२०७७ कात्तिकमा वार्षिक बिन्दुगत उपभोक्ता मुद्रास्फीति ४.०५ प्रतिशत रहेको छ ।
तरकारी उपसमुहको मूल्यवृद्धि २०.८८ प्रतिशत र दाल तथा गेडागुडीउपसमुहको मूल्यवृद्धि १३.७० प्रतिशत रहेको छ।
कुल वस्तु निर्यात १०.८ प्रतिशतले वृद्धि भई रु.४० अर्ब २०करोड पुगेको छ ।
कुल वस्तु आयात १०.६ प्रतिशतले घटेर रु.४०२ अर्ब ४९करोड कायम भएको छ ।
कुल वस्तु व्यापार घाटा १२.५ प्रतिशतले घटी रु.३६२ अर्ब २९करोड कायम भएको छ ।
खुद सेवा आय रु.१५ अर्ब ६४ करोडले घाटामा रहेको छ ।
विप्रेषण आप्रवाह ११.२ प्रतिशतले वृद्धि भई रु.३३७ अर्ब ७२ करोड पुगेको छ ।
वैदेशिक रोजगारीका लागि अन्तिम श्रम स्वीकृति लिने नेपालीको संख्या ७५.८ प्रतिशतले कमी आएको छ ।
चालु खाता रु.२० अर्ब ४६ करोडले बचतमा रहेको छ ।
शोधनान्तर स्थिति रु.११० अर्ब ६५ करोडले बचतमा रहेको छ ।
कुल विदेशी विनिमय सञ्चिति ७.४ प्रतिशतले वृद्धि भई २०७७ कात्तिक मसान्तमा रु.१५०६ अर्ब ६ करोड पुगेको छ ।
बैकिङ्ग क्षेत्रसँग रहेको विदेशी विनिमय सञ्चिति १५.४ महिनाको वस्तु आयात र १४.० महिनाको वस्तु तथा सेवा आयात धान्न पर्याप्त रहने देखिन्छ ।
बैंकिङ्ग कारोबारमा आधारित सरकारको वित्त स्थिति रु.१०अर्ब २३ करोडले घाटामा रहेको छ ।
संघीय सरकारको कुल खर्च रु.२५०अर्ब ४१ करोड रहेको छ ।
बैंकिङ्ग कारोबारमा आधारित राजस्व संकलन रु.२४०अर्ब १५करोड रहेकोे छ ।
सरकारका विभिन्न खातामा रु.२२०अर्ब ९८करोड नगद मौज्दात रहेको छ ।
विस्तृत मुद्राप्रदाय ६.४प्रतिशतले बढेको छ ।
कुल आन्तरिक कर्जा ४.४प्रतिशतले बढेको छ ।
बैंक तथा वित्तीय संस्थाहरूमा रहेकोनिक्षेप ५ प्रतिशतले बढेको छ ।
बैंक तथा वित्तीय संस्थाहरुबाट निजी क्षेत्रमा प्रवाहित कर्जा ४.९ प्रतिशतले बढेको छ ।
कुल ४६ हजार ३४३ ऋणीलाई सहुलियतपूर्ण कर्जा प्रवाह भई रु.७९ अर्ब ७५करोड कर्जा बक्यौता रहेको छ ।
वाणिज्य बैंकहरुको औसत आधार दर २०७६ कात्तिकमा ९.५०प्रतिशत रहेकोमा २०७७ कात्तिकमा ७.५७ प्रतिशत कायम भएको छ ।
२०७७ कात्तिकमा वाणिज्य बैंकहरुको निक्षेपको भारित औसत ब्याजदर ५.३१ प्रतिशत र कर्जाको भारित औसत ब्याजदर ९.५२ प्रतिशत रहेको छ ।
इजाजतप्राप्त बैंक तथा वित्तीय संस्थाहरुको संख्या २०७७ कात्तिक मसान्तमा १४६ कायम भएको छ ।
बैंक तथा वित्तीय संस्थाहरुको शाखा संख्या २०७७ असार मसान्तमा ९७६५ रहेकोमा २०७७ कात्तिक मसान्तमा ९९३७ पुगेको छ ।
नेपाल स्टक एक्सचेन्ज लिमिटेडमा सूचीकृत कम्पनीहरूको संख्या २१२ रहेको छ ।
Monday, December 21, 2020
आर्थिक वर्ष २०७७७८ को चार महिनाको आर्थिक तथा वित्तीय अवस्थाको संक्षिप्त झलक
Friday, December 18, 2020
Bar Chart Race/Bar Chart Animation in R
R is a very powerful software for data visualization. In this post, I present a simple case of how data can be visualized in Bar Chart Race in R. I have used the COVID cases data by country and showed the evolution of COVID cases in the 10 most affected countries during the last 350 days.
