Autoregressive Wild Bootstrap Inference for Nonparametric Trends
Author
Abstract
Suggested Citation
DOI: 10.26481/umagsb.2017010
Download full text from publisher
Other versions of this item:
- Friedrich, Marina & Smeekes, Stephan & Urbain, Jean-Pierre, 2020. "Autoregressive wild bootstrap inference for nonparametric trends," Journal of Econometrics, Elsevier, vol. 214(1), pages 81-109.
- Marina Friedrich & Stephan Smeekes & Jean-Pierre Urbain, 2018. "Autoregressive Wild Bootstrap Inference for Nonparametric Trends," Papers 1807.02357, arXiv.org, revised Nov 2019.
References listed on IDEAS
- Wei Biao Wu & Zhibiao Zhao, 2007. "Inference of trends in time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(3), pages 391-410, June.
- Davidson, Russell & Flachaire, Emmanuel, 2008.
"The wild bootstrap, tamed at last,"
Journal of Econometrics, Elsevier, vol. 146(1), pages 162-169, September.
- Davidson, R. & Flachaire, E., 1999. "The Wild Bootstrap, Tamed at Last," G.R.E.Q.A.M. 99a32, Universite Aix-Marseille III.
- Russell Davidson & Emmanuel Flachaire, 2008. "The wild bootstrap, tamed at last," Post-Print hal-00649250, HAL.
- Emmanuel Flachaire & Russell Davidson, 2001. "The Wild Bootstrap, Tamed At Last," Working Paper 1000, Economics Department, Queen's University.
- Emmanuel Flachaire, 2001. "The Wild Bootstrap, Tamed at Last," STICERD - Distributional Analysis Research Programme Papers 58, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- Davidson, Russell & Flachaire, Emmanuel, 2001. "The wild bootstrap, tamed at last," LSE Research Online Documents on Economics 6560, London School of Economics and Political Science, LSE Library.
- Russell Davidson & Emmanuel Flachaire, 2000. "The Wild Bootstrap, Tamed at Last," Econometric Society World Congress 2000 Contributed Papers 1413, Econometric Society.
- Leucht, Anne & Neumann, Michael H., 2013. "Dependent wild bootstrap for degenerate U- and V-statistics," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 257-280.
- Efstathios Paparoditis & Dimitris N. Politis, 2002. "The tapered block bootstrap for general statistics from stationary sequences," Econometrics Journal, Royal Economic Society, vol. 5(1), pages 131-148, June.
- Smeekes, S. & Urbain, J.R.Y.J., 2014. "A multivariate invariance principle for modified wild bootstrap methods with an application to unit root testing," Research Memorandum 008, Maastricht University, Graduate School of Business and Economics (GSBE).
- Newey, Whitney K, 1991.
"Uniform Convergence in Probability and Stochastic Equicontinuity,"
Econometrica, Econometric Society, vol. 59(4), pages 1161-1167, July.
- Newey, W.K., 1989. "Uniform Convergence In Probability And Stochastic Equicontinuity," Papers 342, Princeton, Department of Economics - Econometric Research Program.
- Andrews, Donald W.K., 1992.
"Generic Uniform Convergence,"
Econometric Theory, Cambridge University Press, vol. 8(2), pages 241-257, June.
- Donald W.K. Andrews, 1990. "Generic Uniform Convergence," Cowles Foundation Discussion Papers 940, Cowles Foundation for Research in Economics, Yale University.
- Shao, Xiaofeng, 2010. "The Dependent Wild Bootstrap," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 218-235.
- Ross R. McKitrick & Timothy J. Vogelsang, 2014. "HAC robust trend comparisons among climate series with possible level shifts," Environmetrics, John Wiley & Sons, Ltd., vol. 25(7), pages 528-547, November.
- Palm, Franz C. & Smeekes, Stephan & Urbain, Jean-Pierre, 2011.
"Cross-sectional dependence robust block bootstrap panel unit root tests,"
Journal of Econometrics, Elsevier, vol. 163(1), pages 85-104, July.
