Just in case anyone is interested at how I approached this in the end: I created a matrix with the inflation rates and then looped on the whole data set performing the multiplications. the code is attached. it is not working perfectly but i think there problem is with the data and there is some excess manipulation in the code due to the format of the data i am working with matrix INFL = { 515, 438.35, 367.83, 303.24 ; 252.1, 202.28, 159.59, 130.99 ; \ 107.75, 87.03, 70.13, 45.06 ; 31.64, 20.25, 12.75, 8.27 ; 6.18, 4.22, 3.09, \ 2.9 ; 2.85, 2.67, 2.59, 2.42 ; 2.32, 2.23, 2.18, 2.09 ; 2, 1.92, 1.87, 1.79 ;\ 1.68, 1.6, 1.55, 1.49 ; 1.44, 1.38, 1.31, 1.26 ; 1.22, 1.15, 1.08, 1.07} # inflation figures, each year is a row smpl full genr monthly_w_c = (V4 < 6) ? V49/1000 : V49 genr Adapt_wage = monthly_w_c genr k = 0 # will be used to store the year genr l = 0 # will be used to store the quarter genr br=$nobs loop j=1..br genr m = j k = V4[j] genr kr = (k < 10) ? k : (k=90) ? 10 : 11 l = V3[j] genr lr = (l < 5) ? l : 4 # there is at least one quarter var that is 7 for some reason Adapt_wage[j] = INFL[kr,lr]*Adapt_wage[j] endloop "I often see problems in the world and wonder why someone doesn't fix them. Then I realize that I am someone, and that makes me feel bad because it is all my fault." http://dilbert.com/blog/entry/fixing_the_world/ ________________________________ From: Raz Lev <raz_lev(a)yahoo.com&gt; To: gretl-users(a)lists.wfu.edu Sent: Friday, April 10, 2009 3:27:00 PM Subject: [Gretl-users] Correcting for inflation Hello again, I am trying to convert data to account for inflation. I have an excel (or variable, whichever is better) with the cumulative inflation for every quarter between 1980 and 1990 (my period of interest) and in gretl data about salaries that contain, amongst else, the wage, and the year and the quarter in which the sample was taken. What I want to do is to multiply all the wages from samples from quarter i, year j with the right inflation factor. Any hints on how to do this? thanks very much, Raz "I often see problems in the world and wonder why someone doesn't fix them. Then I realize that I am someone, and that makes me feel bad because it is all my fault." http://dilbert.com/blog/entry/fixing_the_world/

On Fri, 10 Apr 2009, Allin Cottrell wrote: Sorry, I meant base 1983:3. Allin.

On Sat, 11 Apr 2009, Raz Lev wrote: 400,000 monthly CPI figures would be 33,333 years' worth, so I assume you can't have that. Even 400,000 daily observations would be about 1000 years' worth, so I guess you don't have that either. My best guess, then, is that you have (a) a panel with a large number of "individuals" observed in each of several months or years, plus (b) a plain time series of CPI figures, covering all the time periods in the panel sample, and you want to expand the CPI data into panel form, so that (e.g.) the CPI value for 1980:01 gets written into each slot in the big dataset that records an individual's characteristics in 1980:01. If my interpretation is right, this is a somewhat fiddly task. Here's one way to do it (I suspect there may be a more elegant way). Let's say I have these CPI data in cpi.txt: date cpi 1980:01 100 1980:02 101 1980:03 103 1980:04 105 1980:05 106 and let's say I want to create a data file containing 30 stacked copies of this series for 30 panel units. Then <script> open cpi.txt # contains time-series data scalar n = 30 # assuming 30 panel units scalar T = $nobs # number of CPI observations scalar nT = n * T # target number of observations matrix m = zeros(nT, 1) scalar k = 1 loop n --quiet loop j=1..T --quiet m[k] = cpi[j] k++ endloop endloop # create full-length dataset, preserving the matrix nulldata nT --preserve # convert matrix to series series bigcpi = m # define the new dataset as a panel setobs T 1:1 --stacked-time-series # check that our expanded series looks OK print bigcpi --byobs # save as gretl data file store bigcpi.gdt bigcpi </script> The new data file bigcpi.gdt can now be appended (File/Append data/Standard format) to an existing panel data file in stacked time series format. Allin Cottrell