e-05  3.699e-05   1.360 0.176039    
I(0.5 * qLab^2) -3.084e-05  2.081e-05  -1.482 0.140671    
I(0.5 * qMat^2) -1.896e-03  8.951e-04  -2.118 0.036106 *  
I(qCap * qLab)  -3.097e-05  1.498e-05  -2.067 0.040763 *  
I(qCap * qMat)  -4.160e-05  1.474e-04  -0.282 0.778206    
I(qLab * qMat)   4.011e-04  1.112e-04   3.608 0.000439 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1344000 on 130 degrees of freedom
Multiple R-squared:  0.8449,    Adjusted R-squared:  0.8342 
F-statistic: 78.68 on 9 and 130 DF,  p-value: < 2.2e-16

Linear Especification vs Quadratic Especification

From the above, which of the models is better? Wald Test, Likelihood Ratio Test and ANOVA can help. In this case I’m working with nested models, because the Linear Especification is contained within the Quadratic Especification:

waldtest(prodLin, prodQuad)

Wald test

Model 1: qOut ~ qCap + qLab + qMat
Model 2: qOut ~ qCap + qLab + qMat + I(0.5 * qCap^2) + I(0.5 * qLab^2) + 
    I(0.5 * qMat^2) + I(qCap * qLab) + I(qCap * qMat) + I(qLab * 
    qMat)
  Res.Df Df      F    Pr(>F)    
1    136                        
2    130  6 8.1133 1.869e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

lrtest(prodLin, prodQuad)

Likelihood ratio test

Model 1: qOut ~ qCap + qLab + qMat
Model 2: qOut ~ qCap + qLab + qMat + I(0.5 * qCap^2) + I(0.5 * qLab^2) + 
    I(0.5 * qMat^2) + I(qCap * qLab) + I(qCap * qMat) + I(qLab * 
    qMat)
  #Df  LogLik Df  Chisq Pr(>Chisq)    
1   5 -2191.3                         
2  11 -2169.1  6 44.529  5.806e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(prodLin, prodQuad, test = "F")

Analysis of Variance Table

Model 1: qOut ~ qCap + qLab + qMat
Model 2: qOut ~ qCap + qLab + qMat + I(0.5 * qCap^2) + I(0.5 * qLab^2) + 
    I(0.5 * qMat^2) + I(qCap * qLab) + I(qCap * qMat) + I(qLab * 
    qMat)
  Res.Df        RSS Df  Sum of Sq      F    Pr(>F)    
1    136 3.2285e+14                                   
2    130 2.3489e+14  6 8.7957e+13 8.1133 1.869e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

From the results the Quadratic Especification should be preferred as the tests show that the additional variables do add information.

Cobb-Douglas Production Function

A Cobb-Douglas production function is of the form:

$$y = A \prod_i x_i^{\beta_i} $$

And from that I’ll estimate the function coefficients and a prediction of the output.

This function is not linear, so I need to convert the function before fitting:

$$\ln(y) = \ln(A) + \sum_{i=1}^n \beta_i \ln(x_i)$$

With the arrangements made for the linear case now I have it ready to obtain the coefficients and the predicted output:

prodCD = lm(log(qOut) ~ log(qCap) + log(qLab) + log(qMat), data = data)
data$qOutCD = fitted(prodCD)
summary(prodCD)

Call:
lm(formula = log(qOut) ~ log(qCap) + log(qLab) + log(qMat), data = data)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.67239 -0.28024  0.00667  0.47834  1.30115 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -2.06377    1.31259  -1.572   0.1182    
log(qCap)    0.16303    0.08721   1.869   0.0637 .  
log(qLab)    0.67622    0.15430   4.383 2.33e-05 ***
log(qMat)    0.62720    0.12587   4.983 1.87e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.656 on 136 degrees of freedom
Multiple R-squared:  0.5943,    Adjusted R-squared:  0.5854 
F-statistic: 66.41 on 3 and 136 DF,  p-value: < 2.2e-16

Translog Production Function

A Translog production function is of the form:

$$\ln y = \beta_0 + \sum_i \alpha_i \ln x_i + \frac{1}{2} \sum_i \sum_j \beta_{ij} \ln x_i \ln x_j$$

And from that I’ll estimate the function coefficients and a prediction of the output.

