get_mu_yt: Compute the conditional mean mu_{y,t}=phi_{y,t} +...

In sstvars: Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models

Compute the conditional mean \mu_{y,t}=\phi_{y,t} + \sum_{i=1}^pA_{y,t,i}y_{t-i} for a single time period

Description

get_mu_yt computes the conditional mean \mu_{y,t}=\phi_{y,t} + \sum_{i=1}^pA_{y,t,i}y_{t-i} for a single time period based on the intercepts, AR matrices, and the vector of lagged observations.

Usage

get_mu_yt(phi_yt, all_A_yti, bold_y_t_minus_1)

Arguments

phi_yt	a (d \times M) matrix such that the mth column contains the intercept parameters of the mth regime.
all_A_yti	a 3D array containing the coefficient matrices for the given time period so that the lag i coefficient matrix A_{y,t,i} can be obtained by choosing [, , i].
bold_y_t_minus_1	a (dp \times 1) vector \boldsymbol{y}_{t-1}=(y_{t-1},...,y_{t-p}) containing the lagged observations for the time period t.

Details

This is used in simulation of the counterfactual scenarios.

Value

Returns the (d \times 1) vector of the conditional mean for the time period t.

sstvars documentation built on June 8, 2025, 10:07 a.m.