View Item 

Show simple item record

Frequentist inference in weakly identified dynamic stochastic general equilibrium models: Acronyms must be spelled out in titles for indexing purposes

dc.contributor.authorGuerron‐quintana, Pabloen_US
dc.contributor.authorInoue, Atsushien_US
dc.contributor.authorKilian, Lutzen_US
dc.date.accessioned2013-08-02T20:51:30Z
dc.date.available2014-09-02T14:12:53Zen_US
dc.date.issued2013-07en_US
dc.identifier.citationGuerron‐quintana, Pablo ; Inoue, Atsushi; Kilian, Lutz (2013). "Frequentist inference in weakly identified dynamic stochastic general equilibrium models: Acronyms must be spelled out in titles for indexing purposes." Quantitative Economics 4(2). <http://hdl.handle.net/2027.42/99029>en_US
dc.identifier.issn1759-7323en_US
dc.identifier.issn1759-7331en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/99029
dc.publisherBlackwell Publishing Ltden_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherC32en_US
dc.subject.otherDSGE Modelsen_US
dc.subject.otherIdentificationen_US
dc.subject.otherInferenceen_US
dc.subject.otherConfidence Setsen_US
dc.subject.otherBayes Factoren_US
dc.subject.otherLikelihood Ratioen_US
dc.subject.otherC52en_US
dc.subject.otherE30en_US
dc.subject.otherE50en_US
dc.titleFrequentist inference in weakly identified dynamic stochastic general equilibrium models: Acronyms must be spelled out in titles for indexing purposesen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelEconomicsen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumUniversity of Michigan and CEPR; lkilian@umich.eduen_US
dc.contributor.affiliationotherFederal Reserve Bank of Philadelphia; pguerron@gmail.comen_US
dc.contributor.affiliationotherNorth Carolina State University; atsushi@ncsu.eduen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/99029/1/QE306.pdf
dc.identifier.doi10.3982/QE306en_US
dc.identifier.sourceQuantitative Economicsen_US
dc.identifier.citedreferenceEdwards, W., H. Lindman, and L. J. Savage ( 1963 ), “ Bayesian statistical inference for psychological research. Psychological Review, 70, 193 – 242. DOI: 10.1037/h0044139en_US
dc.identifier.citedreferenceDufour, J.‐M. and M. Taamouti ( 2005 ), “ Projection‐based statistical inference in linear structural models with possibly weak instruments. Econometrica, 73, 1351 – 1365. DOI: 10.1111/j.1468-0262.2005.00618.xen_US
dc.identifier.citedreferenceFernández‐Villaverde, J., P. A. Guerron‐Quintana, and J. F. Rubio‐Ramírez ( 2010 ), “ The new macroeconometrics: A Bayesian approach.” In Handbook of Applied Bayesian Analysis, 366 – 399, Oxford University Press, Oxford.en_US
dc.identifier.citedreferenceFernández‐Villaverde, J., J. F. Rubio‐Ramírez, T. J. Sargent, and M. W. Watson ( 2007 ), “ A, B, C 's (and D 's) for understanding VARs. American Economic Review, 97, 1021 – 1026. DOI: 10.1257/aer.97.3.1021en_US
dc.identifier.citedreferenceFukač, M., D. F. Waggoner, and T. Zha ( 2007 ), “ Local and global identification of DSGE models: A simultaneous‐equation approach.” Unpublished manuscript, Reserve Bank of New Zealand and Federal Reserve Bank of Atlanta.en_US
dc.identifier.citedreferenceGelfand, A. and A. Smith ( 1990 ), “ Sampling based approaches to calculating marginal densities. Journal of American Statistical Association, 85, 398 – 409. DOI: 10.1080/01621459.1990.10476213en_US
dc.identifier.citedreferenceGelman, A., J. B. Carlin, H. S. Stern, and D. B. Rubin ( 2004 ), Bayesian Data Analysis, second edition. Chapman & Hall/CRC, Boca Raton, FL.en_US
