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Adam McCloskey

Personal Details

First Name:Adam
Middle Name:
Last Name:McCloskey
Suffix:
RePEc Short-ID:pmc156
Terminal Degree:2011 Department of Economics; Boston University (from RePEc Genealogy)

Affiliation

Department of Economics
University of Colorado

Boulder, Colorado (United States)
https://www.colorado.edu/economics/
RePEc:edi:decolus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Software

Working papers

  1. Adam McCloskey & Pascal Michaillat, 2022. "Incentive-Compatible Critical Values," NBER Working Papers 29702, National Bureau of Economic Research, Inc.
  2. Philipp Ketz & Adam McCloskey, 2021. "Short and Simple Confidence Intervals when the Directions of Some Effects are Known," Papers 2109.08222, arXiv.org.
  3. Adam McCloskey & Pascal Michaillat, 2020. "Critical Values Robust to P-hacking," Papers 2005.04141, arXiv.org, revised Jun 2022.
  4. Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2019. "Inference after estimation of breaks," CeMMAP working papers CWP51/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  5. Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2018. "Inference on winners," CeMMAP working papers CWP31/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  6. Adam McCloskey, 2012. "Estimation of the Long-Memory Stochastic Volatility Model Parameters that is Robust to Level Shifts and Deterministic Trends," Working Papers 2012-17, Brown University, Department of Economics.
  7. Adam McCloskey, 2012. "Bonferroni-Based Size-Correction for Nonstandard Testing Problems," Working Papers 2012-16, Brown University, Department of Economics.
  8. Pierre Perron & Adam McCloskey, 2010. "Memory Parameter Estimation in the Presence of Level Shifts and Deterministic Trends," Boston University - Department of Economics - Working Papers Series WP2010-048, Boston University - Department of Economics.

Articles

  1. Isaiah Andrews & Dillon Bowen & Toru Kitagawa & Adam McCloskey, 2022. "Inference for Losers," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 635-642, May.
  2. Andrews, Isaiah & Kitagawa, Toru & McCloskey, Adam, 2021. "Inference after estimation of breaks," Journal of Econometrics, Elsevier, vol. 224(1), pages 39-59.
  3. Adam McCloskey, 2020. "Asymptotically Uniform Tests After Consistent Model Selection in the Linear Regression Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 810-825, October.
  4. Sukjin Han & Adam McCloskey, 2019. "Estimation and inference with a (nearly) singular Jacobian," Quantitative Economics, Econometric Society, vol. 10(3), pages 1019-1068, July.
  5. McCloskey, Adam, 2017. "Bonferroni-based size-correction for nonstandard testing problems," Journal of Econometrics, Elsevier, vol. 200(1), pages 17-35.
  6. Adam McCloskey & Jonathan B. Hill, 2017. "Parameter Estimation Robust to Low-Frequency Contamination," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 598-610, October.
  7. Mccloskey, Adam & Perron, Pierre, 2013. "Memory Parameter Estimation In The Presence Of Level Shifts And Deterministic Trends," Econometric Theory, Cambridge University Press, vol. 29(6), pages 1196-1237, December.
  8. Adam McCloskey, 2013. "Estimation of the long-memory stochastic volatility model parameters that is robust to level shifts and deterministic trends," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 285-301, May.

Software components

  1. Chad Brown & Philipp Ketz & Adam McCloskey, 2021. "SSCI: Stata module to compute Short and Simple Confidence Interval," Statistical Software Components S458986, Boston College Department of Economics.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Adam McCloskey & Pascal Michaillat, 2022. "Incentive-Compatible Critical Values," NBER Working Papers 29702, National Bureau of Economic Research, Inc.

    Cited by:

    1. Graham Elliott & Nikolay Kudrin & Kaspar Wuthrich, 2022. "(When) Can We Detect $p$-Hacking?," Papers 2205.07950, arXiv.org.

