Cited by 2,649Statistical Methods and InferenceProbability and Statistical ResearchMarkov Chains and Monte Carlo Methods

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

The Stationary Bootstrap

Politis, Dimitris N., Romano, Joseph P.·1994·Journal of the American Statistical Association

DOI 10.1080/01621459.1994.10476870

Open this paper in CoChat — free account

Abstract

This article introduces a resampling procedure called the stationary bootstrap as a means of calculating standard errors of estimators and constructing confidence regions for parameters based on weakly dependent stationary observations. Previously, a technique based on resampling blocks of consecutive observations was introduced to construct confidence intervals for a parameter of the m-dimensional joint distribution of m consecutive observations, where m is fixed. This procedure has been generalized by constructing a “blocks of blocks” resampling scheme that yields asymptotically valid procedures even for a multivariate parameter of the whole (i.e., infinite-dimensional) joint distribution of the stationary sequence of observations. These methods share the construction of resampling blocks of observations to form a pseudo-time series, so that the statistic of interest may be recalculated based on the resampled data set. But in the context of applying this method to stationary data, it is natural to require the resampled pseudo-time series to be stationary (conditional on the original data) as well. Although the aforementioned procedures lack this property, the stationary procedure developed here is indeed stationary and possesses other desirable properties. The stationary procedure is based on resampling blocks of random length, where the length of each block has a geometric distribution. In this article, fundamental consistency and weak convergence properties of the stationary resampling scheme are developed.

Integrity signals

Registry

Crossref · 10.1080/01621459.1994.10476870

Cites

27 of 27 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Bootstrap methods: another look at the jackknife
Efron, B. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344552 · Cited by 17,602
Approximation Theorems of Mathematical Statistics
Serfling, R. J. (1980) · Wiley series in probability and statistics · doi:10.1002/9780470316481 · Cited by 5,908
Spectral Analysis and Time Series
Preston, D. B., & Priestley, M. B. (1983) · Technometrics · doi:10.1080/00401706.1983.10487866 · Cited by 5,050
Martingale Limit Theory and its Application.
Loynes, R. M., Hall, P. V., & Heyde, C. C. (1984) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2981599 · Cited by 2,746
A Course in Probability Theory
(1974) · Elsevier eBooks · doi:10.1016/c2009-0-22097-6 · Cited by 2,735
Time Series: Data Analysis and Theory.
Koopmans, L. H., & Brillinger, D. R. (1976) · Journal of the American Statistical Association · doi:10.2307/2285347 · Cited by 2,647
The Jackknife and the Bootstrap for General Stationary Observations
Künsch, H. R. (1989) · The Annals of Statistics · doi:10.1214/aos/1176347265 · Cited by 2,454
Martingale Limit Theory and its Application
(1980) · Elsevier eBooks · doi:10.1016/c2013-0-10818-5 · Cited by 2,073
On the Theoretical Specification and Sampling Properties of Autocorrelated Time-Series
Bartlett, M. S. (1946) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.2307/2983611 · Cited by 916
Time Series: Data Analysis and Theory.
Dickey, D. A., & Brillinger, D. R. (1982) · Journal of the American Statistical Association · doi:10.2307/2287796 · Cited by 696

Show all 27 references

Research works printed but not linked: 4

10.1080/00401706.1992.10484950 · DOI
Liu R. Y., 1992, Exploring the Limits of Bootstrap
Politis D., Computing Science and Statistics, Proceedings of the 22nd Symposium on the Interface, 98
Politis D., 1992, Exploring the Limits of Bootstrap, 263

