A Statistically Identified Structural Vector Autoregression with Endogenously Switching Volatility Regime

References

• Angelini, G., Bacchiocchi, E., Caggiano, G., and Fanelli, L. (2019), “Uncertainty Across Volatility Regimes,” Journal of Applied Econometrics, 34, 437–455. DOI: 10.1002/jae.2672.
   Web of Science ®Google Scholar
• Bacchiocchi, E., and Fanelli, L. (2015), “Identification in Structural Vector Autoregressive Models with Structural Changes, with an Application to US Monetary policy,” Oxford Bulletin of Economics and Statistics, 77, 761–779. DOI: 10.1111/obes.12092.
   Web of Science ®Google Scholar
• Barth, M. J., and Ramey, V. A. (2001), “The Cost Channel of Monetary Transmission,” in NBER Macroeconomics Annual, eds. B. S. Bernanke and K. Rogoff (Vol. 16), pp. 199–239, Cambridge, MA: MIT Press. DOI: 10.1086/654443.
   Google Scholar
• Christiano, L. J., Eichenbaum, M., and Evans, C. L. (2005), “Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy,” Journal of Political Economy, 113, 1–45. DOI: 10.1086/426038.
   Web of Science ®Google Scholar
• Galí, J. (2015), Monetary Policy, Inflation, and the Business Cycle (2nd ed.), Princeton and Oxford: Princeton University Press.
   Google Scholar
• Hamilton, J. D. (2018), “Why You Should Never Use the Hodrick-Prescott Filter,” The Review of Economics and Statistics, 100, 831–843. DOI: 10.1162/rest_a_00706.
   Web of Science ®Google Scholar
• Herwartz, H., and Lütkepohl, H. (2014), “Structural Vector Autoregressions with Markov Switching: Combining Conventional with Statistical Identification of Shocks,” Journal of Econometrics, 183, 104–116. DOI: 10.1016/j.jeconom.2014.06.012.
   Web of Science ®Google Scholar
• Kalliovirta, L., Meitz, M., and Saikkonen, P. (2015), “A Gaussian Mixture Autoregressive Model for Univariate Time Series,” Journal of Time Series Analysis, 36, 247–266. DOI: 10.1111/jtsa.12108.
   Web of Science ®Google Scholar
• ——— (2016), “Gaussian Mixture Vector Autoregression,” Journal of Econometrics, 192, 465–498.
   Web of Science ®Google Scholar
• Kalliovirta, L., and Saikkonen, P. (2010), “Reliable Residuals for Multivariate Nonlinear Time Series Models,” Unpublished revision of HECER discussion paper No. 247.
   Google Scholar
• Kilian, L., and Lütkepohl, H. (2017), Structural Vector Autoregressive Analysis (1st ed.), Cambridge: Cambridge University Press.
   Google Scholar
• Lanne, M., and Lütkepohl, H. (2010), “Structural Vector Autoregressions With Nonnormal Residuals,” Journal of Business & Economic Statistics, 28, 159–168. DOI: 10.1198/jbes.2009.06003.
   Web of Science ®Google Scholar
• Lanne, M., Lütkepohl, H., and Maciejowska, K. (2010), “Structural Vector Autoregressions with Markov Switching,” Journal of Economic Dynamics and Control, 34, 121–131. DOI: 10.1016/j.jedc.2009.08.002.
   Web of Science ®Google Scholar
• Lanne, M., Meitz, M., and Saikkonen, P. (2017), “Identification and Estimation of Non-Gaussian Structural Vector Autoregressions,” Journal of Econometrics, 196, 288–304. DOI: 10.1016/j.jeconom.2016.06.002.
   Web of Science ®Google Scholar
• Lewis, D. (2021), enquoteIdentifying Shocks via Time-Varying Volatility, The Review of Economic Studies, 88, 3086–3124. DOI: 10.1093/restud/rdab009.
   Web of Science ®Google Scholar
• Lütkepohl, H. (2005), New Introduction to Multiple Time Series Analysis (1st ed.), Berlin: Springer.
