B. Bernanke, J. Boivin, and P. S. Eliasz. Measuring the effects of monetary policy: A factor-augmented vector autoregressive (FAVAR) approach. The Quarterly Journal of Economics, 120(1):387–422, 2005.
C. A. Sims. Macroeconomics and reality. Econometrica, 48:1–48, 1980.
C. Kascha and C. Trenkler. Simple identification and specification of cointegrated VARMA models. Journal of Applied Econometrics, 2014. Forthcoming.
C. Kascha. A comparison of estimation methods for vector autoregressive moving-average models. Econometric Reviews, 31(3):297–324, 2012.
D. Korobilis. Assessing the transmission of monetary policy shocks using time-varying parameter dynamic factor models. Oxford Bulletin of Economics and Statistics, 2012. Forthcoming.
D. Korobilis. VAR forecasting using Bayesian variable selection. Journal of Applied Econometrics, 28(2):204–230, 2013.
- D. P. Kroese and J. C. C. Chan. Statistical Modeling and Computation. Springer, New York, 2014.
Paper not yet in RePEc: Add citation now
- D. P. Kroese, T. Taimre, and Z. I. Botev. Handbook of Monte Carlo Methods. John Wiley & Sons, New York, 2011. E. M. Leeper, T. B. Walker, and S.-C. S. Yang. Fiscal foresight: Analytics and econometrics.
Paper not yet in RePEc: Add citation now
D. S. Poskitt. Vector autoregresive moving average identification for macroeconomic modeling: A new methodology. Working Paper, Monash University, 2011. G. E. Primiceri. Time varying structural vector autoregressions and monetary policy.
E. Eisenstat, J. C. C. Chan, and R. W. Strachan. Model specification search for timevarying parameter VARs. Econometric Reviews, 2014. Forthcoming. J. C. Engwerda, A. C. M. Ran, and A. L. Rijkeboer. Necessary and sufficient conditions for the existence of a positive definite solution of the matrix equation X+A∗ X−1 A = Q.
- E. I. George and R. E. McCulloch. Variable selection via Gibbs sampling. Journal of the American Statistical Association, 88(423):881–889, 1993.
Paper not yet in RePEc: Add citation now
F. Canova. Modelling and forecasting exchange rates with a Bayesian time-varying coefficient model. Journal of Economic Dynamics and Control, 17:233–261, 1993.
G. Athanasopoulos, D. S. Poskitt, and F. Vahid. Two canonical VARMA forms: Scalar component models vis-` a-vis the echelon form. Econometric Reviews, 31(1):60–83, 2012. M. Banbura, D. Giannone, and L. Reichlin. Large Bayesian vector auto regressions.
G. Koop and D. Korobilis. Bayesian multivariate time series methods for empirical macroeconomics. Foundations and Trends in Econometrics, 3(4):267–358, 2010.
G. Koop and D. Korobilis. Large time-varying parameter VARs. Journal of Econometrics, 177(2):185–198, 2013. G. Koop and S. M. Potter. Estimation and forecasting in models with multiple breaks.
G. Koop. Forecasting with medium and large Bayesian VARs. Journal of Applied Econometrics, 2011. DOI: 10.1002/jae.1270.
H. L utkepohl and D. S. Poskitt. Specification of echelon-form VARMA models. Journal of Business & Economic Statistics, 14(1):69–79, 1996.
H. L utkepohl and H. Claessen. Analysis of cointegrated VARMA processes. Journal of Econometrics, 80:223–239, 1997.
- H. L utkepohl. New Introduction to Multiple Time Series Analysis. Springer-Verlag, Berlin, 2005.
Paper not yet in RePEc: Add citation now
- J. C. C. Chan and I. Jeliazkov. Efficient simulation and integrated likelihood estimation in state space models. International Journal of Mathematical Modelling and Numerical Optimisation, 1:101–120, 2009.
Paper not yet in RePEc: Add citation now
J. C. C. Chan, E. Eisenstat, and G. Koop. Large Bayesian VARMAs. Working Paper, 2015. T. E. Clark and T. Doh. A Bayesian evaluation of alternative models of trend inflation.
J. C. C. Chan, G. Koop, R. Leon-Gonzalez, and R. Strachan. Time varying dimension models. Journal of Business and Economic Statistics, 30:358–367, 2012.
J. C. C. Chan. Moving average stochastic volatility models with application to inflation forecast. Journal of Econometrics, 176(2):162–172, 2013.
J. Fern andez-Villaverde, J. F. Rubio-Ram ırez, T. J. Sargent, and M. W. Watson. ABCs (and Ds) of understanding VARs. American Economic Review, 97(3):1021–1026, 2007.
J. Geweke and G. Amisano. Hierarchical Markov normal mixture models with applications to financial asset returns. Journal of Applied Econometrics, 26:1–29, 2011. E. J. Hannan. The identification and parameterization of ARMAX and state space forms.
J. H. Stock and M. W. Watson. Why has U.S. inflation become harder to forecast? Journal of Money Credit and Banking, 39:3–33, 2007.
- J.-M. Dufour and D. Pelletier. Practical methods for modelling weak VARMA processes: identification, estimation and specification with a macroeconomic application. Discussion Paper, McGill University, 2014.
Paper not yet in RePEc: Add citation now
J.-M. Dufour and D. Stevanovi c. Factor-augmented VARMA models with macroeconomic applications. Journal of Business & Economic Statistics, 31(4):491–506, 2013.
K. Metaxoglou and A. Smith. Maximum likelihood estimation of VARMA models using a state-space EM algorithm. Journal of Time Series Analysis, 28(5):666–685, 2007.
- Linear Algebra and its Applications, 186:255–275, 1993.
Paper not yet in RePEc: Add citation now
M. S. Peiris. On the study of some functions of multivariate ARMA processes. Journal of Time Series Analysis, 25(1):146–151, 1988.
- N.i Ravishanker and B. K. Ray. Bayesian analysis of vector ARMA models using Gibbs sampling. Journal of Forecasting, 16(3):177–194, 1997.
Paper not yet in RePEc: Add citation now
- NBER Working Paper 14028, 2008. H. Li and R. S. Tsay. A unified approach to identifying multivariate time series models.
Paper not yet in RePEc: Add citation now
R. Paap and H. van Dijk. Bayes estimates of Markov trends in possibly cointegrated series: An application to us consumption and income. Journal of Business and Economic Statistics, 21:547–563, 2003.
- Review of Economic Studies, 74:763–789, 2007.
Paper not yet in RePEc: Add citation now
S. An and F. Schorfheide. Bayesian analysis of DSGE models. Econometric Reviews, 26 (2-4):113–172, 2007. G. Athanasopoulos and F. Vahid. VARMA versus VAR for macroeconomic forecasting.
S. Kim, N. Shepherd, and S. Chib. Stochastic volatility: Likelihood inference and comparison with ARCH models. Review of Economic Studies, 65(3):361–393, 1998.
S.-C. S. Yang. Quantifying tax effects under policy foresight. Journal of Monetary Economics, 52(8):1557 – 1568, 2005.
T. Cogley and T. J. Sargent. Drifts and volatilities: monetary policies and outcomes in the post WWII US. Review of Economic Dynamics, 8(2):262 – 302, 2005.
T. F. Cooley and M. Dwyer. Business cycle analysis without much theory: A look at structural VARs. Journal of Econometrics, 83(12):57–88, 1998.