Sorted by citations

Items 801–900 of 1,002

Testing the predictive accuracy of COVID-19 forecasts
Iacone, F. (2023) · Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) · doi:10.1016/j.ijforecast.2022.01.005 · Cited by 17
Forecasting the equity premium with frequency-decomposed technical indicators
Stein, T. (2022) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2022.12.001 · Cited by 17
Optimal Portfolio Choice with Unknown Benchmark Efficiency
Kan, R., & Wang, X. (2023) · Management Science · doi:10.1287/mnsc.2021.01767 · Cited by 17
The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach
Wilms, I., Gelper, S., & Croux, C. (2016) · European Journal of Operational Research · doi:10.1016/j.ejor.2016.03.041 · Cited by 17
Realized hedge ratio: Predictability and hedging performance
Markopoulou, C. E., Skintzi, V. D., & Refenes, A.-P. N. (2016) · International Review of Financial Analysis · doi:10.1016/j.irfa.2016.03.005 · Cited by 18
Haptic contents of a movie dynamically engage the spectator's sensorimotor cortex
Lankinen, K., Smeds, E., Tikka, P., Pihko, E., Hari, R., & Koskinen, M. (2016) · Human Brain Mapping · doi:10.1002/hbm.23295 · Cited by 17
A Spectral Domain Test for Stationarity of Spatio‐Temporal Data
Bandyopadhyay, S., Jentsch, C., & Rao, S. S. (2016) · Journal of Time Series Analysis · doi:10.1111/jtsa.12222 · Cited by 17
High‐order Corrected Estimator of Asymptotic Variance with Optimal Bandwidth
Chan, K. W., & Yau, C. Y. (2017) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12279 · Cited by 17
Methods for detecting seasonal influenza epidemics using a school absenteeism surveillance system
Ward, M. A., Stanley, A., Deeth, L. E., Deardon, R., Feng, Z. Z., & Trotz‐Williams, L. A. (2019) · BMC Public Health · doi:10.1186/s12889-019-7521-7 · Cited by 17
Network origins of portfolio risk
Zareei, A. (2019) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2019.105663 · Cited by 17
Combining Multivariate Volatility Forecasts: An Economic-Based Approach
Caldeira, J. F., Moura, G. V., Nogales, F. J., & Santos, A. A. P. (2016) · Journal of Financial Econometrics · doi:10.1093/jjfinec/nbw010 · Cited by 17
Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility
Louzis, D. P., Xanthopoulos‐Sisinis, S., & Refenes, A. N. (2011) · Applied Economics · doi:10.1080/00036846.2011.577025 · Cited by 17
Properties, formulations, and algorithms for portfolio optimization using Mean-Gini criteria
Ji, R., Lejeune, M. A., & Prasad, S. Y. (2016) · Annals of Operations Research · doi:10.1007/s10479-016-2230-4 · Cited by 17
Metals: resources or financial assets? A multivariate cross-sectional analysis
Lutzenberger, F. T., Gleich, B., Mayer, H. G., Stepanek, C., & Rathgeber, A. (2016) · Empirical Economics · doi:10.1007/s00181-016-1162-9 · Cited by 17
International Journal of Administration and Governance
Vijayakumar, M. (2015) · Cited by 17
BOOTSTRAP-ASSISTED UNIT ROOT TESTING WITH PIECEWISE LOCALLY STATIONARY ERRORS
Rho, Y., & Shao, X. (2018) · Econometric Theory · doi:10.1017/s0266466618000038 · Cited by 17
On tail fatness of macroeconomic dynamics
Liu, X. (2019) · Journal of Macroeconomics · doi:10.1016/j.jmacro.2019.103154 · Cited by 17
A DCC-type approach for realized covariance modeling with score-driven dynamics
Vassallo, D., Buccheri, G., & Corsi, F. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2020.07.006 · Cited by 17
Timescale betas and the cross section of equity returns: Framework, application, and implications for interpreting the Fama–French factors
Kang, B. U., In, F. H., & Kim, T. S. (2017) · Journal of Empirical Finance · doi:10.1016/j.jempfin.2017.01.004 · Cited by 17
Commonality in Hedge Fund Returns: Driving Factors and ImplicationsPreprint
Bussière, M., Hoerova, M., & Klaus, B. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2034894 · Cited by 17
Nonparametric Tests for Independence
Diks, C. G. H., & Meyers, R. (2011) · SpringerReference · doi:10.1007/springerreference_60541 · Cited by 17
Can We Prove a Bank Guilty of Creating Systemic Risk? A Minority ReportPreprint
Danı́elsson, J., James, K. R., Valenzuela, M., & Zer, I. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2692086 · Cited by 17
A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations: an example from the Amazon region
Rammig, A., Heinke, J., Hofhansl, F., Verbeeck, H., Baker, T. R., Christoffersen, B., Ciais, P., Deurwaerder, H. D., Fleischer, K., Galbraith, D. R., et al. (2018) · Geoscientific model development · doi:10.5194/gmd-11-5203-2018 · Cited by 17
Interpolation uncertainty of atmospheric temperature profiles
Fassò, A., Sommer, M., & von Rohden, C. (2020) · Atmospheric measurement techniques · doi:10.5194/amt-13-6445-2020 · Cited by 16
Self-sustainment of coherent structures in counter-rotating Taylor–Couette flow
Wang, B., Ayats, R., Deguchi, K., Mellibovsky, F., & Meseguer, Á. (2022) · Journal of Fluid Mechanics · doi:10.1017/jfm.2022.828 · Cited by 16