rm(list=ls()) # removes the existing objects from the environment.
# library used
library(tidyverse)
library(readxl)
library(dplyr)
library(gganimate)
# setting working directory
setwd('C:/Users/siddhabhatta/Desktop/October31')
# data source : https://www.ecdc.europa.eu/en/covid-19/data
# importing data
The data and R script can be downloaded from the link below:
https://drive.google.com/drive/folders/1HkPFE7v4fIx2rOOJnhE1E52rCQlGkigq?usp=sharing
data=read_excel('coviddec14.xlsx')
# first few observations
head(data)
# creating a new date variable with standard date format
data$date<-as.Date(data$dateRep, format="%m/%d/%y")
head(data$date)
# Making the country names short
data$country[data$country=="United_States_of_America"]<-"USA"
data$country[data$country=="United_Kingdom"]<-"UK"
data$country[data$country=="Cases_on_an_international_conveyance_Japan"]<-"Intl_CV_Center_Japan"
#groupoing the data by country and date and finding cumulated total of cases per day
datanew<-data %>% # %>% can be read as then
select(country, cases, date, continent) %>%
group_by(continent, country, date) %>%
summarise(total=sum(cases)) %>%
mutate(cumtotal=cumsum(total))
# prepare data by ranks and filter the top 10 countries
data2=datanew %>%
group_by(date) %>%
arrange(date, -cumtotal) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 10)
# producing the static 350 ggplots
data2 %>%
ggplot()+
aes(xmin = 0 ,
xmax = cumtotal) +
aes(ymin = rank - 0.45,
ymax = rank + 0.45,
y = rank) +
facet_wrap(~ date) +
geom_rect(alpha = .7) +
aes(fill = continent) +
scale_fill_viridis_d(option = "magma",
direction = -1) +
scale_x_continuous(
limits = c(-5000000, 16000000),
breaks = c(-5000000, 0, 4000000, 8000000, 12000000, 16000000)) +
geom_text(col = "darkblue",
hjust = "right",
aes(label = country),
x = -100) +
geom_text(col = "darkblue",
hjust = "right",
aes(label = paste(cumtotal), x=12000000)) +
scale_y_reverse() +
labs(fill = NULL) +
ggtitle("Evolution of Covid-19 Cases")+
labs(x = "Covid Cases") +
labs(y = "Top 10 Countries") +
theme_classic() ->
my_plot
# saves the plot in the object my_plot
# animate the 350 frames by date and save it as p
p<-my_plot +
facet_null() +
geom_text(x = 8000000 , y = -10,
family = "Times",
aes(label = as.character(date)),
size = 12, col = "green") +
aes(group = country) +
transition_time(date)
#Animate p with total 350 frames and 5 frames per second
animate(p, nframes=350, fps=5, width=1000)
Saving the results as gif format
gif<- animate(p, fps = 5, width = 1000, height = 700,
renderer = gifski_renderer("gganim.gif"), end_pause = 15, start_pause = 15)
anim_save("gganim.gif", animation = gif )
Here is the output.
And here is the video explanation.
Thursday, December 17, 2020
Session on 'GDP: Concepts, Measurement, Problems and Uses
I am conducting a live session on 'GDP: Concepts, Measurement, Problems and Uses' on coming Saturday (19 December 2020).
Please, register at
https://forms.gle/
-How GDP is Measured.
-Production, Expenditure and Income Approaches to GDP
-GDP Deflator
-GNI , GNDI and PCI
-Domestic and National Savings
-Where remittances are recorded?