  2. Philipp Ketz & Adam McCloskey, 2021. "Short and Simple Confidence Intervals when the Directions of Some Effects are Known," Papers 2109.08222, arXiv.org.

    Cited by:

    1. Gregory Cox, 2022. "A Generalized Argmax Theorem with Applications," Papers 2209.08793, arXiv.org.

  3. Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2019. "Inference after estimation of breaks," CeMMAP working papers CWP51/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2018. "Inference on winners," CeMMAP working papers CWP73/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  4. Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2018. "Inference on winners," CeMMAP working papers CWP31/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Magne Mogstad & Joseph P. Romano & Daniel Wilhelm & Azeem M. Shaikh, 2020. "Inference for ranks with applications to mobility across neighborhoods and academic achievement across countries," CeMMAP working papers CWP10/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Will Davis & Alexander Gordan & Rusty Tchernis, 2021. "Measuring the spatial distribution of health rankings in the United States," Health Economics, John Wiley & Sons, Ltd., vol. 30(11), pages 2921-2936, November.
    3. Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2020. "Inference after Estimation of Breaks," CeMMAP working papers CWP34/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Kohei Yata, 2021. "Optimal Decision Rules Under Partial Identification," Papers 2111.04926, arXiv.org.
    5. Davide Viviano & Jelena Bradic, 2020. "Fair Policy Targeting," Papers 2005.12395, arXiv.org, revised Jun 2022.
    6. Sergei Bazylik & Magne Mogstad & Joseph P. Romano & Azeem M. Shaikh & Daniel Wilhelm, 2021. "Finite- and large-sample inference for ranks using multinomial data with an application to ranking political parties," CeMMAP working papers CWP40/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Davide Viviano, 2020. "Policy design in experiments with unknown interference," Papers 2011.08174, arXiv.org, revised Feb 2022.
    8. Hsieh, Yu-Wei & Shi, Xiaoxia & Shum, Matthew, 2022. "Inference on estimators defined by mathematical programming," Journal of Econometrics, Elsevier, vol. 226(2), pages 248-268.
    9. David J. Deming, 2021. "The Growing Importance of Decision-Making on the Job," NBER Working Papers 28733, National Bureau of Economic Research, Inc.
    10. Isaiah Andrews & Jonathan Roth & Ariel Pakes, 2019. "Inference for Linear Conditional Moment Inequalities," NBER Working Papers 26374, National Bureau of Economic Research, Inc.
    11. Jiafeng Chen, 2021. "Nonparametric Treatment Effect Identification in School Choice," Papers 2112.03872, arXiv.org, revised Nov 2022.

  5. Adam McCloskey, 2012. "Estimation of the Long-Memory Stochastic Volatility Model Parameters that is Robust to Level Shifts and Deterministic Trends," Working Papers 2012-17, Brown University, Department of Economics.

    Cited by:

    1. Arteche, Josu & García-Enríquez, Javier, 2017. "Singular Spectrum Analysis for signal extraction in Stochastic Volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 85-98.
    2. Hou, Jie & Perron, Pierre, 2014. "Modified local Whittle estimator for long memory processes in the presence of low frequency (and other) contaminations," Journal of Econometrics, Elsevier, vol. 182(2), pages 309-328.
    3. Christensen, Bent Jesper & Varneskov, Rasmus Tangsgaard, 2017. "Medium band least squares estimation of fractional cointegration in the presence of low-frequency contamination," Journal of Econometrics, Elsevier, vol. 197(2), pages 218-244.
    4. Matei Demetrescu & Mehdi Hosseinkouchack, 2022. "Autoregressive spectral estimates under ignored changes in the mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(2), pages 329-340, March.
    5. Busch, Marie & Sibbertsen, Philipp, 2018. "An Overview of Modified Semiparametric Memory Estimation Methods," Hannover Economic Papers (HEP) dp-628, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    6. Heni Boubaker, 2016. "A Comparative Study of the Performance of Estimating Long-Memory Parameter Using Wavelet-Based Entropies," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 693-731, December.
    7. Dalla, Violetta & Giraitis, Liudas & Robinson, Peter M., 2020. "Asymptotic theory for time series with changing mean and variance," Journal of Econometrics, Elsevier, vol. 219(2), pages 281-313.

