How to treat different frequencies of a time series data? I have annual GDP, annual CPI, monthly exchange rate, and monthly export data. I would like to go for estimation being monthly stock return as the dependent variable. What are the methods to treat the annual figures in to monthly and/or the monthly in to annual? Please, I need your recommendations or books to read?
CPI is a stock while GDP is a flow. Re-sampling of stocks to higher frequencies can be approximated with a number of choices, but probably the most common is linear interpolation. In some contexts filling forward and filling backward are common. But in principle you could fit any function you want through all the points you have and use the values from the function instead. Flows are a bit more complicated because annual flows are the sum of monthly flows. Dividing the annual flow into twelfths across the entire year is analogous to filling forward or filling backward (depending on what month of the year is the date of annual measurement). For example, if GDP is 144 and measured in December of 2018, assigning every month in 2018 a value of 12 is similar to filling backwards. But again, as long as you remember that the flows are supposed to add up across months to equal or approximate annual flows you can you a number of functional forms to approximate them. 

This is a big topic. I recommend searching for papers related to "interpolated time series". Resampling and Subsampling for Financial Time Series by E Paparoditis, DN Politis might be a good, if advanced, place to start. Modeling and Forecasting Time Series Sampled at Different Frequencies by Casals, Jerez, and Sotoca is another useful reference. In comparing the effectiveness different methods, Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment by Lepot, Aubin, and Clemens provides a nice list of methods but doesn't focus on the stock versus flow distinction. 


Deterministic methods (fill forward or back)
Nearest-neighbor
Polynomial interpolation
Distance weighting methods
Fourier transformation based methods
Regression models
Auto-regressive models
Machine learning based methods
Kernel methods
K-nearest neighbors
Box-Jenkins models
Kriging-based methods