With the arrangements made for the linear case now I have it ready to obtain the coefficients and the predicted output:

prodTL = lm(log(qOut) ~ log(qCap) + log(qLab) + log(qMat) + I(0.5*log(qCap)^2) 
             + I(0.5*log(qLab)^2) + I(0.5*log(qMat)^2) + I(log(qCap)*log(qLab))
             + I(log(qCap)*log(qMat)) + I(log(qLab)*log(qMat)), data = data)
data$qOutTL = fitted(prodTL)
summary(prodTL)

Call:
lm(formula = log(qOut) ~ log(qCap) + log(qLab) + log(qMat) + 
    I(0.5 * log(qCap)^2) + I(0.5 * log(qLab)^2) + I(0.5 * log(qMat)^2) + 
    I(log(qCap) * log(qLab)) + I(log(qCap) * log(qMat)) + I(log(qLab) * 
    log(qMat)), data = data)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.68015 -0.36688  0.05389  0.44125  1.26560 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)  
(Intercept)              -4.14581   21.35945  -0.194   0.8464  
log(qCap)                -2.30683    2.28829  -1.008   0.3153  
log(qLab)                 1.99328    4.56624   0.437   0.6632  
log(qMat)                 2.23170    3.76334   0.593   0.5542  
I(0.5 * log(qCap)^2)     -0.02573    0.20834  -0.124   0.9019  
I(0.5 * log(qLab)^2)     -1.16364    0.67943  -1.713   0.0892 .
I(0.5 * log(qMat)^2)     -0.50368    0.43498  -1.158   0.2490  
I(log(qCap) * log(qLab))  0.56194    0.29120   1.930   0.0558 .
I(log(qCap) * log(qMat)) -0.40996    0.23534  -1.742   0.0839 .
I(log(qLab) * log(qMat))  0.65793    0.42750   1.539   0.1262  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.6412 on 130 degrees of freedom
Multiple R-squared:  0.6296,    Adjusted R-squared:  0.6039 
F-statistic: 24.55 on 9 and 130 DF,  p-value: < 2.2e-16

Cobb-Douglas Especification vs Translog Especification

This is really similar to the comparison between Linear Especification and Quadratic Especification. In this case the models are nested too and the tests are:

waldtest(prodCD, prodTL)

Wald test

Model 1: log(qOut) ~ log(qCap) + log(qLab) + log(qMat)
Model 2: log(qOut) ~ log(qCap) + log(qLab) + log(qMat) + I(0.5 * log(qCap)^2) + 
    I(0.5 * log(qLab)^2) + I(0.5 * log(qMat)^2) + I(log(qCap) * 
    log(qLab)) + I(log(qCap) * log(qMat)) + I(log(qLab) * log(qMat))
  Res.Df Df     F  Pr(>F)  
1    136                   
2    130  6 2.062 0.06202 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

lrtest(prodCD, prodTL)

Likelihood ratio test

Model 1: log(qOut) ~ log(qCap) + log(qLab) + log(qMat)
Model 2: log(qOut) ~ log(qCap) + log(qLab) + log(qMat) + I(0.5 * log(qCap)^2) + 
    I(0.5 * log(qLab)^2) + I(0.5 * log(qMat)^2) + I(log(qCap) * 
    log(qLab)) + I(log(qCap) * log(qMat)) + I(log(qLab) * log(qMat))
  #Df  LogLik Df  Chisq Pr(>Chisq)  
1   5 -137.61                       
2  11 -131.25  6 12.727    0.04757 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(prodCD, prodTL, test = "F")

Analysis of Variance Table

Model 1: log(qOut) ~ log(qCap) + log(qLab) + log(qMat)
Model 2: log(qOut) ~ log(qCap) + log(qLab) + log(qMat) + I(0.5 * log(qCap)^2) + 
    I(0.5 * log(qLab)^2) + I(0.5 * log(qMat)^2) + I(log(qCap) * 
    log(qLab)) + I(log(qCap) * log(qMat)) + I(log(qLab) * log(qMat))
  Res.Df    RSS Df Sum of Sq     F  Pr(>F)  
1    136 58.534                             
2    130 53.447  6    5.0866 2.062 0.06202 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

From the results the Translog Especification should be preferred as the tests show that the additional variables do add information.