- Palm, F.C. & Smeekes, S. & Urbain, J.R.Y.J., 2008. "Cross-sectional dependence robust block bootstrap panel unit root tests," Research Memorandum 048, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
- Hansen, Bruce E., 1991. "Strong Laws for Dependent Heterogeneous Processes," Econometric Theory, Cambridge University Press, vol. 7(2), pages 213-221, June.
- Cai, Zongwu, 2007. "Trending time-varying coefficient time series models with serially correlated errors," Journal of Econometrics, Elsevier, vol. 136(1), pages 163-188, January.
- Peter Hall & Joel L. Horowitz, 2013. "A simple bootstrap method for constructing nonparametric confidence bands for functions," CeMMAP working papers CWP29/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Jansson, Michael, 2002. "Consistent Covariance Matrix Estimation For Linear Processes," Econometric Theory, Cambridge University Press, vol. 18(6), pages 1449-1459, December.
- Joel R. Norris & Robert J. Allen & Amato T. Evan & Mark D. Zelinka & Christopher W. O’Dell & Stephen A. Klein, 2016. "Evidence for climate change in the satellite cloud record," Nature, Nature, vol. 536(7614), pages 72-75, August.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "Temperature in the Iberian Peninsula: Trend, seasonality, and heterogeneity," Papers 2406.14145, arXiv.org.
- Marina Friedrich & Sébastien Fries & Michael Pahle & Ottmar Edenhofer, 2020. "Rules vs. Discretion in Cap-and-Trade Programs: Evidence from the EU Emission Trading System," CESifo Working Paper Series 8637, CESifo.
- Beutner, Eric & Heinemann, Alexander & Smeekes, Stephan, 2024.
"A residual bootstrap for conditional Value-at-Risk,"
Journal of Econometrics, Elsevier, vol. 238(2).
- Eric Beutner & Alexander Heinemann & Stephan Smeekes, 2018. "A Residual Bootstrap for Conditional Value-at-Risk," Papers 1808.09125, arXiv.org, revised Aug 2023.
- Marina Friedrich & Eric Beutner & Hanno Reuvers & Stephan Smeekes & Jean-Pierre Urbain & Whitney Bader & Bruno Franco & Bernard Lejeune & Emmanuel Mahieu, 2020.
"A statistical analysis of time trends in atmospheric ethane,"
Climatic Change, Springer, vol. 162(1), pages 105-125, September.
- Marina Friedrich & Eric Beutner & Hanno Reuvers & Stephan Smeekes & Jean-Pierre Urbain & Whitney Bader & Bruno Franco & Bernard Lejeune & Emmanuel Mahieu, 2019. "A statistical analysis of time trends in atmospheric ethane," Papers 1903.05403, arXiv.org, revised Jun 2020.
- Friedrich, Marina & Lin, Yicong, 2024. "Sieve bootstrap inference for linear time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 239(1).
- Marina Friedrich & Luca Margaritella & Stephan Smeekes, 2023. "High-Dimensional Granger Causality for Climatic Attribution," Papers 2302.03996, arXiv.org, revised Jun 2024.
- Yayi Yan & Jiti Gao & Bin peng, 2020. "A Class of Time-Varying Vector Moving Average (infinity) Models," Monash Econometrics and Business Statistics Working Papers 39/20, Monash University, Department of Econometrics and Business Statistics.
- González-Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir, 2023. "Modelling intervals of minimum/maximum temperatures in the Iberian Peninsula," DES - Working Papers. Statistics and Econometrics. WS 37968, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Marina Friedrich & S'ebastien Fries & Michael Pahle & Ottmar Edenhofer, 2019. "Understanding the explosive trend in EU ETS prices -- fundamentals or speculation?," Papers 1906.10572, arXiv.org, revised Mar 2020.
- Yicong Lin & Mingxuan Song, 2023. "Robust bootstrap inference for linear time-varying coefficient models: Some Monte Carlo evidence," Tinbergen Institute Discussion Papers 23-049/III, Tinbergen Institute.