Global industry portfolios and short-term predictability of returns: Is it there?
Shynkevich, A. (2012) · Journal of Financial Markets · doi:10.1016/j.finmar.2012.05.002 · Cited by 17
Bootstrap-after-Bootstrap Model Averaging for Reducing Model Uncertainty in Model Selection for Air Pollution Mortality Studies
Roberts, S., & Martin, M. A. (2009) · Environmental Health Perspectives · doi:10.1289/ehp.0901007 · Cited by 16
Analysis of Regulatory Network Involved in Mechanical Induction of Embryonic Stem Cell Differentiation
Zhang, X., Jaramillo, M., Singh, S. S., Kumta, P. N., & Banerjee, I. (2012) · PLoS ONE · doi:10.1371/journal.pone.0035700 · Cited by 16
Testing the martingale hypothesis for futures prices: Implications for hedgers
de Goyet, C. D. V., Dhaene, G., & Sercu, P. M. F. A. (2008) · Journal of Futures Markets · doi:10.1002/fut.20343 · Cited by 16
GREAT MODERATION AND GREAT RECESSION: FROM PLAIN SAILING TO STORMY SEAS?
Rivas, M. D. G., Loscos, A. G., & Pérez‐Quirós, G. (2018) · International Economic Review · doi:10.1111/iere.12337 · Cited by 16
Transfer Entropy for Nonparametric Granger Causality Detection: An Evaluation of Different Resampling Methods
Diks, C. G. H., & Fang, H. (2017) · Entropy · doi:10.3390/e19070372 · Cited by 16
Profitability of simple stationary technical trading rules with high-frequency data of Chinese Index Futures
Chen, J.-C., Yu, Z., & Wang, X. (2017) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2017.11.088 · Cited by 16
Momentum and reversal strategies in Chinese commodity futures markets
Yang, Y., Göncü, A., & Pantelous, A. A. (2018) · International Review of Financial Analysis · doi:10.1016/j.irfa.2018.09.012 · Cited by 16
Uncertainty in the Hot Hand Fallacy: Detecting Streaky Alternatives to Random Bernoulli Sequences
Ritzwoller, D. M., & Romano, J. P. (2021) · The Review of Economic Studies · doi:10.1093/restud/rdab020 · Cited by 16
Self-citation
Generalized Pareto processes for simulating space-time extreme events: an application to precipitation reanalyses
Palacios‐Rodríguez, F., Toulemonde, G., Carreau, J., & Opitz, T. (2020) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-020-01895-w · Cited by 16
Information asymmetry and the profitability of technical analysis
Hung, C., & Lai, H. (2021) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2021.106347 · Cited by 16
Increasing the information content of realized volatility forecasts
Pascalau, R. C., & Poirier, R. (2021) · Journal of Financial Econometrics · doi:10.1093/jjfinec/nbab028 · Cited by 16
Tail risk transmission from commodity prices to sovereign risk of emerging economies
Zhang, Z., Shahzad, S. J. H., & Bouri, E. I. (2022) · Resources Policy · doi:10.1016/j.resourpol.2022.102869 · Cited by 16
Predictability hidden by Anomalous Observations in Financial Data
Camponovo, L., Olivier Scaillet, B., & Trojani, F. (2024) · Econometrics and Statistics · doi:10.1016/j.ecosta.2024.03.004 · Cited by 16
Evolutionary Learning of Technical Trading Rules without Data-Mining Bias
Agapitos, A., O’Neill, M., & Brabazon, A. (2010) · Springer eBooks · doi:10.1007/978-3-642-15844-5_30 · Cited by 16
False discoveries in volatility timing of mutual funds
Kim, S., & In, F. (2012) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2012.03.014 · Cited by 16
The performance of popular stochastic volatility option pricing models during the subprime crisis
Moyaert, T., & Petitjean, M. J. K. (2011) · Applied Financial Economics · doi:10.1080/09603107.2011.562161 · Cited by 15
Modeling and forecasting the additive bias corrected extreme value volatility estimator
Kumar, D., & Maheswaran, S. (2014) · International Review of Financial Analysis · doi:10.1016/j.irfa.2014.06.002 · Cited by 17
Resampling hierarchical processes in the wavelet domain: A case study using atmospheric turbulence
Angelini, C., Cava, D., Katul, G. G., & Vidaković, B. (2005) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2005.05.015 · Cited by 16
Relevant states and memory in Markov chain bootstrapping and simulation
Cerqueti, R., Falbo, P., & Pelizzari, C. (2016) · European Journal of Operational Research · doi:10.1016/j.ejor.2016.06.006 · Cited by 16
Stationary-sparse causality network learning
He, Y., She, Y., & Wu, D. O. (2013) · Cited by 16
Statistical Assessment and Validation of Experimental and Computational Ship Response in Irregular Waves
Diez, M., Broglia, R., Durante, D., Olivieri, A., Campana, E. F., & Stern, F. M. (2018) · Journal of Verification Validation and Uncertainty Quantification · doi:10.1115/1.4041372 · Cited by 16
Regime differences and industry heterogeneity of the volatility transmission from the energy price to the PPI
He, Y., & Lin, B. (2019) · Energy · doi:10.1016/j.energy.2019.04.025 · Cited by 16
The diabolical sovereigns/banks risk loop: A VAR quantile design
