dyn_adj_est: Estimate Models for Dynamic Adjustment Analysis

In nelson16silva/wavcoreinf: Estimation and Evaluation of Wavelet Core Inflation Measures

Estimate Models for Dynamic Adjustment Analysis

Description

Use dyn_adj_est to estimate several models for dynamic adjustment analysis.

Usage

dyn_adj_est(y, xreg, h, p, ...)

Arguments

y	A vector. If the user estimates the first regression described below, y represents headline inflation or core inflation measure if the regression is the second. The choice depends if interest is in \lambda_h or in \lambda^*_h.
xreg	A vector. Infation or core inflation depending of the y choice.
h	An integer. The horizon of prediction.
p	An integer to specify the lag p.
...	Additional parameter to pass to the function lags for specifying if it is recursive or not.

Details

For \pi being the healine inflation and \pi^* a core inflation measure, two specifications of a regression model can be estimated through this function:

1. \pi_{t + h} - \pi_t = a_0 + \lambda_h(\pi_t - \pi^*_t) + \sum_{i = 1}^pa_i\pi_{t - i} + e_{t + h},

2. \pi^*_{t + h} - \pi^*_t = a^*_0 + \lambda^*_h(\pi_t - \pi^*_t) + \sum_{i = 1}^pa^*_i\pi^*_{t - i} + e^*_{t + h}.

Value

A list where each element of it contains the following:

data	A tibble with the data used in fitting the model.
model	A lm object.
n_obs	Number of observations of y.
h	Horizon used in direct estimation.

See Also

lags, comb_regr

Examples

inf_head <- coreinf_br[["ipca"]]
inf_corems <- coreinf_br[["ipcams"]]
dyn_adj_est(inf_head, inf_corems, 2, 2)
dyn_adj_est(inf_corems, inf_head, 2, 2)

nelson16silva/wavcoreinf documentation built on Feb. 17, 2025, 7:10 p.m.