Econometric models involving data sampled at different frequencies are of general interest.
Mixed-data sampling (MIDAS) is an econometric regression developed by Eric Ghysels with several co-authors.
It can flexibly deal with data sampled at different frequencies and provide a direct forecast of the low-frequency variable.
It incorporates each individual high-frequency data in the regression, which solves the problems of losing potentially useful information and including mis-specification.
The regression models can be viewed in some cases as substitutes for the Kalman filter when applied in the context of mixed frequency data.
Bai, Ghysels and Wright (2013)[5] examine the relationship between MIDAS regressions and Kalman filter state space models applied to mixed frequency data.
High-dimensional mixed frequency time series regressions involve certain data structures that once taken into account should improve the performance of unrestricted estimators in small samples.
To that end, the machine learning MIDAS approach exploits the sparse-group LASSO (sg-LASSO) regularization that accommodates conveniently such structures.
[8] The attractive feature of the sg-LASSO estimator is that it allows us to combine effectively the approximately sparse and dense signals.
These include: In some situations it might be possible to alternatively use temporal disaggregation methods (for upsampling time series data from e.g. monthly to daily).