n ARDL modeling, the relationship is expressed as yt = f(y{t-n}, xt,x{t-n},zt,z{t-n})and n=1,2,3,... I have a data frame with four columns: y, x, z, and time (date) spanning from March 16, 2016, to October 21, 2023. The variable y represents the target or dependent variable, while the others are independent variables. Notably, y has missing values from January 1, 2022, to October 21, 2023.
The challenge is that y is dependent on its lag and other independent variables, and I need to estimate the missing values in y. Implementing this involves forecasting y based on its lag and other independent variables.
It's important to note that when forecasting for observations beyond the data sample, it is referred to as an out-of-sample forecast.
n ARDL modeling, the relationship is expressed as yt = f(y{t-n}, xt,x{t-n},zt,z{t-n})and n=1,2,3,... I have a data frame with four columns: y, x, z, and time (date) spanning from March 16, 2016, to October 21, 2023. The variable y represents the target or dependent variable, while the others are independent variables. Notably, y has missing values from January 1, 2022, to October 21, 2023. The challenge is that y is dependent on its lag and other independent variables, and I need to estimate the missing values in y. Implementing this involves forecasting y based on its lag and other independent variables. It's important to note that when forecasting for observations beyond the data sample, it is referred to as an out-of-sample forecast.