VARsignR: Estimating VARs using sign restrictions in R

Danne, Christian (2015): VARsignR: Estimating VARs using sign restrictions in R.

Abstract

VARsignR identifies structural shocks in Vector Autoregressions (VARs) using sign restrictions. It implements Uhlig’s (2005) rejection method, Uhlig’s (2005) penalty function approach, the Rubio-Ramirez et al. (2010) rejection method, and Fry and Pagan’s (2011) median target method. This vignette shows the usage and provides some technical information on the procedures that should help users to bridge the gap between VARsignR and the underlying technical papers.

• Item Type: MPRA Paper
• Language: English
• Keywords: Sign restrictions, vector autoregression, Bayesian.
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C87 - Econometric Software; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E52 - Monetary Policy

References

Arias, J., Rubio-Ramirez, J., and Waggoner, D. (2014). Inference based on svars identified with sign and zero restrictions: Theory and applications. Federal Reserve System International Finance Discussion Papers 1100.

Bates, D., Mullen, K., Nash, J., and Varadhan, R. (2015). minqa: Derivative-free optimization algorithms by quadratic approximation. URL: https://cran.r-project.org/web/packages/minqa/.

Baumeister, C. and Hamilton, J. (2014). Sign restrictions, structural vector autoregressions, and useful prior information. NBER Working Paper No. 20741.

Bernanke, B., Gertler, M., and Watson, M. (1997). Systematic monetary policy and the effects of oil price shocks. Brookings Papers on Economic Activity, 1:91–157.

Bernanke, B. and Mihov, I. (1998). Measuring monetary policy. Quarterly Journal of Economics, 113:869–902.

Blanchard, O. and Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79:655–673.

Canova, F. (2007). Methods for applied macroeconomic research. Princeton University Press., Princeton, NJ.

Canova, F. and De Nicolo, G. (2002). Monetary disturbances matter for business fluctuations in the G-7. Journal of Monetary Economics, 49:1131–1159.

Canova, F. and Paustian, M. (2011). Business cycle measurement with some theory. Journal of Monetary Economics, 58:345–361.

Canova, F. and Pina, J. (2005). What VAR tells about DSGE models? In Deibolt, C. and Krytsou, C., editors, New Trends and Macroeconomics. Springer, New York, NY.

Del Negro, M. and Schorfheide, F. (2011). Bayesian macroeconometrics. In Geweke, J., K. G. and Van Dijk, H., editors, The Oxford Handbook of Bayesian Econometrics, 293–389. Oxford University Press, Oxford: UK.

Doan, T. (2011). RATS programs to replicate Uhlig’s VAR identification technique. URL: https://www.estima.com/procs_perl/uhligjme2005.zip.

Fasiolo, M., van den Berg, T., Maurer, J., and Watanabe, S. (2015). mvnfast package. URL: https://cran.r- project.org/web/packages/mvnfast.

Faust, J. (1998). On the robustness of the identified VAR conclusions about money. Carnegie-Rochester Conference Series on Public Policy, 49:207–244.

Fry, R. and Pagan, A. (2011). Sign restrictions in structural vector autoregressions: A critical review. Journal of Economic Literature, 49:938–960.

Gali, J. (1992). How well does the IS-LM model fit postwar U.S. data. Quarterly Journal of Economics, 107:709–738.

Kilian, L. and Murphy, D. (2012). Why agnostic sign restrictions are not enough: Understanding the dynamics of oil market VAR models. Journal of the European Economic Association, 10:1166–1188.

Moon, H. and Schorfheide, F. (2012). Bayesian and frequentist inference in partially identified models. Econometrica, 80:755–782.

Moon, H., Schorfheide, F., and Granziera, E. (2011). Inference for VARs identified with sign restrictions. NBER Working Paper No. 17140.

Mountford, A. and Uhlig, H. (2009). What are the effects of fiscal policy shocks? Journal of Applied Econometrics, 24:960–992.

Paustian, M. (2007). Assessing sign restrictions. B.E. Journals of Macroeconomics, 7:1–33.

Petris, G. and Tardella, L. (2015). HI: Simulation from distributions supported by nested hyperplanes. URL: https://cran.r-project.org/web/packages/HI/.

Pfaff, B. (2015). vars: VAR modelling. URL: https://cran.r-project.org/web/packages/vars/.

Powell, M. (2002). UOBYQA: unconstrained optimization by quadratic approximation. Mathematical Programming - Series B, 92:555–582.

Rubio-Ramirez, J., Waggoner, D., and Zha, T. (2010). Structural vector autoregressions: Theory of identification and algorithms for inference. Review of Economic Studies, 77:665–696.

Sims, C. (1980). Macroeconomics and reality. Econometrica, 48:1-48.

Sims, C. (2010). VAR tools. URL: http://sims.princeton.edu/yftp/VARtools/.

Sims, C. and Zha, T. (1999). Error bands for impulse responses. Econometrica, 67:1113–1156.

Uhlig, H. (2005). What are the effects of monetary policy on output? results from an agnostic identification procedure. Journal of Monetary Economics, 52:381–419.