Foglia, M., & Angelini, E. (2020) · The Journal of Economic Asymmetries · doi:10.1016/j.jeca.2020.e00158 · Cited by 16
Variance estimators for the Lempel-Ziv entropy rate estimator
Amigó, J. M., & Kennel, M. B. (2006) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.2347102 · Cited by 16
Extended tapered block bootstrap
Shao, X. (2010) · Statistica Sinica · Cited by 16
Ship Finance: Hedging Ship Price Risk Using Freight Derivatives
Alizadeh, A. H., & Nomikos, N. K. (2012) · doi:10.1002/9781444345667.ch22 · Cited by 16
Bootstrap Conditional Distribution Tests in the Presence of Dynamic MisspecificationPreprint
Swanson, N. R., & Corradi, V. (2003) · SSRN Electronic Journal · doi:10.2139/ssrn.433002 · Cited by 16
Does Stock Return Predictability Imply Improved Asset Allocation and Performance? Evidence from the U.S. Stock Market (1954-2002)Preprint
Handa, P., & Tiwari, A. (2001) · SSRN Electronic Journal · doi:10.2139/ssrn.252209 · Cited by 16
Harmonic Regression Models: A Comparative Review with ApplicationsPreprint
Artis, M. J., Clavel, J. G., Hoffmann, M., & Nachane, D. M. (2007) · SSRN Electronic Journal · doi:10.2139/ssrn.1017519 · Cited by 16
Dynamics diagnosis of the COVID-19 deaths using the Pearson diagram
Gonçalves, A. D. S., Fernandes, L. H. S., & Nascimento, A. D. C. (2022) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.112634 · Cited by 15
Can we forecast better in periods of low uncertainty? The role of technical indicators
Fernández, M. F., Henry, Ó. T., Pybis, S., & Stamatogiannis, M. P. (2023) · Journal of Empirical Finance · doi:10.1016/j.jempfin.2022.12.014 · Cited by 15
Accuracy and precision when deriving sea-ice thickness from thermistor strings: a comparison of methods
Richter, M. E., Leonard, G. H., Smith, I. J., Langhorne, P. J., Mahoney, A. R., & Parry, M. F. (2022) · Journal of Glaciology · doi:10.1017/jog.2022.108 · Cited by 15
Examining the volatility of soybean market in the MIDAS framework: The importance of bagging-based weather information
Wang, L., Wu, R., Ma, W., & Xu, W. (2023) · International Review of Financial Analysis · doi:10.1016/j.irfa.2023.102720 · Cited by 15
Bootstrap Confidence Intervals
Manfred Mudelsee, A. (2014) · Atmospheric and oceanographic sciences library · doi:10.1007/978-3-319-04450-7_3 · Cited by 15
Composite likelihood-based inferences on genetic data from dependent loci
RoyChoudhury, A. (2010) · Journal of Mathematical Biology · doi:10.1007/s00285-010-0329-9 · Cited by 15
Short-term predictability of equity returns along two style dimensions
Shynkevich, A. (2012) · Journal of Empirical Finance · doi:10.1016/j.jempfin.2012.07.003 · Cited by 15
Stationary bootstrap for kernel density estimators under ψ -weak dependence
Hwang, E., & Shin, D. W. (2011) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2011.10.001 · Cited by 15
A Fourier analysis of extreme events
Mikosch, T., & Zhao, Y. (2014) · Bernoulli · doi:10.3150/13-bej507 · Cited by 15
Estimation of stationary autoregressive models with the Bayesian LASSO
Schmidt, D. F., & Makalic, E. (2013) · Journal of Time Series Analysis · doi:10.1111/jtsa.12027 · Cited by 15
Evaluation of volatility predictions in a VaR framework
Amendola, A., & Candila, V. (2015) · Quantitative Finance · doi:10.1080/14697688.2015.1062122 · Cited by 14
Resampling methods for functional data
McMurry, T. L., & Politis, D. N. (2018) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199568444.013.7 · Cited by 15
Self-citation
Fat tails and spurious estimation of consumption‐based asset pricing models
Toda, A. A., & Walsh, K. J. (2017) · Journal of Applied Econometrics · doi:10.1002/jae.2564 · Cited by 15
Predicting seasonal influenza using supermarket retail records
Miliou, I., Xiong, X., Rinzivillo, S., Zhang, Q., Rossetti, G., Giannotti, F., Pedreschi, D., & Vespignani, A. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009087 · Cited by 15
The impact of a sustainability constraint on the mean-tracking error efficient frontier
Boudt, K., Cornelissen, J., & Croux, C. (2013) · Economics Letters · doi:10.1016/j.econlet.2013.03.020 · Cited by 15
Multi‐Proxy, Multi‐Season Streamflow Reconstruction With Mass Balance Adjustment
Nguyen, H. T. T., Galelli, S., Xu, C., & Buckley, B. M. (2021) · Water Resources Research · doi:10.1029/2020wr029394 · Cited by 14
Attributes of Forecast Quality
Bradley, A. A., Demargne, J., & Franz, K. J. (2019) · Springer eBooks · doi:10.1007/978-3-642-39925-1_2 · Cited by 16
The US banking crisis in 2023: Intraday attention and price variation of banks at risk
Lyócsa, Š., Halousková, M., & Haugom, E. (2023) · Finance research letters · doi:10.1016/j.frl.2023.104209 · Cited by 15
Robust quantification of the exposure to operational risk
Menéndez, S. C., & Suárez, A. (2010) · Computers & Operations Research · doi:10.1016/j.cor.2010.10.001 · Cited by 15
A NOTE ON THE POWER OF BOOTSTRAP UNIT ROOT TESTS
Swensen, A. R. (2003) · Econometric Theory · doi:10.1017/s0266466603191025 · Cited by 15