Quadratic Especification vs Translog Especification

The dependent variable is different ($y$ versus $\ln(y)$) and I also cannot compare $R^2$ or use tests for nested models.

What I can do is to calculate the hypothetical value of $y$ by doing a logarithmic transformation to the Quadratic Especification and then I’ll obtain a comparable $R^2$:

summary(prodTL)$r.squared; rSquared(log(data$qOut), log(data$qOut) - log(data$qOutQuad))

[1] 0.6295696

          [,1]
[1,] 0.5481309

$\Longrightarrow$ the Translog Especification has a better fit on $\ln(y)$.

Another option is to apply an exponential transformation to the Translog Especification and then I’ll obtain a comparable $R^2$:

summary(prodQuad)$r.squared; rSquared(data$qOut, data$qOut - exp(data$qOutTL))

[1] 0.8448983

          [,1]
[1,] 0.7696638

$\Longrightarrow$ the Quadratic Especification has a better fit on $y$.

This table resumes this part of the analysis:

	Quadratic	Translog
$R^2$ on $y$	0.85	0.77
$R^2$ on $ln(y)$	0.55	0.63

Here I can compare $R^2$ provided that the number of coefficients is the same. If the models had a different number of coefficients I should use $\bar{R}^2$.

In any case, comparing $R^2$ does not help to choose an especification. An alternative is to verify the theoretical consistency of both especifications.

Microeconomic Concepts

Plot of output versus predicted output after adjusting the scale:

data$qOutTL = exp(fitted(prodTL))
par(mfrow=c(1,2))
compPlot(data$qOut, data$qOutQuad, log = "xy", main = "Quadratic Especification")
compPlot(data$qOut, data$qOutTL, log = "xy", main = "Translog Especification")

Provided that I have some negative coefficients both in Quadratic and Translog Especifications, there can be a negative predicted output:

sum(data$qOutQuad < 0); sum(data$qOutTL < 0)

[1] 0

[1] 0

That problem did appear here.

Another problem that can appear from negative coefficients is a negative marginal effect of an increase in the inputs. Logically, an increase in the inputs should never decrease the output.

To check this I need the Input-Output Elasticity:

$$ \epsilon_i = \frac{\partial \ln (y)}{\partial \ln(x_i)} $$

And the marginal productivity:

$$MP_{i} = \frac{\partial y}{\partial x_i}$$

In the case of a Translog Especification the Input-Output elasticity can be written as:

$$ \epsilon_i = \beta_i + \sum_{j=1}^n \beta_{ij} \ln(x_j) $$

Also to keep in mind with the Translog Especification:

$$ \frac{\partial \ln(y)}{\partial \ln(x_i)} = \frac{\Delta y}{y} \frac{x_i}{\Delta x_i} \: \Longrightarrow \: MP_i = \frac{\partial y}{\partial x_i} = \frac{y}{x_i} \frac{\partial \ln(y)}{\partial \ln(x_i)} = \frac{y}{x_i}\epsilon_i $$

