MIDAS is useful when you have a low frequency series and you want to include high frequency data in the regression. So for instance, if you want to forecast quarterly GDP data and want to include daily S&P 500 data as a regressor instead of just using the quarter end value of S&P 500.
Usually we assume that the causality runs from S&P 500 to GDP. It becomes trickier when you start thinking about the impact of lagged GDP on S&P 500. You can usually get away with some kind of ad hoc adjustment like filling in the GDP growth rate for each daily period as equal to the quarterly growth rate, but it won't necessarily capture the relationship properly.
Kalman Filters kabi davlat makon usullari aralash ma'lumotlar ketma-ketligini qo'llashda yana bir keng tarqalgan yondashuv. Philly Fed-ning ADS biznes shartlari indekslari va NowKutingdagi adabiyotlar bulardan foydalanadi. Bu katta, murakkab adabiyot, ehtimol, xulosa chiqarish uchun kichik postning orqasida. Yondashuvning umumiy ma'nosiga erishish uchun ular ma'lumotlar etishmayotgan deb hisoblashadi va yo'qolgan ma'lumotlarni hisobga olgan holda parametrlarni baholash uchun Kalman Filtersdan foydalanishadi.
Another approach is to use a combination of forecasting models. In the above S&P 500 and GDP example, you could use a daily forecasting model that includes GDP and a quarterly forecasting model that includes it. You can use the daily model to forecast iteratively out for the remainder of the quarter and then use the quarterly model thereafter. This approach could be made significantly richer with monthly models, etc.
Sizning ikkinchi savolingizga kelsak, ushbu modelni baholash qiyin bo'ladi. Modeldagi modelni yaratganingizdan so'ng uning parametrlarini kiritishingiz mumkin, prognozlash odatda sodda.