   Google Scholar
• Lütkepohl, H., Meitz, M., Netšunajev, A., and Saikkonen, P. (2021), “Testing Identification via Heteroskedasticity in Structural Vector Autoregressive Models,” The Econometrics Journal, 24, 1–22. DOI: 10.1093/ectj/utaa008.
   Web of Science ®Google Scholar
• Lütkepohl, H., and Netšunajev, A. (2014), “Disentangling Demand and Supply Shocks in the Crude Oil Market: How to Check Sign Restrictions in Structural VARs,” Journal of Applied Econometrics, 29, 479–496. DOI: 10.1002/jae.2330.
   Web of Science ®Google Scholar
• ——— (2017), “Structural Vector Autoregressions with Smooth Transitions in Variances,” Journal of Economic Dynamics & Control, 84, 43–57.
   Web of Science ®Google Scholar
• Lütkepohl, H., and Schlaak, T. (2018), “Choosing Between Different Time-Varying Volatility Models for Structural Vector Autoregressive Analysis,” Oxford Bulletin of Economics and Statistics, 80, 715–735. DOI: 10.1111/obes.12238.
   Web of Science ®Google Scholar
• Meitz M., Preve D., Saikkonen P. (2023), “A Mixture Autoregressive Model based on Student’s t-distribution,” Communications in Statistics - Theory and Methods, 52, 499–515. DOI: 10.1080/03610926.2021.1916531.
   Web of Science ®Google Scholar
• Meitz, M., and Saikkonen, P. (2021), “Testing for Observation-Dependent Regime Switching in Mixture Autoregressive Models,” Journal of Econometrics, 222, 601–624. DOI: 10.1016/j.jeconom.2020.04.048.
   Web of Science ®Google Scholar
• Muirhead, R. (1982), Aspects of Multivariate Statistical Theory (1st ed.), Hoboken, NJ: Wiley.
   Google Scholar
• Netsunajev, A. (2013), “Reaction to Technology Shocks in Markov-Switching Structural VARs: Identification via Heteroskedasticity,” Journal of Macroeconomics, 36, 51–62. DOI: 10.1016/j.jmacro.2012.12.005.
   Web of Science ®Google Scholar
• Normandin, M., and Phaneuf, L. (2004), “Monetary Policy Shocks: Testing Identification Conditions under Time-Varying Conditional Volatility,” Journal of Monetary Economics, 51, 1217–1243. DOI: 10.1016/S0304-3932(04)00069-8.
   Web of Science ®Google Scholar
• Ramey, V. A. (2016), “Macroeconomic Shocks and Their Propagation,” in Handbook of Macroeconomics (Vol. 2), eds. J. B. Taylor and H. Uhlig, Amsterdam: Elsevier Science B.V.
   Google Scholar
• Rigobon, R. (2003), “Identification through Heteroskedasticity,” The Review of Economics and Statistics, 85, 777–792. DOI: 10.1162/003465303772815727.
   Web of Science ®Google Scholar
• Virolainen, S. (2018a), gmvarkit: Estimate Gaussian and Student’s t Mixture Vector Autoregressive Models, R package version 2.1.0 available at CRAN: https://CRAN.R-project.org/package=gmvarkit.
   Google Scholar
• ——— (2018b), uGMAR: Estimate Univariate Gaussian and Student’s t Mixture Autoregressive Models, R package version 3.4.2 available at CRAN: https://CRAN.R-project.org/package=uGMAR.
   Google Scholar
• ——— (2022), “A Mixture Autoregressive Model based on Gaussian and Student’s t-distributions,” Studies in Nonlinear Dynamics & Econometrics, 26, 559–580.
   Web of Science ®Google Scholar
• Wu, J., and Xia, F. (2016), “Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound,” Journal of Money, Credit and Banking, 48, 253–291. DOI: 10.1111/jmcb.12300.
   Web of Science ®Google Scholar