I have two macroeconomic time series with annual GDP growth rates for a given country. One series has annual frequency (year-on-year growth) and historical data from mid 1990s until 2012 (about 20 data points). The other series starts in 2013 and has quarterly frequency (quarter-on-quarter growth; about 18 data points).
The objective is to conduct a forecasting exercise to verify the predictability of GDP. One possibility is to only use one of the series (either annual frequency series or quarterly frequency series), but this means there are only very few data points available – probably too few observations to get meaningful results.
I had the idea to somehow combine both series in order to increase the number of observations available for model training and testing, but I have not come across any references (libraries, papers etc.) in this respect.
Are there are any strategies to deal with this situation and, if so, what are the options?
Best Answer
If you are ok with yearly forecast, convert quarterly growth into yearly. like 10%, 10%, 10%, 10% quarterly growth would become 46.41 % yearly growth.
24 data points are less but this is what we have. Start with Moving average and then build on as GDP growth would have less variation.