Hi, I want to write a code for extended Kalman filter. I know SAS has subroutine such as KALCVF but this subroutine is for a standard linear Kalman filter. The observation equation that I am trying to deal with has non-linear and time-varying coefficients that depends on the value of the previous state variables and observation variables. Specifically, my extended Kalman filter is
X_t = a X_t-1 + error (State Equation)
Y_t = f(Y_t-1, X_t-2) X_t-1 + error (Observation Equation),
where f is a quadratic function.
I think there is no way to use subroutine for run this Kalman filter. Probably, I need to write a code from scratch. If anyone had similar experience, please help me with this problem.
Thank you
are you using a sas dataset as input and want to use it in SAS data step? If so can you post a sample of this data and explains what aX_t and aY_t mean? Mathematically I understand what these mean. Do you have an expression for f that you can use in a data step. Do you have expressions for error function, state equation and observation equation. If so then you can write it in a sas data step using lawn function in SAS.
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