  6. Adam McCloskey, 2012. "Bonferroni-Based Size-Correction for Nonstandard Testing Problems," Working Papers 2012-16, Brown University, Department of Economics.

    Cited by:

    1. Karthik Muralidharan & Mauricio Romero & Kaspar Wüthrich, 2020. "Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments," CESifo Working Paper Series 8137, CESifo.
    2. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Size-corrected Bootstrap Test after Pretesting for Exogeneity with Heteroskedastic or Clustered Data," MPRA Paper 110899, University Library of Munich, Germany.
    3. James A. Duffy & Jerome R. Simons, 2020. "The Cointegrated VAR without Unit Roots: Representation Theory and Asymptotics," Papers 2002.08092, arXiv.org.
    4. Cheng, Xu & Liao, Zhipeng, 2015. "Select the valid and relevant moments: An information-based LASSO for GMM with many moments," Journal of Econometrics, Elsevier, vol. 186(2), pages 443-464.
    5. David Hao Zhang & Paul S. Willen, 2021. "Do Lenders Still Discriminate? A Robust Approach for Assessing Differences in Menus," NBER Working Papers 29142, National Bureau of Economic Research, Inc.
    6. Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2018. "Inference on winners," CeMMAP working papers CWP73/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Massimo Franchi & Søren Johansen, 2017. "Improved inference on cointegrating vectors in the presence of a near unit root using adjusted quantiles," CREATES Research Papers 2017-17, Department of Economics and Business Economics, Aarhus University.
    8. Ivan A. Canay & Azeem M. Shaikh, 2016. "Practical and theoretical advances in inference for partially identified models," CeMMAP working papers CWP05/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Chevillon, Guillaume & Mavroeidis, Sophocles & Zhan, Zhaoguo, 2016. "Robust inference in structural VARs with long-run restrictions," ESSEC Working Papers WP1702, ESSEC Research Center, ESSEC Business School.
    10. Donald W.K. Andrews & Xu Cheng & Patrik Guggenberger, 2011. "Generic Results for Establishing the Asymptotic Size of Confidence Sets and Tests," Cowles Foundation Discussion Papers 1813, Cowles Foundation for Research in Economics, Yale University.
    11. Jean-Jacques Forneron, 2019. "Detecting Identification Failure in Moment Condition Models," Papers 1907.13093, arXiv.org, revised Aug 2022.
    12. Philipp Ketz, 2019. "Testing overidentifying restrictions with a restricted parameter space," Post-Print halshs-02492665, HAL.
    13. Jochmans, K., 2019. "Testing Correlation in Error-Component Models," Cambridge Working Papers in Economics 1993, Faculty of Economics, University of Cambridge.
    14. Philipp Ketz & Adam Mccloskey, 2021. "Short and Simple Confidence Intervals when the Directions of Some Effects are Known," Working Papers hal-03388199, HAL.
    15. Donald W. K. Andrews & Patrik Guggenberger, 2015. "Identification- and Singularity-Robust Inference for Moment Condition," Cowles Foundation Discussion Papers 1978R2, Cowles Foundation for Research in Economics, Yale University, revised Jan 2019.