- Yayi Yan & Jiti Gao & Bin Peng, 2020. "A Class of Time-Varying Vector Moving Average Models: Nonparametric Kernel Estimation and Application," Papers 2010.01492, arXiv.org.
- Giannerini, Simone & Goracci, Greta & Rahbek, Anders, 2024. "The validity of bootstrap testing for threshold autoregression," Journal of Econometrics, Elsevier, vol. 239(1).
- Yayi Yan & Jiti Gao & Bin Peng, 2021. "Asymptotics for Time-Varying Vector MA(∞) Processes," Monash Econometrics and Business Statistics Working Papers 22/21, Monash University, Department of Econometrics and Business Statistics.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Smeekes, S. & Urbain, J.R.Y.J., 2014. "A multivariate invariance principle for modified wild bootstrap methods with an application to unit root testing," Research Memorandum 008, Maastricht University, Graduate School of Business and Economics (GSBE).
- Hounyo, Ulrich & Varneskov, Rasmus T., 2020. "Inference for local distributions at high sampling frequencies: A bootstrap approach," Journal of Econometrics, Elsevier, vol. 215(1), pages 1-34.
- Paulo M. D. C. Parente & Richard J. Smith, 2021.
"Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models,"
Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
- Paulo M.D.C. Parente & Richard J. Smith, 2018. "Quasi-Maximum Likelihood and the Kernel Block Bootstrap for Nonlinear Dynamic Models," Working Papers REM 2018/59, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
- Paulo Parente & Richard J. Smith, 2019. "Quasi-maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," CeMMAP working papers CWP60/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Paul Doukhan & Gabriel Lang & Anne Leucht & Michael H. Neumann, 2015.
"Recent developments in bootstrap methods for dependent data,"
Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 290-314, May.
- Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Michael Wolf & Dan Wunderli, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 352-376, May.
- Helmut Herwartz & Yabibal M. Walle, 2018. "A powerful wild bootstrap diagnosis of panel unit roots under linear trends and time-varying volatility," Computational Statistics, Springer, vol. 33(1), pages 379-411, March.
- Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation and Inference for a Class of Generalized Hierarchical Models," Papers 2311.02789, arXiv.org, revised Apr 2024.
- Ulrich Hounyo & Rasmus T. Varneskov, 2018. "Inference for Local Distributions at High Sampling Frequencies: A Bootstrap Approach," CREATES Research Papers 2018-16, Department of Economics and Business Economics, Aarhus University.
- Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation of Semiparametric Multi-Index Models Using Deep Neural Networks," Monash Econometrics and Business Statistics Working Papers 21/23, Monash University, Department of Econometrics and Business Statistics.
- Cavaliere, Giuseppe & Nielsen, Morten Ørregaard & Taylor, A.M. Robert, 2015.
"Bootstrap score tests for fractional integration in heteroskedastic ARFIMA models, with an application to price dynamics in commodity spot and futures markets,"
Journal of Econometrics, Elsevier, vol. 187(2), pages 557-579.
- Giuseppe Cavaliere & Morten Ø. Nielsen & A.M. Robert Taylor, 2013. "Bootstrap Score Tests For Fractional Integration In Heteroskedastic Arfima Models, With An Application To Price Dynamics In Commodity Spot And Futures Markets," Working Paper 1309, Economics Department, Queen's University.
- Giuseppe Cavaliere & Morten Ørregaard Nielsen & A.M. Robert Taylor, 2014. "Bootstrap Score Tests for Fractional Integration in Heteroskedastic ARFIMA Models, with an Application to Price Dynamics in Commodity Spot and Futures Markets," CREATES Research Papers 2014-22, Department of Economics and Business Economics, Aarhus University.
- Pedroni, Peter L. & Vogelsang, Timothy J. & Wagner, Martin & Westerlund, Joakim, 2015.
"Nonparametric rank tests for non-stationary panels,"
Journal of Econometrics, Elsevier, vol. 185(2), pages 378-391.
