references.bib

@book{wozniakBsvarsBayesianEstimation2022,
	title = {bsvars: Bayesian Estimation of Structural Vector Autoregressive Models},
	author = {{Wo{\'{z}}niak}, Tomasz},
	year = {2022},
	month = {09},
	date = {2022-09-01},
	series = {R package},
	url = {https://cran.r-project.org/package=bsvars}
}


@article{Sweeney_2023,
title={Sydney property: Australia’s $9.3 trillion housing question},
url={https://www.afr.com/property/residential/australia-s-9-3-trillion-housing-question-20230111-p5cbsu},
journal={Australian Financial Review},
author={Sweeney, Nila}, year={2023}, month={Jan}
}

@book{AK_2017, title={Manias, Panics, and Crashes: A History of Financial Crises, Seventh Edition}, ISBN={9781137525741}, publisher={Springer}, author={Aliber, Robert Z. and Kindleberger, Charles P.}, year={2017}, month={Dec} }

@article{waggoner2003a,
title = {A Gibbs sampler for structural vector autoregressions},
journal = {Journal of Economic Dynamics and Control},
volume = {28},
number = {2},
pages = {349-366},
year = {2003},
issn = {0165-1889},
doi = {https://doi.org/10.1016/S0165-1889(02)00168-9},
url = {https://www.sciencedirect.com/science/article/pii/S0165188902001689},
author = {Daniel F. Waggoner and Tao Zha},
keywords = {Simultaneity, Importance sampling, Gibbs sampler},
abstract = {Structural VAR modeling has played an important role in empirical macroeconomics. The importance sampler used in the existing literature, however, can be prohibitively inefficient for obtaining accurate finite-sample inferences. In this paper we develop a Gibbs sampler for Bayesian inferences of structural VARs that restrict the covariance matrix of reduced-form residuals. Our method is computationally efficient in comparison to the existing method and can be readily applied. We show, by examples, that inferences based on the importance sampler can seriously distort economic interpretations.}
}

@ARTICLE{waggoner2003b,
title = {Likelihood preserving normalization in multiple equation models},
author = {Waggoner, Daniel and Zha, Tao},
year = {2003},
journal = {Journal of Econometrics},
volume = {114},
number = {2},
pages = {329-347},
url = {https://EconPapers.repec.org/RePEc:eee:econom:v:114:y:2003:i:2:p:329-347}
}

@ARTICLE{arias2018a,
title = {Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications},
author = {Arias, Jonas E. and Rubio‐Ramírez, Juan F. and Waggoner, Daniel},
year = {2018},
journal = {Econometrica},
volume = {86},
number = {2},
pages = {685-720},
abstract = {In this paper, we develop algorithms to independently draw from a family of conjugate posterior distributions over the structural parameterization when sign and zero restrictions are used to identify structural vector autoregressions (SVARs). We call this family of conjugate posteriors normal‐generalized‐normal. Our algorithms draw from a conjugate uniform‐normal‐inverse‐Wishart posterior over the orthogonal reduced‐form parameterization and transform the draws into the structural parameterization; this transformation induces a normal‐generalized‐normal posterior over the structural parameterization. The uniform‐normal‐inverse‐Wishart posterior over the orthogonal reduced‐form parameterization has been prominent after the work of Uhlig (2005). We use Beaudry, Nam, and Wang's (2011) work on the relevance of optimism shocks to show the dangers of using alternative approaches to implementing sign and zero restrictions to identify SVARs like the penalty function approach. In particular, we analytically show that the penalty function approach adds restrictions to the ones described in the identification scheme.},
url = {https://EconPapers.repec.org/RePEc:wly:emetrp:v:86:y:2018:i:2:p:685-720}
}

@ARTICLE{rr2010,
title = {Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference},
author = {Rubio-Ramirez, Juan F and Waggoner, Daniel and Zha, Tao},
year = {2010},
journal = {Review of Economic Studies},
volume = {77},
number = {2},
pages = {665-696},
abstract = {Structural vector autoregressions (SVARs) are widely used for policy analysis and to provide stylized facts for dynamic stochastic general equilibrium (DSGE) models; yet no workable rank conditions to ascertain whether an SVAR is globally identified have been established. Moreover, when nonlinear identifying restrictions are used, no efficient algorithms exist for small-sample estimation and inference. This paper makes four contributions towards filling these important gaps in the literature. First, we establish general rank conditions for global identification of both identified and exactly identified models. These rank conditions are sufficient for general identification and are necessary and sufficient for exact identification. Second, we show that these conditions can be easily implemented and that they apply to a wide class of identifying restrictions, including linear and certain nonlinear restrictions. Third, we show that the rank condition for exactly identified models amounts to a straightforward counting exercise. Fourth, we develop efficient algorithms for small-sample estimation and inference, especially for SVARs with nonlinear restrictions. Copyright , Wiley-Blackwell.},
url = {https://EconPapers.repec.org/RePEc:oup:restud:v:77:y:2010:i:2:p:665-696}
}