optimism

A sample data to identify optimism shock.

datasets

Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) <doi:10.1162/REST_a_00483>. The sign restrictions are implemented employing the methods proposed by Rubio-Ramírez, Waggoner & Zha (2010) <doi:10.1111/j.1467-937X.2009.00578.x>, while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramírez, & Waggoner (2018) <doi:10.3982/ECTA14468>. Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolín-Díaz and Rubio-Ramírez (2018) <doi:10.1257/aer.20161852>. Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation including the vignette by Wang & Woźniak (2024) <doi:10.48550/arXiv.2501.16711>. The 'bsvarSIGNs' package is aligned regarding objects, workflows, and code structure with the R package 'bsvars' by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, and they constitute an integrated toolset. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024.

Xiaolei Wang

bsvarSIGNs

Bayesian SVARs with Sign, Zero, and Narrative Restrictions

Tomasz Woźniak

optimism function

A matrix and a <code>ts</code> object with time series of over two hundred observations on 5 variables:<dl>
 <dt>productivity</dt>
<dd>quarterly factor-utilization-adjusted total factor productivity</dd> <dt>stock_prices</dt>
<dd>quarterly end-of-period S&amp;P 500 divided by CPI</dd> <dt>consumption</dt>
<dd>quarterly real consumption expenditures on nondurable goods and services</dd> <dt>real_interest_rate</dt>
<dd>quarterly real interest rate</dd> <dt>hours_worked</dt>
<dd>quarterly hours of all persons in the non-farm business sector</dd>
</dl>The series are as described by Beaudry, Nam and Wang (2011) in section 2.2.

Format

A 5-variable US business cycle data, from 1955 Q1 to 2004 Q4 — optimism

A matrix and a <code>ts</code> object with time series of over two hundred observations on 5 variables:<dl>
 <dt>productivity</dt>
<dd>quarterly factor-utilization-adjusted total factor productivity</dd>

 <dt>stock_prices</dt>
<dd>quarterly end-of-period S&amp;P 500 divided by CPI</dd>

 <dt>consumption</dt>
<dd>quarterly real consumption expenditures on nondurable goods and services</dd>

 <dt>real_interest_rate</dt>
<dd>quarterly real interest rate</dd>

 <dt>hours_worked</dt>
<dd>quarterly hours of all persons in the non-farm business sector</dd>


</dl>

The series are as described by Beaudry, Nam and Wang (2011) in section 2.2.

optimism: A 5-variable US business cycle data, from 1955 Q1 to 2004 Q4

Description

Usage

Arguments

Format

References