- Pedroni, Peter & Vogelsang, Timothy J. & Wagner, Martin & Westerlund, Joakim, 2011. "Nonparametric Rank Tests for Non-stationary Panels," Economics Series 270, Institute for Advanced Studies.
- Ghysels, Eric & Guay, Alain, 2004.
"Testing For Structural Change In The Presence Of Auxiliary Models,"
Econometric Theory, Cambridge University Press, vol. 20(6), pages 1168-1202, December.
- Eric Ghysels & Alain Guay, 2001. "Testing for Structural Change in the Presence of Auxiliary Models," Cahiers de recherche CREFE / CREFE Working Papers 133, CREFE, Université du Québec à Montréal.
- Eric Ghysels & Alain Guay, 2001. "Testing for Structural Change in the Presence of Auxiliary Models," CIRANO Working Papers 2001s-54, CIRANO.
- Ngai Hang Chan & Linhao Gao & Wilfredo Palma, 2022. "Simultaneous variable selection and structural identification for time‐varying coefficient models," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 511-531, July.
- Cheng, Wei & Lee, Lung-fei, 2017. "Testing endogeneity of spatial and social networks," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 81-97.
- Friedrich, Marina & Lin, Yicong, 2024. "Sieve bootstrap inference for linear time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 239(1).
- Lee, Taewook & Baek, Changryong, 2020. "Block wild bootstrap-based CUSUM tests robust to high persistence and misspecification," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
- Antonio Merlo & Áureo de Paula, 2017.
"Identification and Estimation of Preference Distributions When Voters Are Ideological,"
The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(3), pages 1238-1263.
- Antonio Merlo & Aureo de Paula, 2010. "Identification and Estimation of Preference Distributions When Voters Are Ideological," PIER Working Paper Archive 11-001, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Antonio Merlo & Áureo de Paula, 2015. "Identification and estimation of preference distributions when voters are ideological," CeMMAP working papers 50/15, Institute for Fiscal Studies.
- Merlo, Antonio & de Paula, Aureo, 2015. "Identification and Estimation of Preference Distributions When Voters Are Ideological," CEPR Discussion Papers 10821, C.E.P.R. Discussion Papers.
- Antonio Merlo & Áureo de Paula, 2013. "Identification and estimation of preference distributions when voters are ideological," CeMMAP working papers 51/13, Institute for Fiscal Studies.
- Antonio Merlo & Áureo de Paula, 2015. "Identification and estimation of preference distributions when voters are ideological," CeMMAP working papers CWP50/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Antonio Merlo & Áureo de Paula, 2013. "Identification and estimation of preference distributions when voters are ideological," CeMMAP working papers CWP51/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Merlo, Antonio & de Paula, Aureo, 2015. "Identification and Estimation of Preference Distributions When Voters Are Ideological," Working Papers 15-007, Rice University, Department of Economics.
- Jenish, Nazgul & Prucha, Ingmar R., 2009. "Central limit theorems and uniform laws of large numbers for arrays of random fields," Journal of Econometrics, Elsevier, vol. 150(1), pages 86-98, May.
- Degui Li & Bin Peng & Songqiao Tang & Weibiao Wu, 2023. "Inference of Grouped Time-Varying Network Vector Autoregression Models," Monash Econometrics and Business Statistics Working Papers 5/23, Monash University, Department of Econometrics and Business Statistics.
- Chen, Bin, 2015. "Modeling and testing smooth structural changes with endogenous regressors," Journal of Econometrics, Elsevier, vol. 185(1), pages 196-215.
- Ramdan Dridi, 2000. "Simulated Asymptotic Least Squares Theory," STICERD - Econometrics Paper Series 396, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
More about this item
JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2017-05-14 (Econometrics)
- NEP-ETS-2017-05-14 (Econometric Time Series)
- NEP-ORE-2017-05-14 (Operations Research)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:unm:umagsb:2017010. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Andrea Willems or Leonne Portz (email available below). General contact details of provider: https://edirc.repec.org/data/meteonl.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.