Norman R. Swanson

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Valentina CorradiUniversity of SurreyEmail confirmado em surrey.ac.uk
John C. Chao*Department of Economics, University of MarylandEmail confirmado em econ.umd.edu
Tiemen WoutersenUniversity of ArizonaEmail confirmado em email.arizona.edu
Hyun Hak KimKoomin UniversityEmail confirmado em kookmin.ac.kr
Eric GhyselsBernstein Distinguished Professor of Economics and Professor of Finance, UNC Chapel HillEmail confirmado em unc.edu
Whitney NeweyProfessor of Economics, MITEmail confirmado em mit.edu
Philip Hans FransesErasmus School of EconomicsEmail confirmado em ese.eur.nl
Mark WatsonDepartment of Economics, Princeton UniversityEmail confirmado em princeton.edu
Xiye Yang (杨希野)Rutgers UniversityEmail confirmado em econ.rutgers.edu
Oleg KorenokProfessor, Virginia Commonwealth UniversityEmail confirmado em vcu.edu
Michael P. ClementsProfessor of Econometrics, Reading UniversityEmail confirmado em reading.ac.uk
Diep (ND) DuongSenior Financial Economist - Office of the Comptroller of the CurrencyEmail confirmado em occ.treas.gov
Jörg BreitungUniversity of CologneEmail confirmado em statistik.uni-koeln.de
Xiaohong ChenProfessor of Economics, Yale UniversityEmail confirmado em yale.edu
Walter DistasoImperial College Business SchoolEmail confirmado em imperial.ac.uk
Dick van DijkProfessor of Financial Econometrics, Erasmus University RotterdamEmail confirmado em ese.eur.nl
Andrés FernándezIMFEmail confirmado em imf.org
Lance BachmeierKansas State UniversityEmail confirmado em ksu.edu
Ataman OzyildirimThe Conference BoardEmail confirmado em conference-board.org
Oguzhan CepniCopenhagen Business SchoolEmail confirmado em cbs.dk

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Norman R. Swanson

Distinguished Professor of Economics, Rutgers University

Email confirmado em econ.rutgers.edu - Página inicial

Econometrics time series financial econometrics macro econometrics forecasting


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
Impulse response functions based on a causal approach to residual orthogonalization in vector autoregressions NR Swanson, CWJ Granger Journal of the American Statistical Association 92 (437), 357-367, 1997	663	1997
A model selection approach to real-time macroeconomic forecasting using linear models and artificial neural networks NR Swanson, H White Review of Economics and Statistics 79 (4), 540-550, 1997	526	1997
Consistent estimation with a large number of weak instruments JC Chao, NR Swanson Econometrica 73 (5), 1673-1692, 2005	443	2005
Money and output viewed through a rolling window NR Swanson Journal of monetary Economics 41 (3), 455-474, 1998	394	1998
Forecasting economic and financial time-series with non-linear models MP Clements, PH Franses, NR Swanson International journal of forecasting 20 (2), 169-183, 2004	383	2004
A model-selection approach to assessing the information in the term structure using linear models and artificial neural networks NR Swanson, H White Journal of Business & Economic Statistics 13 (3), 265-275, 1995	381	1995
Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models NR Swanson, H White International journal of Forecasting 13 (4), 439-461, 1997	372	1997
Predictive Density Evaluation, in Handbook of Economic Forecasting, CWJ Granger, G. Elliott and A. Timmermann, eds V Corradi, NR Swanson Elsevier: Amsterdam, 197&284, 2006	303*	2006
An introduction to stochastic unit-root processes CWJ Granger, NR Swanson Journal of Econometrics 80 (1), 35-62, 1997	291	1997
Weak instrument robust tests in GMM and the new Keynesian Phillips curve F Kleibergen, S Mavroeidis Journal of Business & Economic Statistics 27 (3), 293-311, 2009	255	2009
An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series G Bhardwaj, NR Swanson Journal of econometrics 131 (1-2), 539-578, 2006	221	2006
Instrumental variable estimation with heteroskedasticity and many instruments JA Hausman, WK Newey, T Woutersen, JC Chao, NR Swanson Quantitative Economics 3 (2), 211-255, 2012	214	2012
Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence HH Kim, NR Swanson Journal of Econometrics 178, 352-367, 2014	194	2014
Semiparametric ARX neural-network models with an application to forecasting inflation X Chen, J Racine, NR Swanson IEEE Transactions on neural networks 12 (4), 674-683, 2001	162	2001
Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods HH Kim, NR Swanson International Journal of Forecasting 34 (2), 339-354, 2018	159*	2018
Asymptotic distribution of JIVE in a heteroskedastic IV regression with many instruments JC Chao, NR Swanson, JA Hausman, WK Newey, T Woutersen Econometric Theory 28 (1), 42-86, 2012	159*	2012
Predictive density and conditional confidence interval accuracy tests V Corradi, NR Swanson Journal of Econometrics 135 (1-2), 187-228, 2006	158*	2006
FUTURE DEVELOPMENTS IN THE STUDY OF COINTEGRATED VARIABLES^* CWJ Granger, N Swanson Oxford Bulletin of Economics and Statistics 58 (3), 537-553, 1996	145	1996
Out-of-sample tests for Granger causality J Chao, V Corradi, NR Swanson Macroeconomic Dynamics 5 (4), 598-620, 2001	144	2001
Bootstrap conditional distribution tests in the presence of dynamic misspecification V Corradi, NR Swanson Journal of Econometrics 133 (2), 779-806, 2006	140	2006

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