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Description

Multicountry Term Structure of Interest Rates Models.

Package for estimating, analyzing, and forecasting multi-country macro-finance affine term structure models (ATSMs). All setups build on the single-country unspanned macroeconomic risk framework from Joslin, Priebsch, and Singleton (2014, JF) <doi:10.1111/jofi.12131>. Multicountry extensions by Jotikasthira, Le, and Lundblad (2015, JFE) <doi:10.1016/j.jfineco.2014.09.004>, Candelon and Moura (2023, EM) <doi:10.1016/j.econmod.2023.106453>, and Candelon and Moura (2024, JFEC) <doi:10.1093/jjfinec/nbae008> are also available. The package also provides tools for bias correction as in Bauer Rudebusch and Wu (2012, JBES) <doi:10.1080/07350015.2012.693855>, bootstrap analysis, and several graphical/numerical outputs.

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Overview

MultiATSM is an R package for estimating, analyzing, and forecasting multi-country macro-finance affine term structure models (ATSMs). The package implements a variety of models, including JPS, GVAR, and JLL frameworks (see complete references below), and provides tools for bias correction, bootstrap analysis, and graphical/numerical outputs.

Features

  • Estimation of multi-country ATSMs (JPS, 2014, JLL, 2015, GVAR, 2023-2024, and variants)
  • Flexible input structure for macroeconomic and yield data
  • Graphical and numerical outputs for model diagnostics and interpretation
  • Bias correction for a VAR dynamics (BRW, 2012)
  • Bootstrap analysis for confidence intervals
  • Out-of-sample forecasting

Installation

You can install the development version of MultiATSM from GitHub with:

# install.packages("devtools")
devtools::install_github("rubensmoura87/MultiATSM")

You can also get the official release version from CRAN

install.packages("MultiATSM")

Usage and documentation

  • See the package manual and vignettes for detailed function documentation and examples.
  • Main functions: InputsForOpt, Bias_Correc_VAR, Optimization, NumOutputs, Bootstrap, ForecastYields, etc.

More information about {MultiATSM}

You can learn more about the underlying models in the paper published in the R Journal.

References

  • Bauer, M. D., Rudebusch, G. D., & Wu, J. C. (2012). Correcting estimation bias in dynamic term structure models. Journal of Business & Economic Statistics, 30(3), 454-467.

  • Candelon, B., & Moura, R. (2023). Sovereign yield curves and the COVID-19 in emerging markets. Economic Modelling, 127, 106453.

  • Candelon, B., & Moura, R. (2024). A Multicountry Model of the Term Structures of Interest Rates with a GVAR. Journal of Financial Econometrics, 22(5), 1558-1587.

  • Joslin, S., Priebsch, M., & Singleton, K. J. (2014). Risk premiums in dynamic term structure models with unspanned macro risks. The Journal of Finance, 69(3), 1197-1233.

  • Joslin, S., Singleton, K. J., & Zhu, H. (2011). A new perspective on Gaussian dynamic term structure models. The Review of Financial Studies, 24(3), 926-970.

  • Jotikasthira, C., Le, A., & Lundblad, C. (2015). Why do term structures in different currencies co-move?. Journal of Financial Economics, 115(1), 58-83.

  • Moura, R (2025). MultiATSM: An R Package for Arbitrage-Free Macrofinance Multicountry Affine Term Structure Models. The R Journal, 17(4), 275-304.

  • See also the references in the package documentation.

Metadata

Version

1.5.1-1

License

Unknown

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