Market imperfections and the information content of implied and realized volatility
Wong, W. K., & Tu, A. H. (2008) · Pacific-Basin Finance Journal · doi:10.1016/j.pacfin.2007.12.002 · Cited by 15
Trends of labour productivity in Italy: a study with panel co‐integration methods
Fachin, S., & Gavosto, A. (2010) · International Journal of Manpower · doi:10.1108/01437721011081581 · Cited by 15
Nonparametric change point detection in multivariate piecewise stationary time series
Sundararajan, R. R., & Pourahmadi, M. (2018) · Journal of nonparametric statistics · doi:10.1080/10485252.2018.1504943 · Cited by 15
Persistence and unit root in $$\text {CO}_{2}$$CO2 emissions: evidence from disaggregated global and regional data
Fallahi, F. (2018) · Empirical Economics · doi:10.1007/s00181-018-1608-3 · Cited by 15
Bootstrapping impulse responses of structural vector autoregressive models identified through GARCH
Lütkepohl, H., & Schlaak, T. (2019) · Journal of Economic Dynamics and Control · doi:10.1016/j.jedc.2019.01.008 · Cited by 15
Rotation Effects on Corn and Soybean Yield Inferred from Satellite and Field‐level Data
Cohen, A. A. B., Seifert, C. A., Azzari, G., & Lobell, D. B. (2019) · Agronomy Journal · doi:10.2134/agronj2019.03.0157 · Cited by 15
Investing in the S&P 500 index: Can anything beat the buy‐and‐hold strategy?
Dichtl, H. (2019) · Review of Financial Economics · doi:10.1002/rfe.1078 · Cited by 18
A machine‐learning analysis of the rationality of aggregate stock market forecasts
Pierdzioch, C., & Risse, M. (2018) · International Journal of Finance & Economics · doi:10.1002/ijfe.1641 · Cited by 15
Batching methods in simulation output analysis
Schmeiser, B. W., & Song, W. T. (1996) · Winter Simulation Conference · doi:10.1145/256562.256589 · Cited by 15
Option-Implied Information and Predictability of Extreme ReturnsPreprint
Vilkov, G., & Xiao, Y. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2147437 · Cited by 15
ETF Arbitrage and Return PredictabilityPreprint
Brown, D. C., Davies, S. W., & Ringgenberg, M. C. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2872414 · Cited by 15
Option-Implied Information and Predictability of Extreme ReturnsPreprint
Vilkov, G., & Xiao, Y. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2209654 · Cited by 15
The Dynamic Relationship between Investor Attention and Stock Market Volatility: International Evidence
Said, I. B. E. H., & Slim, S. (2022) · Journal of risk and financial management · doi:10.3390/jrfm15020066 · Cited by 14
Reduced-Rank Envelope Vector Autoregressive Model
Samadi, S. Y., & Herath, H. M. W. B. (2023) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2023.2260862 · Cited by 14
Consistent causal inference from time series with PC algorithm and its time-aware extension
Biswas, R., & Mukherjee, S. (2023) · Statistics and Computing · doi:10.1007/s11222-023-10330-3 · Cited by 16
The safe withdrawal rate: evidence from a broad sample of developed markets
Anarkulova, A., Cederburg, S., O’Doherty, M. S., & Sias, R. W. (2025) · Journal of Pensions Economics and Finance · doi:10.1017/s1474747225000010 · Cited by 14
Currency jumps and crises: Do developed and emerging market currencies jump together?
Chan, K. F., Powell, J. G., & Treepongkaruna, S. (2014) · Pacific-Basin Finance Journal · doi:10.1016/j.pacfin.2014.08.001 · Cited by 14
Holding a commodity futures index fund in a globally diversified portfolio: A placebo effect?
Cao, B., Jayasuriya, S. A., & Shambora, W. E. (2010) · RePEc: Research Papers in Economics · Cited by 14
High-Dimensional Multivariate Time Series With Additional Structure
Schweinberger, M., Babkin, S., & Ensor, K. B. (2016) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2016.1265528 · Cited by 14
Dynamic conditional copula correlation and optimal hedge ratios with currency futures
Kotkatvuori-Örnberg, J. (2016) · International Review of Financial Analysis · doi:10.1016/j.irfa.2016.06.006 · Cited by 14
A Generalised Fractional Differencing Bootstrap for Long Memory Processes
Kapetanios, G., Papailias, F., & Taylor, R. (2019) · Journal of Time Series Analysis · doi:10.1111/jtsa.12460 · Cited by 14
Conditional Variance Forecasts for Long-Term Stock Returns
Mammen, E., Nielsen, J. P., Scholz, M., & Sperlich, S. (2019) · Risks · doi:10.3390/risks7040113 · Cited by 14
HirePeer: Impartial Peer-Assessed Hiring at Scale in Expert Crowdsourcing Markets
Kotturi, Y., Kahng, A., Procaccia, A. D., & Kulkarni, C. E. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5641 · Cited by 14
Detecting relevant differences in the covariance operators of functional time series: a sup-norm approach
Dette, H., & Kokot, K. (2021) · Annals of the Institute of Statistical Mathematics · doi:10.1007/s10463-021-00795-2 · Cited by 14
Portfolio Choices with Many Big Models
Anderson, E. W., & Cheng, A.-R. (2021) · Management Science · doi:10.1287/mnsc.2020.3876 · Cited by 14

