I have CPI inflation data of India and US. I have to regress this data with a dataset of frequency daily. How to convert the monthly CPI inflation data to daily CPI inflation data.
Thanks in advance.
TIME | inflation_US | inflation_IN |
---|---|---|
2011-01 | 1.631847 | 9.302325 |
2011-02 | 2.107585 | 8.823529 |
2011-03 | 2.681603 | 8.823529 |
2011-04 | 3.163631 | 9.411765 |
2011-05 | 3.568646 | 8.72093 |
2011-06 | 3.558828 | 8.620689 |
2011-07 | 3.628716 | 8.426967 |
2011-08 | 3.771208 | 8.988764 |
2011-09 | 3.868357 | 10.05587 |
2011-10 | 3.5252 | 9.392265 |
2011-11 | 3.394378 | 9.340659 |
2011-12 | 2.962419 | 6.486486 |
2012-01 | 2.925217 | 5.319149 |
2012-02 | 2.871099 | 7.567567 |
2012-03 | 2.651398 | 8.648648 |
2012-04 | 2.30274 | 10.21505 |
2012-05 | 1.704254 | 10.16043 |
2012-06 | 1.663994 | 10.05291 |
2012-07 | 1.408451 | 9.84456 |
2012-08 | 1.692379 | 10.30928 |
2012-09 | 1.991282 | 9.137055 |
2012-10 | 2.162344 | 9.59596 |
2012-11 | 1.764134 | 9.547739 |
2012-12 | 1.741022 | 11.16751 |
2013-01 | 1.594865 | 11.61616 |
2013-02 | 1.977924 | 12.0603 |
2013-03 | 1.473896 | 11.44279 |
2013-04 | 1.063085 | 10.2439 |
2013-05 | 1.361965 | 10.67961 |
2013-06 | 1.754417 | 11.05769 |
2013-07 | 1.960682 | 10.84906 |
2013-08 | 1.518368 | 10.74766 |
2013-09 | 1.184925 | 10.69767 |
2013-10 | 0.9636127 | 11.05991 |
2013-11 | 1.237072 | 11.46789 |
2013-12 | 1.501736 | 9.132421 |
2014-01 | 1.578947 | 7.239819 |
2014-02 | 1.126349 | 6.726458 |
2014-03 | 1.512203 | 6.696429 |
2014-04 | 1.952858 | 7.079646 |
2014-05 | 2.127111 | 7.017544 |
2014-06 | 2.072341 | 6.493506 |
2014-07 | 1.992329 | 7.234043 |
2014-08 | 1.699611 | 6.751055 |
2014-09 | 1.657919 | 6.302521 |
2014-10 | 1.66434 | 4.979253 |
2014-11 | 1.322355 | 4.115226 |
2014-12 | 0.7564933 | 5.85774 |
2015-01 | -0.08934832 | 7.172996 |
2015-02 | -0.0251298 | 6.302521 |
2015-03 | -0.07363739 | 6.276151 |
2015-04 | -0.1995174 | 5.785124 |
2015-05 | -0.03993275 | 5.737705 |
2015-06 | 0.1237712 | 6.097561 |
2015-07 | 0.1695698 | 4.365079 |
2015-08 | 0.1950793 | 4.347826 |
2015-09 | -0.03612975 | 5.13834 |
2015-10 | 0.1705744 | 6.324111 |
2015-11 | 0.5017976 | 6.719368 |
2015-12 | 0.7295198 | 6.324111 |
2016-01 | 1.373087 | 5.905512 |
2016-02 | 1.0178 | 5.533597 |
2016-03 | 0.8525362 | 5.511811 |
2016-04 | 1.12511 | 5.859375 |
2016-05 | 1.019323 | 6.589147 |
2016-06 | 0.9973265 | 6.130268 |
2016-07 | 0.8271388 | 6.463878 |
2016-08 | 1.062875 | 5.30303 |
2016-09 | 1.463784 | 4.135338 |
2016-10 | 1.635988 | 3.345725 |
2016-11 | 1.692537 | 2.592592 |
2016-12 | 2.074622 | 2.230483 |
2017-01 | 2.500042 | 1.858736 |
2017-02 | 2.737958 | 2.621723 |
2017-03 | 2.380612 | 2.61194 |
2017-04 | 2.19969 | 2.214022 |
2017-05 | 1.874878 | 1.090909 |
2017-06 | 1.633488 | 1.083032 |
2017-07 | 1.727978 | 1.785714 |
2017-08 | 1.938974 | 2.517986 |
2017-09 | 2.232964 | 2.888087 |
2017-10 | 2.041129 | 3.23741 |
2017-11 | 2.202583 | 3.971119 |
2017-12 | 2.109082 | 4 |
2018-01 | 2.070508 | 5.109489 |
2018-02 | 2.211795 | 4.744525 |
2018-03 | 2.359711 | 4.363636 |
2018-04 | 2.462744 | 3.971119 |
2018-05 | 2.801012 | 3.956835 |
2018-06 | 2.871548 | 3.928571 |
2018-07 | 2.949515 | 5.614035 |
2018-08 | 2.69918 | 5.614035 |
2018-09 | 2.276972 | 5.614035 |
2018-10 | 2.52247 | 5.226481 |
2018-11 | 2.176601 | 4.861111 |
2018-12 | 1.910159 | 5.244755 |
2019-01 | 1.551235 | 6.597222 |
2019-02 | 1.520135 | 6.968641 |
2019-03 | 1.862523 | 7.665505 |
2019-04 | 1.99644 | 8.333333 |
2019-05 | 1.790228 | 8.650519 |
2019-06 | 1.648485 | 8.591065 |
2019-07 | 1.811465 | 5.980066 |
2019-08 | 1.74978 | 6.312293 |
2019-09 | 1.711305 | 6.976744 |
2019-10 | 1.764043 | 7.615894 |
2019-11 | 2.051278 | 8.609271 |
2019-12 | 2.28513 | 9.634551 |
2020-01 | 2.486572 | 7.491857 |