Littérature scientifique sur le sujet « Time-series analysis »
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Articles de revues sur le sujet "Time-series analysis":
Zhuravka, Fedir, Hanna Filatova, Petr Šuleř et Tomasz Wołowiec. « State debt assessment and forecasting : time series analysis ». Investment Management and Financial Innovations 18, no 1 (28 janvier 2021) : 65–75. http://dx.doi.org/10.21511/imfi.18(1).2021.06.
Bowerman, Bruce, et Jonathan D. Cryer. « Time Series Analysis ». Technometrics 29, no 2 (mai 1987) : 240. http://dx.doi.org/10.2307/1269781.
Donatelli, Richard E., Ji-Ae Park, Spencer M. Mathews et Shin-Jae Lee. « Time series analysis ». American Journal of Orthodontics and Dentofacial Orthopedics 161, no 4 (avril 2022) : 605–8. http://dx.doi.org/10.1016/j.ajodo.2021.07.013.
Potscher, Benedikt M., et James D. Hamilton. « Time Series Analysis. » Journal of the American Statistical Association 91, no 433 (mars 1996) : 439. http://dx.doi.org/10.2307/2291435.
Bakouch, Hassan S. « Time Series Analysis ». Journal of the Royal Statistical Society : Series A (Statistics in Society) 172, no 1 (janvier 2009) : 283. http://dx.doi.org/10.1111/j.1467-985x.2008.00571_4.x.
Subba Rao, T. « Time Series Analysis ». Journal of Time Series Analysis 31, no 2 (mars 2010) : 139. http://dx.doi.org/10.1111/j.1467-9892.2009.00641.x.
Breitung, Jorg, et James D. Hamilton. « Time Series Analysis. » Contemporary Sociology 24, no 2 (mars 1995) : 271. http://dx.doi.org/10.2307/2076916.
Taylor, Diana. « Time-Series Analysis ». Western Journal of Nursing Research 12, no 2 (avril 1990) : 254–61. http://dx.doi.org/10.1177/019394599001200210.
Mills, Terence C. « TIME SERIES ANALYSIS ». Journal of Economic Surveys 9, no 3 (septembre 1995) : 325–28. http://dx.doi.org/10.1111/j.1467-6419.1995.tb00120.x.
Dattalo, Patrick. « Time Series Analysis ». Journal of Community Practice 5, no 4 (30 septembre 1998) : 67–85. http://dx.doi.org/10.1300/j125v05n04_05.
Thèses sur le sujet "Time-series analysis":
Pope, Kenneth James. « Time series analysis ». Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318445.
Yin, Jiang Ling. « Financial time series analysis ». Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2492929.
Gore, Christopher Mark. « A time series classifier ». Diss., Rolla, Mo. : Missouri University of Science and Technology, 2008. http://scholarsmine.mst.edu/thesis/pdf/Gore_09007dcc804e6461.pdf.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed April 29, 2008) Includes bibliographical references (p. 53-55).
Lam, Vai Iam. « Time domain approach in time series analysis ». Thesis, University of Macau, 2000. http://umaclib3.umac.mo/record=b1446633.
Malan, Karien. « Stationary multivariate time series analysis ». Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-06132008-173800.
Huang, Naijing. « Essays in time series analysis ». Thesis, Boston College, 2015. http://hdl.handle.net/2345/bc-ir:104627.
I have three chapters in my dissertation. The first chapter is about the estimation and inference for DSGE model; the second chapter is about testing financial contagion among stock markets, and in the last chapter, I propose a new econometrics method to forecast inflation interval. This first chapter studies proper inference and asymptotically accurate structural break tests for parameters in Dynamic Stochastic General Equilibrium (DSGE) models in a maximum likelihood framework. Two empirically relevant issues may invalidate the conventional inference procedures and structural break tests for parameters in DSGE models: (i) weak identification and (ii) moderate parameter instability. DSGE literatures focus on dealing with weak identification issue, but ignore the impact of moderate parameter instability. This paper contributes to the literature via considering the joint impact of two issues in DSGE framework. The main results are: in a weakly identified DSGE model, (i) moderate instability from weakly identified parameters would not affect the validity of standard inference procedures or structural break tests; (ii) however, if strongly identified parameters are featured with moderate time-variation, the asymptotic distributions of test statistics would deviate from standard ones and would no longer be nuisance parameter free, which renders standard inference procedures and structural break tests invalid and provides practitioners misleading inference results; (iii) as long as I concentrate out strongly identified parameters, the instability impact of them would disappear as the sample size goes to infinity, which recovers the power of conventional inference procedure and structural break tests for weakly identified parameters. To illustrate my results, I simulate and estimate a modified version of the Hansen (1985) Real Business Cycle model and find that my theoretical results provide reasonable guidance for finite sample inference of the parameters in the model. I show that confidence intervals that incorporate weak identification and moderate parameter instability reduce the biases of confidence intervals that ignore those effects. While I focus on