dc.identifier.citedreferenceGlover, K. and J. C. Willems ( 1974 ), “ Parameterizations of linear dynamical systems: Canonical forms and identifiability. IEEE Transactions on Automatic Control, AC‐19, 640 – 645. DOI: 10.1109/TAC.1974.1100711en_US
dc.identifier.citedreferenceIskrev, N. ( 2010 ), “ Local identification in DSGE models. Journal of Monetary Economics, 57, 189 – 202. DOI: 10.1016/j.jmoneco.2009.12.007en_US
dc.identifier.citedreferenceKadane, J. B. ( 1975 ), “ The role of identification in Bayesian theory.” In Studies in Bayesian Econometrics and Statistics: In Honor of Leonard J. Savage ( S. E. Fienberg and A. Zellner, eds.), 175 – 191, North‐Holland, Amsterdam.en_US
dc.identifier.citedreferenceKim, J.‐Y. ( 1998 ), “ Large sample properties of posterior densities, Bayesian information criterion and the likelihood principle in nonstationary time series models. Econometrica, 66, 359 – 380. DOI: 10.2307/2998562en_US
dc.identifier.citedreferenceKleibergen, F. and S. Mavroeidis ( 2009 ), “ Weak instrument robust tests in GMM and the new Keynesian Phillips curve. Journal of Business and Economic Statistics, 27, 293 – 311. DOI: 10.1198/jbes.2009.08280en_US
dc.identifier.citedreferenceKlenow, P. J. and O. Kryvtsov ( 2008 ), “ State‐dependent or time‐dependent pricing: Does it matter for recent U.S. inflation? Quarterly Journal of Economics, 123, 863 – 904. DOI: 10.1162/qjec.2008.123.3.863en_US
dc.identifier.citedreferenceKomunjer, I. and S. Ng ( 2009 ), “ Dynamic identification of DSGE models.” Unpublished manuscript, University of California, San Diego and Columbia University.en_US
dc.identifier.citedreferenceKoop, G., M. H. Pesaran, and R. P. Smith ( 2011 ), “ On identification of Bayesian DSGE models.” Unpublished manuscript, Cambridge University.en_US
dc.identifier.citedreferenceLe Cam, L. and G. L. Yang ( 2000 ), Asymptotics in Statistics: Some Basic Concepts, second edition. Springer‐Verlag, New York. DOI: 10.1007/978-1-4612-1166-2en_US
dc.identifier.citedreferenceMikusheva, A. ( 2007 ), “ Uniform inference in autoregressive models. Econometrica, 75, 1411 – 1452. DOI: 10.1111/j.1468-0262.2007.00798.xen_US
dc.identifier.citedreferenceMoon, H. R. and F. Schorfheide ( 2012 ), “ Bayesian and frequentist inference in partially identified models. Econometrica, 80 ( 2012 ), 755 – 782. DOI: 10.3982/ECTA8360en_US
dc.identifier.citedreferenceNakamura, E. and J. Steinsson ( 2008 ), “ Five facts about prices: A reevaluation of menu cost models. Quarterly Journal of Economics, 123, 1415 – 1464.en_US
dc.identifier.citedreferencePhillips, P. C. B. and W. Ploberger ( 1996 ), “ An asymptotic theory of Bayesian inference for time series. Econometrica, 64, 381 – 412. DOI: 10.2307/2171788en_US
dc.identifier.citedreferencePoirier, D. J. ( 1998 ), “ Revising beliefs in nonidentified models. Econometric Theory, 14, 483 – 509. DOI: 10.1017/S0266466698144043en_US
dc.identifier.citedreferenceQu, Z. ( 2011 ), “ Inference and specification testing in DSGE models with possible weak identification.” Report, Boston University.en_US
dc.identifier.citedreferenceRoberts, G., A. Gelman, and W. Gilks ( 1997 ), “ Weak convergence and optimal scaling of random walk Metropolis algorithms. The Annals of Applied Probability, 7, 110 – 120. DOI: 10.1214/aoap/1034625254en_US
dc.identifier.citedreferenceSchorfheide, F. ( 2011 ), “ Estimation and evaluation of DSGE models: Progress and challenges.” Working Paper 16781, NBER.en_US
dc.identifier.citedreferenceScott, D. W. ( 1992 ), Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley, New York. DOI: 10.1002/9780470316849en_US