-How economic growth rate is measured?
-GDP at PPP
-Problems in GDP Measurement
-Uses of GDP
Wednesday, December 9, 2020
Line Chart Animation in R
R is a powerful software environment for dealing with graphics. In this post, I illustrate the use of R for producing line chart animation. I will use Nepal Stock exchange data with 2205 daily observations.
The data and R script can be downloaded from here.
# It uses the following packages in R
library(ggplot2)
library(lubridate)
library(dplyr)
library(gganimate)
library(tidyr)
# First set the working directory
setwd("C:/Users/siddhabhatta/Desktop/October31")
# read the data by using the 'readxl' package.
library(readxl)
nepse=read_excel('nepse.xlsx')
head(nepse)
# save date as standard date format
nepse$new_date<-as.Date(nepse$date, format="%m/%d/%y")
head(nepse$new_date)
summary(nepse)
# produce a static line plot
ggplot(data=nepse, aes(x=new_date, y=close))+
geom_line(color="blue", size=1.0)+
theme_classic()+
ggtitle("Nepse Index Movement in Nepal")
# add aesthetics and labels to the plot and save it as an object (p here)
p<-nepse %>%
ggplot(aes(x=new_date, y=close))+
geom_line(color="blue", size=1.0)+
geom_point(size=5, color="green")+
geom_text(aes(label=new_date),color="darkblue", fontface="bold", vjust=-2)+
geom_text(aes(label=close),color="red",fontface="bold", vjust=-4)+
theme_classic()+
theme(plot.title = element_text(hjust = 0.5))+
ggtitle("NEPSE Index Movement of the Past 2205 Days")+
transition_reveal(new_date) # this last line produces the animation by date
animate(p, fps=2, nframes=500, width=1200) # number of frames 500 and frame per second is 2
# you can save the animation in gif format by usng the following line of codes
p1<-animate(p, nframes=500, fps = 2, width = 1200,
renderer = gifski_renderer())
anim_save("animation.gif", animation = p1 )
Here is the output.
Friday, December 4, 2020
How to Estimate the GDP Loss due to COVID-19
Many of my followers have asked a lot of queries about how to estimate the GDP loss due to a crisis such as COVID-19. In this blog, I illustrate a simple but scientific approach to calculate such loss.
To calculate the GDP Loss, we need :
Let us take the case of Nepal.
In the year 2018/19, GDP in nominal terms in Nepal was Rs. 3458793 million.
In the year 2019/20, the economy was expected to follow the trend growth of the past three years i.e. economic growth rate was projected to be 7 percent. With the deflator growth of 6.5 percent (a measure of inflation), the growth rate of nominal GDP would be : ((1*1.07*1.065)-1)*100=13.955
With this, GDP in 2019/20 would be 3458793*1.13955=Rs. 3941467 million. This is the size that Nepali economy would achieve has there no COVID-19. So it is called baseline GDP.
Now, lets us come to the case of COVID-19. If we assume that the growth rate is zero percent, then the growth rate of nominal GDP can be calculated as : 1*1.0*1.065=6.5 percent(1*(1+growth_rate/100)*(1+deflator_inflation/100). As such the actual GDP size in 2019/20 is : 3458793*1.065=3683614 million.
Now, GDP Loss=Baseline GDP-Actual GDP=Rs. 3941467-Rs. 3683614=Rs. 257.826 million or Rs. 257 billion.
Loss in GDP in percent =257826/3941467 =6.54 percent
Again suppose that the economy contracted by 2 percent in 2019/20 instead of growing by zero percent. The size of GDP in 2019/20 would be Rs. 3458973*0.98*1.06489=Rs. 3609568 million.
GDP Loss =3941467-3609568=331491 million or 331 billion.
Loss in GDP in percent =331491/3941467 =8.41 percent .
If we assume that the economic growth rate would be 6 percent had there no corona crisis, GDP loss would be Rs. 221 billion in case zero growth and Rs. 295 billion in case of contraction by 2 percent.
This simple calculation shows that the economy lost between Rs. 221 billion to 331 billion income in 2019/20 which is about 5.7 percent to 8.4 percent of GDP.