DSGE models in this paper, all of my theoretical results could be applied to any linear dynamic models or nonlinear GMM models. The second chapter, regarding the asymmetric and leptokurtic behavior of financial data, we propose a new contagion test in the quantile regression framework that is robust to model misspecification. Unlike conventional correlation-based tests, the proposed quantile contagion test allows us to investigate the stock market contagion at various quantiles, not only at the mean. We show that the quantile contagion test can detect a contagion effect that is possibly ignored by correlation-based tests. A wide range of simulation studies show that the proposed test is superior to the correlation-based tests in terms of size and power. We compare our test with correlation-based tests using three real data sets: the 1994 Tequila crisis, the 1997 Asia crisis, and the 2001 Argentina crisis. Empirical results show substantial differences between two types of tests. In the third chapter, I use Quantile Bayesian Approach-- to do the interval forecast for inflation in the semi-parametric framework. This new method introduces Bayesian solution to the quantile framework for two reasons: 1. It enables us to get more efficient quantile estimates when the informative prior is used (He and Yang (2012)); 2. We use Markov Chain Monte Carlo (MCMC) algorithm to generate samples of the posterior distribution for unknown parameters and take the mean or mode as the estimates. This MCMC estimator takes advantage of numerical integration over the standard numerical differentiation based optimization, especially when the likelihood function is complicated and multi-modal. Simulation results find better interval forecasting performance of Quantile Bayesian Approach than commonly used parametric approach
Thesis (PhD) — Boston College, 2015
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Economics
Alagon, J. « Discriminant analysis for time series ». Thesis, University of Oxford, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.375222.
Warnes, Alexis. « Diagnostics in time series analysis ». Thesis, Durham University, 1994. http://etheses.dur.ac.uk/5159/.
Chan, Hon Tsang. « Discriminant analysis of time series ». Thesis, University of Newcastle Upon Tyne, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315614.
Fulcher, Benjamin D. « Highly comparative time-series analysis ». Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:642b65cf-4686-4709-9f9d-135e73cfe12e.
Livres sur le sujet "Time-series analysis":
Madsen, Henrik. Time series analysis. Boca Raton : Chapman & Hall/CRC, 2008.
Palma, Wilfredo. Time series analysis. Hoboken : John Wiley & Sons, Inc., 2016.
D, Hamilton James. Time Series Analysis. Princeton, NJ, USA : Princeton University Press, 1994.
Ostrom, Charles. Time Series Analysis. 2455 Teller Road, Thousand Oaks California 91320 United States of America : SAGE Publications, Inc., 1990. http://dx.doi.org/10.4135/9781412986366.
Tanaka, Katsuto. Time Series Analysis. Hoboken, New Jersey : John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119132165.
Cryer, Jonathan D., et Kung-Sik Chan. Time Series Analysis. New York, NY : Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-75959-3.
Cryer, Jonathan D. Time series analysis. Boston : Duxbury Press, 1986.
Woodward, Wayne A. Applied time series analysis. Boca Raton : Chapman & Hall/CRC, 2011.
Sayrs, Lois W. Pooled time series analysis. Newbury Park, Calif : Sage Publications, 1989.
Maurice, Kendall. Time series. 3e éd. Sevenoaks : Edward Arnold, 1993.
Chapitres de livres sur le sujet "Time-series analysis":
Brandt, Siegmund. « Time Series Analysis ». Dans Data Analysis, 331–40. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03762-2_13.
Brandt, Siegmund. « Time Series Analysis ». Dans Data Analysis, 427–40. New York, NY : Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1446-5_13.
Arkes, Jeremy. « Time-series models ». Dans Regression Analysis, 287–314. 2e éd. London : Routledge, 2022. http://dx.doi.org/10.4324/9781003285007-10.
Myers, Sara A. « Time Series ». Dans Nonlinear Analysis for Human Movement Variability, 29–53. Boca Raton : Taylor & Francis, Taylor & Francis, a CRC title, part of the : CRC Press, 2018. http://dx.doi.org/10.1201/9781315370651-2.
Baltagi, Badi H. « Time-Series Analysis ». Dans Solutions Manual for Econometrics, 341–67. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03383-4_14.
Baltagi, Badi H. « Time-Series Analysis ». Dans Econometrics, 363–86. Berlin, Heidelberg : Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58714-6_14.
Chatfield, Christopher. « Time-series analysis ». Dans Problem Solving, 154–60. Boston, MA : Springer US, 1988. http://dx.doi.org/10.1007/978-1-4899-3017-0_19.
Trauth, Martin H. « Time-Series Analysis ». Dans MATLAB® Recipes for Earth Sciences, 151–213. Berlin, Heidelberg : Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46244-7_5.
Baltagi, Badi H. « Time-Series Analysis ». Dans Springer Texts in Business and Economics, 383–408. Berlin, Heidelberg : Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54548-1_14.
