shock_decomposition vs conditional_variance_decomposition

Dear All,

Could someone clarify what is the difference between the two commands “shock_decomposition” and “conditional_variance_decomposition”?


conditional_variance_decomposition performs a forecast error variance decomposition at the specified horizons, i.e. it tells you for example how much of the variance of output is driven by TFP shocks.

shock_decomposition uses the Kalman smoother to compute the most likely shock realizations that lead to the observed values of the data, e.g. at time t=1, 50% of output deviation from steady state is due to a positive technology shock and 50% due to a monetary policy shock.

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