So now I compute the Input-Output Elasticity for the Translog Especification:

b1  = coef(prodTL)["log(qCap)"]
b2  = coef(prodTL)["log(qLab)"]
b3  = coef(prodTL)["log(qMat)"]
b11 = coef(prodTL)["I(0.5 * log(qCap)^2)"]
b22 = coef(prodTL)["I(0.5 * log(qLab)^2)"]
b33 = coef(prodTL)["I(0.5 * log(qMat)^2)"]
b12 = b21 = coef(prodTL)["I(log(qCap) * log(qLab))"]
b13 = b31 = coef(prodTL)["I(log(qCap) * log(qMat))"]
b23 = b32 = coef(prodTL)["I(log(qLab) * log(qMat))"] 

data$eCapTL = with(data, b1 + b11*log(qCap) + b12*log(qLab) + b13*log(qMat))
data$eLabTL = with(data, b2 + b21*log(qCap) + b22*log(qLab) + b23*log(qMat))
data$eMatTL = with(data, b3 + b31*log(qCap) + b32*log(qLab) + b33*log(qMat))

sum(data$eCapTL < 0); sum(data$eLabTL < 0); sum(data$eMatTL < 0)

[1] 32

[1] 14

[1] 8

summary(data$eCapTL); summary(data$eLabTL); summary(data$eMatTL)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-0.46630  0.03239  0.17720  0.15760  0.31440  0.72970 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-1.0260  0.3283  0.6079  0.6550  1.0100  1.9640 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.3775  0.3608  0.6783  0.6379  0.8727  1.9020 

par(mfrow=c(1,3)); hist(data$eCapTL, breaks=20); hist(data$eLabTL, breaks=20); hist(data$eMatTL, breaks=20)

$\Longrightarrow$ The median effect of an increase of 1% in an input has a positive median effect in the output (e.g. an increase of 1% in labour units increases the output in 0.6%), but the red flag here is that there are negative elasticities being them a sign of model misspecification.

And the Marginal Productivity for the Translog Especification:

data$mpCapTL = with(data, eCapTL * qOutTL / qCap)
data$mpLabTL = with(data, eLabTL * qOutTL / qLab)
data$mpMatTL = with(data, eMatTL * qOutTL / qMat)

sum(data$mpCapTL < 0); sum(data$mpLabTL < 0); sum(data$mpMatTL < 0)

[1] 32

[1] 14

[1] 8

summary(data$mpCapTL); summary(data$mpLabTL); summary(data$mpMatTL)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-13.680   0.534   3.766   4.566   7.369  26.840 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 -2.570   2.277   5.337   6.857  10.520  26.830 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 -28.44   28.55   51.83   48.12   70.97  122.90 

par(mfrow=c(1,3)); hist(data$mpCapTL, breaks=20); hist(data$mpLabTL, breaks=20); hist(data$mpMatTL, breaks=20)

$\Longrightarrow$ The effect of increasing the input in one unit has a positive median effect in the output (e.g. an increase of 1 labour unit increases the output in 5.3 units), but the red flag here is that there are negative marginal productivities being them another sign of model misspecification.

Another useful concept is Scale Elasticity:

$$\mathcal{E} = \sum_{i=1}^n \epsilon_i$$

So if I compute the Scale Elasticity for the Translog Especification:

data$eScaleTL = data$eCapTL + data$eLabTL + data$eMatTL

sum(data$eScaleTL < 0)

[1] 0

summary(data$eScaleTL)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.163   1.373   1.440   1.451   1.538   1.738 

hist(data$eScaleTL, breaks=20)

$\Longrightarrow$ at least the Scale Elasticity is always positive, but there are negative Input-Output Elasticities behind this result.

In the case of a Quadratic Especification the Marginal Productivity can be written as:

$$MP_i = \beta_i + \sum_{j=1}^n \beta_{ij} x_j$$

And from that I can obtain the Input-Output Elasticity:

$$\epsilon_i = MP_i \frac{x_i}{y}$$

Now I compute the Marginal Productivity for the Quadratic Especification:

b1  = coef(prodQuad)["qCap"]
b2  = coef(prodQuad)["qLab"]
b3  = coef(prodQuad)["qMat"]
b11 = coef(prodQuad)["I(0.5 * qCap^2)"]
b22 = coef(prodQuad)["I(0.5 * qLab^2)"]
b33 = coef(prodQuad)["I(0.5 * qMat^2)"]
b12 = b21 = coef(prodQuad)["I(qCap * qLab)"]
b13 = b31 = coef(prodQuad)["I(qCap * qMat)"]
b23 = b32 = coef(prodQuad)["I(qLab * qMat)"]

data$mpCapQuad = with(data, b1 + b11*qCap + b12*qLab + b13*qMat)
data$mpLabQuad = with(data, b2 + b21*qCap + b22*qLab + b23*qMat)
data$mpMatQuad = with(data, b3 + b31*qCap + b32*qLab + b33*qMat)

sum(data$mpCapQuad < 0); sum(data$mpLabQuad < 0); sum(data$mpMatQuad < 0)

