I have two time-series variables: each has 14 points with an interval of 5 years. The precise years are:
I want to do the unit root test and cointegrating test for each variable. But I think the number of observations is too small. So I want to interpolate this 5-year data to annual data. Because my variables are land areas in specific years, and they are increasing year by year as the data are based on large scale and the general trend is growing.
I have tried to interpolate the data by linear interpolation: the time-series plots can be seen as below, the blue points are original points, the red ones are interpolated:
Because the data of one variable (protected area) are based on my spatial analysis, and each layer costs much time for PC to run. So I want to interpolate data to add observations. In other hand, because the protected area increased by a stable rate (no drop or very rapid increase), so I think even after I use spatial analysis processing to generate the data of middle years, the results won't have much discrepancy with the interpolated ones.
I want to know whether can I do this when considering the characteristics of my data and my data processing purpose. And if doing this, anything should I notice when I do time-series processing?
These was a similar question on Cross Validated: however, it hasn't be solved: How to interpolate independent variable over five-year period?
My data are as follows:
Year,Parea,Uarea 1950,3435829.43 ,144179.7476 1955,3619503.16 ,168028.4699 1960,3881482.63 ,196839.0495 1965,4310040.34 ,229032.161 1970,4950230.51 ,262543.7928 1975,6216028.19 ,297502.4439 1980,7062749.74 ,337481.6276 1985,8187770.34 ,381059.4338 1990,9893501.67 ,432255.4666 1995,12011196.93 ,487330.1703 2000,13327189.88 ,546829.7056 2005,15231484.09 ,612606.1358 2010,16986859.05 ,683200.605 2014,18097951.40 ,743693