    16. Xu Cheng, 2014. "Uniform Inference in Nonlinear Models with Mixed Identification Strength," PIER Working Paper Archive 14-018, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    17. Cavaliere, Giuseppe & Nielsen, Heino Bohn & Pedersen, Rasmus Søndergaard & Rahbek, Anders, 2022. "Bootstrap inference on the boundary of the parameter space, with application to conditional volatility models," Journal of Econometrics, Elsevier, vol. 227(1), pages 241-263.
    18. Francis J. DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 14-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Dec 2014.
    19. Timothy B. Armstrong & Michal Koles�r, 2016. "Optimal Inference in a Class of Regression Models," Cowles Foundation Discussion Papers 2043, Cowles Foundation for Research in Economics, Yale University.
    20. Jui-Chung Yang & Ke-Li Xu, 2013. "Estimation and Inference under Weak Identi cation and Persistence: An Application on Forecast-Based Monetary Policy Reaction Function," 2013 Papers pya307, Job Market Papers.
    21. Jungbin Hwang & Gonzalo Valdés, 2020. "Low Frequency Cointegrating Regression in the Presence of Local to Unity Regressors and Unknown Form of Serial Dependence," Working papers 2020-03, University of Connecticut, Department of Economics, revised Aug 2020.
    22. Romano, Joseph P. & Wolf, Michael, 2013. "Testing for monotonicity in expected asset returns," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 93-116.
    23. Philipp Ketz, 2018. "Subvector inference when the true parameter vector may be near or at the boundary," PSE-Ecole d'économie de Paris (Postprint) halshs-01884381, HAL.
    24. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Uniform Inference after Pretesting for Exogeneity with Heteroskedastic Data," MPRA Paper 106408, University Library of Munich, Germany.
    25. Firmin Doko Tchatoka & Wenjie Wang, 2020. "Uniform Inference after Pretesting for Exogeneity," School of Economics Working Papers 2020-05, University of Adelaide, School of Economics.
    26. DiTraglia, Francis J., 2016. "Using invalid instruments on purpose: Focused moment selection and averaging for GMM," Journal of Econometrics, Elsevier, vol. 195(2), pages 187-208.
    27. Wang, Wenjie & Doko Tchatoka, Firmin, 2018. "On Bootstrap inconsistency and Bonferroni-based size-correction for the subset Anderson–Rubin test under conditional homoskedasticity," Journal of Econometrics, Elsevier, vol. 207(1), pages 188-211.
    28. Canepa Alessandra, 2022. "Small Sample Adjustment for Hypotheses Testing on Cointegrating Vectors," Journal of Time Series Econometrics, De Gruyter, vol. 14(1), pages 51-85, January.
    29. Chevillon, Guillaume, 2017. "Robustness of Multistep Forecasts and Predictive Regressions at Intermediate and Long Horizons," ESSEC Working Papers WP1710, ESSEC Research Center, ESSEC Business School.
    30. Katarina Juselius, 2017. "Recent Developments in Cointegration," Econometrics, MDPI, vol. 6(1), pages 1-5, December.
    31. Francis DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 15-027, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 10 Aug 2015.
    32. Cheng, Xu, 2015. "Robust inference in nonlinear models with mixed identification strength," Journal of Econometrics, Elsevier, vol. 189(1), pages 207-228.
    33. Xu Cheng & Zhipeng Liao, 2012. "Select the Valid and Relevant Moments: A One-Step Procedure for GMM with Many Moments," PIER Working Paper Archive 12-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

