Preparation for the Panzar-Rosse data analysis

library(vars)
## Loading required package: MASS
## Loading required package: strucchange
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
## Loading required package: sandwich
## Warning: package 'sandwich' was built under R version 3.3.2
## Loading required package: urca
## Loading required package: lmtest
## Warning: package 'lmtest' was built under R version 3.3.2
library(astsa)
## Warning: package 'astsa' was built under R version 3.3.2
data <- read.csv(file = "/Users/YanYang/panzar-rosse/2016-Table 1-1.csv")
colname <- data.frame(number = c(1:length(names(data))), names = names(data))

Preparation for the regressors

lnINREV <- log(data[, 34]/data[, 29], base = exp(1))

lnPF <- log(data[, 3]/data[, 2], base = exp(1))
lnPK <- log(data[, 18]/data[, 17], base = exp(1))
lnPL <- log(data[, 21]/data[, 22], base = exp(1))

lnRISKASS <- log(data[, 29]/data[, 23], base = exp(1))
lnNPL <- log(data[, 31], base = exp(1))
lnAss <- log(data[, 29], base = exp(1))
use16 <- data.frame(lnINREV, lnPF, lnPK, lnPL, lnRISKASS, lnNPL, lnAss)

Fit the model with linear regression

fit <- lm(lnINREV ~ ., data = use16)
summary(fit)$coef[, c(1, 2)]
##                 Estimate Std. Error
## (Intercept) -2.198537322 0.99308560
## lnPF         0.003049342 0.06624323
## lnPK         0.079691356 0.10387076
## lnPL         0.059620996 0.11860280
## lnRISKASS   -0.616005386 0.29492397
## lnNPL        0.129998506 0.19433804
## lnAss       -0.027644699 0.04620482

Calculate the H value

sum(fit$coef[c(2:4)])