Hi Naveed,

To convert the yearly data into quartely data you can use "Ecotrim" software for univariate and multivariate temporal disaggregation developed by Eurostat. Based on mathematical/statistical techniques, Ecotrim produces high frequency estimates (short term statistics) starting from low frequency data.

For more information see:

http://circa.europa.eu/Public/irc/dsis/ecotrim/library

Kind regards

Dear Saidi is Econtrim free download available

Hi,

Normaly, yes, Ecotrim is free. Other possibility, you can also other try to test a similar progam. See the web sites of eurostat, OECD, ECB for more information of Ecotrim.

See an example  here:

http://www.oecd.org/std/21781488.ppt

Best regards

hi Naveed Ahmad Lone

There are a few methods which can disaggregate time-series but besides the original annual series you need to have a quarterly/monthly indicator series from which the seasonal cycle will be inferred.

These benchmarking and extrapolation methods try to conserve as much as possible from the quarter-to-quarter changes in new series with simultaneously having a binding constraint that the sum of four quarters is same as one value in annual series.

arg min {x} t(x)Ax such that Ra=c, where x is error between bench-marked series a and original higher frequency series b. Vector c contains annual values and R is a matrix of constraints.

you can also use "workfile" program.

check below the link

http://forums.eviews.com/viewtopic.php?f=7&t=3799

best wishes

Naveed, it depends on the series you are looking on. If you let me know the series, would be helpful. Yes, using ecotrim is a general option. Agree with Youssef.

sir i have data on GDP, Money supply  fiscal deficit, and i have ms and fd data quarterly, but gdp is yearly. 

Is that for one country ? if it is so, you can get quaterly data from Annual reports of most of the central bank.

Hi,

Please keep in mind that all tests will be joint tests about the model and the way you change the frequency. So my aim is to go on with the original data, and avoid converting series. Instead of GDP you can also use the industrial production index, or an other proxy. This makes more sense to me.

Best

Philippe 

I would suggest aggregating the quarterly data into yearly data  to match with the GDP variable in your model. This is because variable measurement errors can cause more serious damage to estimation results as compared with the aggregation bias problem.

In addition to Ecotrim, the packages for temporal disaggregation are available in different platforms e g. in R package tempdisagg is used for this purpose. But this kind of disaggregation is based on some assumptions (about seasonality etc)  and I agree with Philippe that any econometric model based on disaggregated data will have a joint test problem. The best solution is to use alternative proxy variable.

If you have data with mixed frequency you can also think to used "Mixed Data Sampling Models (MIDAS), Ref for example: Ghysels, Eric, Arthur Sinko, and Rossen Valkanov. "MIDAS regressions: Further results and new directions." Econometric Reviews 26.1 (2007): 53-90.

I want to go by Badhani and Philippe s comments. You need to disaggregate the yearly data for meaningful analysis and comparison. You can then weight the cost and benefits

Olanrewaju Ismail Shittu Sir what type of cost and benefits Plz comment sir

The idea is that measurement error in variables can destroy your results. And by changing frequencies, you will include measurement error into your analysis. If in the model y=ax+b measurement error is in x, you have a huge problem and a will be biaised. If it is in y, it is ok.

I don t know what your model is, but that is something you have to considéré, I feel.

Best

Philippe

Use EViews. See link below

https://www.youtube.com/watch?v=FSMjB0XqHUA

Refer following link.

https://www.eviews.com/Learning/freqconv_a.html

I have similar problem with 12 independent annual time series variables for 18 year period, but my chair advised that the period is not enough for a parametric regression, therefore, it has to be disaggregated to quarterly time series. Any advise will be highly appreciated. How can I disaggregate these variables, any recommended software is ok.

Thanks

Austin, you can follow the link provided by Ume Salma Akbar.

Have there any strong journal paper they used his method ?