  7. Pierre Perron & Adam McCloskey, 2010. "Memory Parameter Estimation in the Presence of Level Shifts and Deterministic Trends," Boston University - Department of Economics - Working Papers Series WP2010-048, Boston University - Department of Economics.

    Cited by:

    1. Arteche, Josu & García-Enríquez, Javier, 2017. "Singular Spectrum Analysis for signal extraction in Stochastic Volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 85-98.
    2. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    3. Hou, Jie & Perron, Pierre, 2014. "Modified local Whittle estimator for long memory processes in the presence of low frequency (and other) contaminations," Journal of Econometrics, Elsevier, vol. 182(2), pages 309-328.
    4. Sibbertsen, Philipp & Leschinski, Christian & Busch, Marie, 2018. "A multivariate test against spurious long memory," Journal of Econometrics, Elsevier, vol. 203(1), pages 33-49.
    5. Kruse, Robinson, 2015. "A modified test against spurious long memory," Economics Letters, Elsevier, vol. 135(C), pages 34-38.
    6. Rodríguez, Gabriel, 2017. "Modeling Latin-American stock and Forex markets volatility: Empirical application of a model with random level shifts and genuine long memory," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 393-420.
    7. Christensen, Bent Jesper & Varneskov, Rasmus Tangsgaard, 2017. "Medium band least squares estimation of fractional cointegration in the presence of low-frequency contamination," Journal of Econometrics, Elsevier, vol. 197(2), pages 218-244.
    8. Rasmus Tangsgaard Varneskov & Pierre Perron, 2011. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns," CREATES Research Papers 2011-26, Department of Economics and Business Economics, Aarhus University.
    9. Niels Haldrup & Robinson Kruse, 2014. "Discriminating between fractional integration and spurious long memory," CREATES Research Papers 2014-19, Department of Economics and Business Economics, Aarhus University.
    10. Matei Demetrescu & Mehdi Hosseinkouchack, 2022. "Autoregressive spectral estimates under ignored changes in the mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(2), pages 329-340, March.
    11. Baek, Changryong & Fortuna, Natércia & Pipiras, Vladas, 2014. "Can Markov switching model generate long memory?," Economics Letters, Elsevier, vol. 124(1), pages 117-121.
    12. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "The Memory of Volatility," Hannover Economic Papers (HEP) dp-601, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    13. Torben G. Andersen & Rasmus T. Varneskov, 2021. "Consistent Inference for Predictive Regressions in Persistent Economic Systems," NBER Working Papers 28568, National Bureau of Economic Research, Inc.
    14. Pierre Perron & Yohei Yamamoto, 2011. "Estimating and Testing Multiple Structural Changes in Linear Models Using Band Spectral Regressions," Boston University - Department of Economics - Working Papers Series WP2011-049, Boston University - Department of Economics.
    15. Javier Haulde & Morten Ørregaard Nielsen, 2022. "Fractional integration and cointegration," CREATES Research Papers 2022-02, Department of Economics and Business Economics, Aarhus University.
    16. Dolado, Juan J. & Rachinger, Heiko & Velasco, Carlos, 2020. "LM tests for joint breaks in the dynamics and level of a long-memory time series," CEPR Discussion Papers 15435, C.E.P.R. Discussion Papers.
    17. Pierre Perron & Wendong Shi, 2014. "Temporal Aggregation, Bandwidth Selection and Long Memory for Volatility Models," Boston University - Department of Economics - Working Papers Series wp2014-009, Boston University - Department of Economics.
    18. Busch, Marie & Sibbertsen, Philipp, 2018. "An Overview of Modified Semiparametric Memory Estimation Methods," Hannover Economic Papers (HEP) dp-628, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    19. Kai Wenger & Christian Leschinski & Philipp Sibbertsen, 2019. "Change-in-mean tests in long-memory time series: a review of recent developments," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(2), pages 237-256, June.
