Shreyes2010

############################################################################################
####################### READING THE RELEVANT FILES #########################################

train.data <- read.csv("train_sample.csv")

## Set seed for reproducing the random numbers
set.seed(10001)

## This is to confirm the selection of trade_price_last{1-10}

Shreyes2010

# Specifying functions:

CalcResiduals <- function(th, data) {
   # Calculates the e_t and h_t for the GARCH(1, 1) model with given parameters.
   #
   # Argumentss:
   #   th: Parameters
   #          th[1] -> mean
   #          th[2] -> alpha.0
   #          th[3] -> alpha.1

Shreyes2010

data <- read.csv("IFE_Update.csv")

# Plotting the foreign reserves of all the countries

png("IR_Relative.png", height = 800, width = 1000)
par(mfrow = c(3, 2)) ## This is to specify the number of rows and columns you want for the plots
# India
plot(data$Index, (data$India.Millions/max(data$India.Millions[!is.na(data$India.Millions)])), 
     type="l", xlab= "Months", ylab= "Foreign reserves (in $ millions)", col="green", lwd = 2, 
     main ="India's foreign reserves since 2002")

Shreyes2010

a <- read.csv("Data.csv")

# Pretty functions to compute the MOM and YOY inflation rates

mom.inf <- function(series){
  x <- rep(NA,length(series))
  for(i in 2:length(series))x[i] <- ((series[i] - series[i-1])/series[i-1])*100
  return(x)
}

Shreyes2010

	Augmented Dickey-Fuller Test

data:  Full sample
Dickey-Fuller = -2.4901, Lag order = 4, p-value = 0.3734
alternative hypothesis: stationary 

	Augmented Dickey-Fuller Test

data:  First 80 obs
Dickey-Fuller = -2.9479, Lag order = 4, p-value = 0.1882

Shreyes2010

## Reading the relevent files

install.packages("zoo")
library(zoo)

ife <- read.csv("CPI_Data.csv")
ife_wpi <- read.csv("WPI_Data_1.csv")

# Function to compute Y-o-Y inflation rate