mini-kep/user-charts

Chart macroeconomic time series from mini-kep dataset

user-charts's Issues

Prescot time series comovement table

Replicate correlations from Prescot artice, see https://github.com/epogrebnyak/cmf-comovement-tables.

put code access sample

import pandas as pd

URL = {'a': 'http://mini-kep.herokuapp.com/annual',
    'q': 'http://mini-kep.herokuapp.com/quarterly',
    'm': 'http://mini-kep.herokuapp.com/monthly'}

def read_csv(source):
    return pd.read_csv(source, converters={0: pd.to_datetime}, index_col=0)

dfa, dfq, dfm = (read_csv(URL[freq]) for freq in 'aqm')

todo: seasonality procedure

As a simple approximation of the task - in a dataset I have some highly seasonal time sereis like these ones:

• http://minikep-db.herokuapp.com/all/series/INVESTMENT_bln_rub/m
• http://minikep-db.herokuapp.com/all/series/TRANSPORT_FREIGHT_bln_tkm/m

Can look up these in macrodash

As well as some time series where the seasonlaity is not apparent, but you also want to have it removed, as in GDP

Basically, I would like to store one or several seasonally adjusted versions for the indicator
and deliver it in API with sa parameter

http://minikep-db.herokuapp.com/all/series/TRANSPORT_FREIGHT_bln_tkm/m/sa (example, not working)

also sa can contain a verson like sa:x13, sa:something

a problem for me is that my code is python and most seasonal adjustment is in R
my own little snippet of R code

refine case list

plot retail sales data

@alexeyzakharenkov made an attempt to chart CPI retail sales from FRED for US and mini-kep fro Russian, here are two notebooks: https://github.com/alexeyzakharenkov/mini-kep-chart

It is good for the start, but I think several things can be done:

• reduce boilerplate/unnecessary code
• make titles in English
• refine the thesis - what exactly we want to illustrate and why this might be interesting
• notebooks put in a seperate folder, not repo root
• think of common naming for notebooks, eg like here

@ravshansk - maybe you can look at this?

literature on reserves

charts:

• https://fred.stlouisfed.org/series/USLLRTL

heavier reading:

• https://www.dnb.nl/binaries/working%20Paper%20404_tcm46-301517.pdf
• https://www.bis.org/publ/qtrpdf/r_qt1703f.htm
• https://www.bde.es/f/webbde/GAP/prensa/intervenpub/diregen/regula/ficheros/es/regula170609e.pdf