Authors: Politis, D. N., & Romano, J. P.
Affiliations: Purdue University West Lafayette (US)
Stanford University (US)
ORCID: Politis, Dimitris N.
Romano, Joseph P.
Venue: Journal of the American Statistical Association
Publisher: Informa UK Limited
Published: 1994-12
Volume: 89
Issue: 428
Pages: 1303-1313
ISSN: 0162-1459 (ISSN-L)
1537-274X
DOI: 10.1080/01621459.1994.10476870
OpenAlex: W2076396344
Cited by: 2,649
References: 27
Citations by year: 2026: 279 · 2025: 137 · 2024: 113 · 2023: 108 · 2022: 102 · 2021: 124 · 2020: 136 · 2019: 117 · 2018: 117 · 2017: 129 · 2016: 125 · 2015: 120 · 2014: 119 · 2013: 117 · 2012: 130
FWCI: 9.25
Type: journal article
Language: en
Open access: closed
Locations: Journal of the American Statistical Association (publishedVersion)
Helmholtz Centre for Ocean Research Kiel (GEOMAR) (acceptedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
Keywords: stationary bootstrap, resampling, standard errors, confidence regions, geometric distribution, consistency, weak convergence, infinite-dimensional parameters, block bootstrap

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

The Stationary Bootstrap

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks