us_fiscal_cond_forecasts: A matrix to be used in a conditional forecasting example...

In bsvars: Bayesian Estimation of Structural Vector Autoregressive Models

A matrix to be used in a conditional forecasting example including the projected values of total tax revenue that are projected to increase at an average quarterly sample growth rate. The other two columns are filled with NA values, which implies that the future values of the corresponding endogenous variables, namely government spending and GDP, will be forecasted given the provided projected values of total tax revenue. The matrix includes future values for the forecast horizon of two years for the US fiscal model for the period 2024 Q3 – 2026 Q2.

Description

Conditional projections variables to be used in conditional forecasting of government spending and GDP given the provided projected values of total tax revenue. Last data update was implemented on 2024-10-22.

Usage

data(us_fiscal_cond_forecasts)

Format

A matrix and a ts object with time series of eight values on 3 variables:

ttr

the values are provided. This variable will not be forecasted.

gs

not provided. This variable will be forecasted conditionally on the provided values for ttr.

gdp

not provided. This variable will be forecasted conditionally on the provided values for ttr

The series are as described by Mertens & Ravn (2014). The data was used by Lütkepohl, Shang, Uzeda, Woźniak (2024).

References

Lütkepohl, H., Shang, F., Uzeda, L., and Woźniak, T. (2024) Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference. University of Melbourne Working Paper, 1–57, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2404.11057")}.

Mertens, K., and Ravn, M.O. (2014) A Reconciliation of SVAR and Narrative Estimates of Tax Multipliers, Journal of Monetary Economics, 68(S), S1–S19. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jmoneco.2013.04.004")}.

Examples

data(us_fiscal_cond_forecasts)   # upload the data

bsvars documentation built on Oct. 24, 2024, 5:11 p.m.