dc.identifier.citedreferenceSmets, F. and R. Wouters ( 2007 ), “ Shocks and frictions in US business cycles: A Bayesian DSGE approach. American Economic Review, 97, 586 – 606. DOI: 10.1257/aer.97.3.586en_US
dc.identifier.citedreferenceStock, J. H. and M. Watson ( 2002 ), “ Has the business cycle changed and why? NBER Macroeconomics Annual, 2002, 159 – 218.en_US
dc.identifier.citedreferenceStock, J. H. and J. H. Wright ( 2000 ), “ GMM with weak identification. Econometrica, 68, 1055 – 1096. DOI: 10.1111/1468-0262.00151en_US
dc.identifier.citedreferenceWoodford, M. M. ( 2003 ), Interest and Prices: Foundations of a Theory of Monetary Policy. Princeton University Press, Princeton, NJ.en_US
dc.identifier.citedreferenceAn, S. and F. Schorfheide ( 2007 ), “ Bayesian analysis of DSGE models. Econometric Reviews, 26, 113 – 172. DOI: 10.1080/07474930701220071en_US
dc.identifier.citedreferenceAndrews, D. W. K. ( 1994 ), “ The large sample correspondence between classical hypothesis tests and Bayesian posterior odds tests. Econometrica, 62, 1207 – 1232. DOI: 10.2307/2951513en_US
dc.identifier.citedreferenceAndrews, D. W. K. and X. Cheng ( 2012 ), “ Estimation and inference with weak, semi‐strong, and strong identification. Econometrica, 80, 2153 – 2211. DOI: 10.3982/ECTA9456en_US
dc.identifier.citedreferenceAndrews, I. and A. Mikusheva ( 2012 ), “ Maximum likelihood inference in weakly identified models.” Report, MIT.en_US
dc.identifier.citedreferenceAntoine, B. and E. Renault ( 2009 ), “ Efficient GMM with nearly‐weak identification. The Econometrics Journal, 12, 135 – 171. DOI: 10.1111/j.1368-423X.2009.00279.xen_US
dc.identifier.citedreferenceBalke, N. S., S. P. A. Brown, and M. K. Yücel ( 2008 ), “ Globalization, oil price shocks and U.S. economic activity.” Unpublished manuscript, Federal Reserve Bank of Dallas.en_US
dc.identifier.citedreferenceBerger, J. O. and T. Sellke ( 1987 ), “ Testing a point null hypothesis: Irreconcilability of p values and evidence. Journal of the American Statistical Association, 82, 112 – 139.en_US
dc.identifier.citedreferenceBickel, P. J. and K. Doksum ( 2006 ), Mathematical Statistics: Basic Concepts and Selected Ideas, Vol. I, second edition. Prentice Hall, Upper Saddle River, NJ.en_US
dc.identifier.citedreferenceCanova, F. and L. Sala ( 2009 ), “ Back to square one: Identification issues in DSGE models. Journal of Monetary Economics, 56, 431 – 449. DOI: 10.1016/j.jmoneco.2009.03.014en_US
dc.identifier.citedreferenceChaudhuri, S. and E. Zivot ( 2011 ), “ A new method of projection‐based inference in GMM with weakly identified nuisance parameters. Journal of Econometrics, 164, 239 – 251.en_US
dc.identifier.citedreferenceChernozhukov, V. and H. Hong ( 2003 ), “ An MCMC approach to classical estimation. Journal of Econometrics, 115, 293 – 346. DOI: 10.1016/S0304-4076(03)00100-3en_US
dc.identifier.citedreferenceChristiano, L., M. Eichenbaum, and C. Evans ( 2005 ), “ Nominal rigidities and the dynamic effects of a shock to monetary policy. Journal of Political Economy, 113, 1 – 45. DOI: 10.1086/426038en_US
dc.identifier.citedreferenceDel Negro, M. and F. Schorfheide ( 2008 ), “ Forming priors for DSGE models (and how it affects the assessment of nominal rigidities). Journal of Monetary Economics, 55, 1191 – 1208. DOI: 10.1016/j.jmoneco.2008.09.006en_US
dc.identifier.citedreferenceDufour, J.‐M., L. Khalaf, and M. Kichian ( 2009 ), “ Structural multi‐equation macroeconomic models: Identification‐robust estimation and fit.” Working Paper 2009‐19, Bank of Canada.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
QE306.pdf
Size:
342.0KB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.