Schmidt, Ruth A., et Helen Wright. « Time Series Analysis ». Dans Financial Aspects of Marketing, 91–100. London : Macmillan Education UK, 1996. http://dx.doi.org/10.1007/978-1-349-25020-2_11.
Actes de conférences sur le sujet "Time-series analysis":
Kurbalija, Vladimir, et Brankica Bratic. « Time series reconstruction analysis ». Dans 2016 IEEE 8th International Conference on Intelligent Systems (IS). IEEE, 2016. http://dx.doi.org/10.1109/is.2016.7737400.
Müller, Ursula U., Anton Schick et Wolfgang Wefelmeyer. « Inference for Alternating Time Series ». Dans Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0069.
Dvořák, Marek. « Time series convolution kernel estimation ». Dans INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017). Author(s), 2018. http://dx.doi.org/10.1063/1.5044115.
Mei, Xu, et Huang Chao. « Financial time series difference analysis based on symbolic time series method ». Dans 2011 International Conference on E-Business and E-Government (ICEE). IEEE, 2011. http://dx.doi.org/10.1109/icebeg.2011.5882598.
TARQUIS, ANA M., ROSA M. BENAVENTE, ANTONIO ROMERO, JOSÉ L. GARCÍA et PHILIPPE BAVEYE. « WIND VELOCITY TIME SERIES ANALYSIS ». Dans Conference on Fractals 2002. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812777720_0040.
Kawamae, Noriaki. « Time Series Analysis Using NOC ». Dans the 25th International Conference Companion. New York, New York, USA : ACM Press, 2016. http://dx.doi.org/10.1145/2872518.2889396.
Muñoz-Diosdado, A. « Multifractal Analysis of Time Series ». Dans MODELING OF COMPLEX SYSTEMS : Seventh Granada Lectures. AIP, 2003. http://dx.doi.org/10.1063/1.1571344.
Corinaldi, Sharif, et Leon Cohen. « Time-frequency analysis of econometric time series ». Dans SPIE Fourth International Symposium on Fluctuations and Noise, sous la direction de János Kertész, Stefan Bornholdt et Rosario N. Mantegna. SPIE, 2007. http://dx.doi.org/10.1117/12.726112.
Daou, Hoda. « Identifying Influencers using Time Series Analysis ». Dans 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS). IEEE, 2019. http://dx.doi.org/10.1109/snams.2019.8931833.
He-Shan Guam et Qing-Shan Jiang. « Cluster financial time series for portfolio ». Dans 2007 International Conference on Wavelet Analysis and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4420788.
Rapports d'organisations sur le sujet "Time-series analysis":
Anderson, Theodore W. Time Series Analysis and Multivariate Statistical Analysis. Fort Belvoir, VA : Defense Technical Information Center, novembre 1988. http://dx.doi.org/10.21236/ada202273.
Anderson, Theodore W. Time Series Analysis and Multivariate Statistical Analysis. Fort Belvoir, VA : Defense Technical Information Center, septembre 1985. http://dx.doi.org/10.21236/ada161375.
Lai, Eric, Daniel Moyer, Baichuan Yuan, Eric Fox, Blake Hunter, Andrea L. Bertozzi et Jeffrey Brantingham. Topic Time Series Analysis of Microblogs. Fort Belvoir, VA : Defense Technical Information Center, octobre 2014. http://dx.doi.org/10.21236/ada610278.
Friedman, Avner, Jr Miller et Willard. Radar/Sonar and Time Series Analysis. Fort Belvoir, VA : Defense Technical Information Center, avril 1991. http://dx.doi.org/10.21236/ada238496.
Lipsett, J. J., R. D. Noble et D. D. S. Liu. Time series analysis of gamma densitometry signals. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1986. http://dx.doi.org/10.4095/302665.
Langdon, Chris. Analysis of Arabian Sea Oxygen Time Series. Fort Belvoir, VA : Defense Technical Information Center, septembre 1997. http://dx.doi.org/10.21236/ada628003.
Lewis, Peter A., et A. J. Lawrance. Reversed Residuals in Autoregressive Time Series Analysis. Fort Belvoir, VA : Defense Technical Information Center, avril 1990. http://dx.doi.org/10.21236/ada222711.
Parzen, Emanuel. Stationary Time Series Analysis Using Information and Spectral Analysis. Fort Belvoir, VA : Defense Technical Information Center, septembre 1992. http://dx.doi.org/10.21236/ada257279.
Wheat, Jr., Robert M. Chaos in Electronic Circuits : Nonlinear Time Series Analysis. Office of Scientific and Technical Information (OSTI), juillet 2003. http://dx.doi.org/10.2172/821547.
Stoffer, David S. Walsh-Fourier Analysis of Discrete-Valued Time Series. Fort Belvoir, VA : Defense Technical Information Center, novembre 1985. http://dx.doi.org/10.21236/ada166139.