    20. Dalla, Violetta & Giraitis, Liudas & Robinson, Peter M., 2020. "Asymptotic theory for time series with changing mean and variance," Journal of Econometrics, Elsevier, vol. 219(2), pages 281-313.
    21. Leschinski, Christian & Sibbertsen, Philipp, 2018. "The Periodogram of Spurious Long-Memory Processes," Hannover Economic Papers (HEP) dp-632, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    22. Adam McCloskey, 2012. "Estimation of the Long-Memory Stochastic Volatility Model Parameters that is Robust to Level Shifts and Deterministic Trends," Working Papers 2012-17, Brown University, Department of Economics.
    23. Becker, Janis & Leschinski, Christian & Sibbertsen, Philipp, 2019. "Robust Multivariate Local Whittle Estimation and Spurious Fractional Cointegration," Hannover Economic Papers (HEP) dp-660, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    24. Alexander Boca Saravia & Gabriel Rodríguez, 2022. "Presidential approval in Peru: an empirical analysis using a fractionally cointegrated VAR," Economic Change and Restructuring, Springer, vol. 55(3), pages 1973-2010, August.
    25. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    26. Tzouras, Spilios & Anagnostopoulos, Christoforos & McCoy, Emma, 2015. "Financial time series modeling using the Hurst exponent," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 425(C), pages 50-68.
    27. Gabriel Rodríguez, 2016. "Modeling Latin-American Stock and Forex Markets Volatility: Empirical Application of a Model with Random Level Shifts and Genuine Long Memory [Modelando la volatilidad de los mercados bursátiles y cam," Documentos de Trabajo / Working Papers 2016-416, Departamento de Economía - Pontificia Universidad Católica del Perú.
    28. Lujia Bai & Weichi Wu, 2021. "Detecting long-range dependence for linear models with time-varying coefficients and a difference-based covariance estimator," Papers 2110.08089, arXiv.org, revised Jun 2022.
    29. Carina Gerstenberger, 2021. "Robust discrimination between long‐range dependence and a change in mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(1), pages 34-62, January.
    30. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2017. "Does the ARFIMA really shift?," CREATES Research Papers 2017-16, Department of Economics and Business Economics, Aarhus University.
    31. Pierre Perron & Wendong Shi, 2020. "Temporal Aggregation and Long Memory for Asset Price Volatility," JRFM, MDPI, vol. 13(8), pages 1-18, August.
    32. Less, Vivien & Sibbertsen, Philipp, 2022. "Estimation and Testing in a Perturbed Multivariate Long Memory Framework," Hannover Economic Papers (HEP) dp-704, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    33. Sibbertsen, Philipp & Wenger, Kai & Wingert, Simon, 2020. "Testing for Multiple Structural Breaks in Multivariate Long Memory Time Series," Hannover Economic Papers (HEP) dp-676, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    34. Aeneas Rooch & Ieva Zelo & Roland Fried, 2019. "Estimation methods for the LRD parameter under a change in the mean," Statistical Papers, Springer, vol. 60(1), pages 313-347, February.
    35. García-Enríquez, Javier & Hualde, Javier, 2019. "Local Whittle estimation of long memory: Standard versus bias-reducing techniques," Econometrics and Statistics, Elsevier, vol. 12(C), pages 66-77.
    36. Claudio Morana, 2013. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks: New Insights on the US OIS SPreads Term Structure," Working Papers 233, University of Milano-Bicocca, Department of Economics, revised Feb 2013.
    37. Leschinski, Christian & Sibbertsen, Philipp, 2017. "Origins of Spurious Long Memory," Hannover Economic Papers (HEP) dp-595, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