[1] 28

[1] 5

[1] 8

summary(data$mpCapQuad); summary(data$mpLabQuad); summary(data$mpMatQuad)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-22.3400   0.7743   2.1520   1.7260   4.0270  16.6900 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 -1.599   4.224   6.113   7.031   9.513  30.780 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 -35.57   31.20   47.08   51.57   63.59  260.60 

par(mfrow=c(1,3)); hist(data$mpCapQuad, breaks=20); hist(data$mpLabQuad, breaks=20); hist(data$mpMatQuad, breaks=20)

$\Longrightarrow$ The effect of increasing the input in one unit has a positive median effect in the output (e.g. an increase of 1 labour unit increases the output in 6.1 units), but the red flag here is that there are negative marginal productivities being them another sign of model misspecification.

And the Input-Output Elasticity for the Quadratic Espcification:

data$eCapQuad = with(data, mpCapQuad * qCap / qOutQuad)
data$eLabQuad = with(data, mpLabQuad * qLab / qOutQuad)
data$eMatQuad = with(data, mpMatQuad * qMat / qOutQuad)

sum(data$eCapQuad < 0); sum(data$eLabQuad < 0); sum(data$eMatQuad < 0)

[1] 28

[1] 5

[1] 8

summary(data$eCapQuad); summary(data$eLabQuad); summary(data$eMatQuad)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-0.34470  0.01521  0.07704  0.14240  0.21650  0.94330 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.3157  0.4492  0.6686  0.6835  0.8694  2.3560 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-1.4310  0.3797  0.5288  0.4843  0.6135  1.5110 

par(mfrow=c(1,3)); hist(data$eCapQuad, breaks=20); hist(data$eLabQuad, breaks=20); hist(data$eMatQuad, breaks=20)

$\Longrightarrow$ The median effect of an increase of 1% in an input has a positive median effect in the output (e.g. an increase of 1% in labour units increases the output in 0.67%), but the red flag here is that there are negative elasticities being them a sign of model misspecification.

Finally I compute the Scale Elasticity for the Quadratic Espcification:

data$eScaleQuad = data$eCapQuad + data$eCapTL + data$eCapTL

sum(data$eScaleQuad < 0)

[1] 22

summary(data$eScaleQuad)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.7436  0.1755  0.5162  0.4576  0.7239  1.4550 

hist(data$eScaleQuad, breaks=20)

$\Longrightarrow$ I should prefer the Translog Especification provided there are negative elasticities in this part.

Comparing both especifications:

	Quadratic	Translog
$R^2$ on $y$	0.85	0.77
$R^2$ on $ln(y)$	0.55	0.63
Observations with negative predicted output	0	0
Observations with negative capital elasticity	28	32
Observations with negative labour elasticity	5	14
Observations with negative materials elasticity	8	8
Observations with negative scale elasticity	22	0

$\Longrightarrow$ Translog Especification is not perfect but better than Quadratic Specification in this case.

To seek for an accurate instrument to model the production I should go for non-parametric regression. By doing that I do not depend on a functional form and can I work on a marginal effects basis that can solve the problem with negative effects of inputs increasing.

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	Quadratic	Translog
\(R^2\) on \(y\)	0.85	0.77
\(R^2\) on \(ln(y)\)	0.55	0.63
Observations with negative predicted output	0	0
Observations with negative capital elasticity	28	32
Observations with negative labour elasticity	5	14
Observations with negative materials elasticity	8	8
Observations with negative scale elasticity	22	0