US_Inf_Data

Contains the inflation data for the US for the period 1960Q1 to 2022Q3

datasets

Computes the log likelihood for an inverse gamma stochastic volatility model using a closed form expression of the likelihood. The details of the computation of this closed form expression are given in Gonzalez and Majoni (2023) <http://rcea.org/RePEc/pdf/wp23-11.pdf> .
The closed form expression is obtained for a stationary inverse gamma stochastic volatility model by marginalising out the volatility. This allows the user to obtain the maximum likelihood estimator for this non linear non Gaussian state space model. In addition, the user can obtain the estimates of the smoothed volatility using the exact smoothing distributions.

Blessings Majoni

invgamstochvol

Obtains the Log Likelihood for an Inverse Gamma Stochastic
Volatility Model

Leon Gonzalez

US_Inf_Data function

A data frame with 1 variable and 247 observations.<dl>
<dt>V1</dt>
<dd>Inflation variable</dd>
</dl>

Format

Data to use in the invgamstochvol package — US_Inf_Data

A data frame with 1 variable and 247 observations.<dl>
<dt>V1</dt>
<dd>Inflation variable</dd>


</dl>

US_Inf_Data: Data to use in the invgamstochvol package

Description

Usage

Arguments

Format

Examples