Articles

  1. Andrews, Isaiah & Kitagawa, Toru & McCloskey, Adam, 2021. "Inference after estimation of breaks," Journal of Econometrics, Elsevier, vol. 224(1), pages 39-59.
    See citations under working paper version above.
  2. Adam McCloskey, 2020. "Asymptotically Uniform Tests After Consistent Model Selection in the Linear Regression Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 810-825, October.

    Cited by:

    1. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Size-corrected Bootstrap Test after Pretesting for Exogeneity with Heteroskedastic or Clustered Data," MPRA Paper 110899, University Library of Munich, Germany.
    2. Timothy B. Armstrong & Michal Kolesár, 2020. "Sensitivity Analysis using Approximate Moment Condition Models," Working Papers 2020-28, Princeton University. Economics Department..
    3. Philipp Ketz & Adam Mccloskey, 2021. "Short and Simple Confidence Intervals when the Directions of Some Effects are Known," Working Papers hal-03388199, HAL.
    4. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Uniform Inference after Pretesting for Exogeneity with Heteroskedastic Data," MPRA Paper 106408, University Library of Munich, Germany.

  3. Sukjin Han & Adam McCloskey, 2019. "Estimation and inference with a (nearly) singular Jacobian," Quantitative Economics, Econometric Society, vol. 10(3), pages 1019-1068, July.

    Cited by:

    1. Valérie Lechene & Krishna Pendakur & Alexander Wolf, 2020. "OLS estimation of the intra-household distribution of expenditure," IFS Working Papers W20/6, Institute for Fiscal Studies.
    2. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Size-corrected Bootstrap Test after Pretesting for Exogeneity with Heteroskedastic or Clustered Data," MPRA Paper 110899, University Library of Munich, Germany.
    3. Tetsuya Kaji, 2019. "Theory of Weak Identification in Semiparametric Models," Papers 1908.10478, arXiv.org, revised Aug 2020.
    4. Adam McCloskey, 2012. "Bonferroni-Based Size-Correction for Nonstandard Testing Problems," Working Papers 2012-16, Brown University, Department of Economics.
    5. Donald W.K. Andrews & Xu Cheng & Patrik Guggenberger, 2011. "Generic Results for Establishing the Asymptotic Size of Confidence Sets and Tests," Cowles Foundation Discussion Papers 1813, Cowles Foundation for Research in Economics, Yale University.
    6. Gregory Cox, 2018. "Almost Sure Uniqueness of a Global Minimum Without Convexity," Papers 1803.02415, arXiv.org, revised Feb 2019.
    7. Shakeeb Khan & Denis Nekipelov, 2019. "On Uniform Inference in Nonlinear Models with Endogeneity," Boston College Working Papers in Economics 986, Boston College Department of Economics.
    8. Gregory Cox, 2022. "A Generalized Argmax Theorem with Applications," Papers 2209.08793, arXiv.org.
    9. Valérie Lechene & Krishna Pendakur & Alexander Wolf, 2019. "OLS estimation of the intra-household distribution of consumption," IFS Working Papers W19/19, Institute for Fiscal Studies.
    10. Woosik Gong & Myung Hwan Seo, 2022. "Robust Inference for Dynamic Panel Threshold Models," Papers 2211.04027, arXiv.org.
    11. Gregory Cox, 2020. "Weak Identification with Bounds in a Class of Minimum Distance Models," Papers 2012.11222, arXiv.org, revised Dec 2022.
    12. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Uniform Inference after Pretesting for Exogeneity with Heteroskedastic Data," MPRA Paper 106408, University Library of Munich, Germany.
    13. Firmin Doko Tchatoka & Wenjie Wang, 2020. "Uniform Inference after Pretesting for Exogeneity," School of Economics Working Papers 2020-05, University of Adelaide, School of Economics.
    14. Patrik Guggenberger & Frank Kleibergen & Sophocles Mavroeidis, 2021. "A Powerful Subvector Anderson Rubin Test in Linear Instrumental Variables Regression with Conditional Heteroskedasticity," Papers 2103.11371, arXiv.org, revised Oct 2022.
    15. Gregory Cox, 2022. "Weak Identification in Low-Dimensional Factor Models with One or Two Factors," Papers 2211.00329, arXiv.org.
    16. Frank Kleibergen & Zhaoguo Zhan, 2021. "Double robust inference for continuous updating GMM," Papers 2105.08345, arXiv.org.
    17. Isaiah Andrews & Anna Mikusheva, 2022. "Optimal Decision Rules for Weak GMM," Econometrica, Econometric Society, vol. 90(2), pages 715-748, March.

  4. McCloskey, Adam, 2017. "Bonferroni-based size-correction for nonstandard testing problems," Journal of Econometrics, Elsevier, vol. 200(1), pages 17-35.
    See citations under working paper version above.
  5. Adam McCloskey & Jonathan B. Hill, 2017. "Parameter Estimation Robust to Low-Frequency Contamination," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 598-610, October.

    Cited by:

    1. Christensen, Bent Jesper & Varneskov, Rasmus Tangsgaard, 2017. "Medium band least squares estimation of fractional cointegration in the presence of low-frequency contamination," Journal of Econometrics, Elsevier, vol. 197(2), pages 218-244.
    2. Rasmus Tangsgaard Varneskov & Pierre Perron, 2011. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns," CREATES Research Papers 2011-26, Department of Economics and Business Economics, Aarhus University.
    3. Niels Haldrup & Oskar Knapik & Tommaso Proietti, 2016. "A generalized exponential time series regression model for electricity prices," CREATES Research Papers 2016-08, Department of Economics and Business Economics, Aarhus University.
    4. Matei Demetrescu & Mehdi Hosseinkouchack, 2022. "Autoregressive spectral estimates under ignored changes in the mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(2), pages 329-340, March.
    5. Alessandro Casini, 2021. "Theory of Evolutionary Spectra for Heteroskedasticity and Autocorrelation Robust Inference in Possibly Misspecified and Nonstationary Models," Papers 2103.02981, arXiv.org.
    6. Busch, Marie & Sibbertsen, Philipp, 2018. "An Overview of Modified Semiparametric Memory Estimation Methods," Hannover Economic Papers (HEP) dp-628, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    7. Dalla, Violetta & Giraitis, Liudas & Robinson, Peter M., 2020. "Asymptotic theory for time series with changing mean and variance," Journal of Econometrics, Elsevier, vol. 219(2), pages 281-313.
    8. Leschinski, Christian & Sibbertsen, Philipp, 2018. "The Periodogram of Spurious Long-Memory Processes," Hannover Economic Papers (HEP) dp-632, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    9. Adam McCloskey, 2012. "Estimation of the Long-Memory Stochastic Volatility Model Parameters that is Robust to Level Shifts and Deterministic Trends," Working Papers 2012-17, Brown University, Department of Economics.
    10. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    11. Gabriel Rodríguez, 2016. "Modeling Latin-American Stock and Forex Markets Volatility: Empirical Application of a Model with Random Level Shifts and Genuine Long Memory [Modelando la volatilidad de los mercados bursátiles y cam," Documentos de Trabajo / Working Papers 2016-416, Departamento de Economía - Pontificia Universidad Católica del Perú.
    12. Tommaso Proietti & Niels Haldrup & Oskar Knapik, 2017. "Spikes and memory in (Nord Pool) electricity price spot prices," CREATES Research Papers 2017-39, Department of Economics and Business Economics, Aarhus University.
    13. Zhongjun Qu & Denis Tkachenko, 2011. "Frequency Domain Analysis of Medium Scale DSGE Models with Application to Smets and Wouters (2007)," Boston University - Department of Economics - Working Papers Series WP2011-060, Boston University - Department of Economics.
    14. Alessandro Casini & Taosong Deng & Pierre Perron, 2021. "Theory of Low Frequency Contamination from Nonstationarity and Misspecification: Consequences for HAR Inference," Papers 2103.01604, arXiv.org, revised Nov 2021.

  6. Mccloskey, Adam & Perron, Pierre, 2013. "Memory Parameter Estimation In The Presence Of Level Shifts And Deterministic Trends," Econometric Theory, Cambridge University Press, vol. 29(6), pages 1196-1237, December.
    See citations under working paper version above.
  7. Adam McCloskey, 2013. "Estimation of the long-memory stochastic volatility model parameters that is robust to level shifts and deterministic trends," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 285-301, May. See citations under working paper version above.

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 8 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (7) 2012-11-17 2012-11-17 2012-11-17 2018-07-23 2020-01-06 2020-05-18 2021-09-27. Author is listed
  2. NEP-ETS: Econometric Time Series (3) 2012-11-17 2012-11-17 2021-07-19
  3. NEP-ISF: Islamic Finance (1) 2021-09-27

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