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Tucker Sprague McElroy

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. Henry R. Hyatt & Tucker S. McElroy, 2017. "Labor Reallocation, Employment, and Earnings: Vector Autoregression Evidence," Working Papers 17-11, Center for Economic Studies, U.S. Census Bureau.

    Cited by:

    1. Hahn, Joyce K. & Hyatt, Henry R. & Janicki, Hubert P., 2021. "Job ladders and growth in earnings, hours, and wages," European Economic Review, Elsevier, vol. 133(C).
    2. Asmae Beladel & Radouane Raouf, 2022. "Impact assessment of job reallocation on unemployment in Morocco: An ARDL approach," African Development Review, African Development Bank, vol. 34(4), pages 500-512, December.

  2. McElroy, Tucker & Politis, Dimitris, 2013. "Spectral Density and Spectral Distribution Inference for Long Memory Time Series via Fixed-b Asymptotics," University of California at San Diego, Economics Working Paper Series qt6164c110, Department of Economics, UC San Diego.

    Cited by:

    1. Tucker McElroy & Anindya Roy, 2022. "A Review of Seasonal Adjustment Diagnostics," International Statistical Review, International Statistical Institute, vol. 90(2), pages 259-284, August.
    2. Tucker S McElroy & Agnieszka Jach, 2019. "Testing collinearity of vector time series," The Econometrics Journal, Royal Economic Society, vol. 22(2), pages 97-116.
    3. McElroy, Tucker S. & Jach, Agnieszka, 2023. "Identification of the differencing operator of a non-stationary time series via testing for zeroes in the spectral density," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    4. Tucker S. McElroy & Anindya Roy, 2022. "Model identification via total Frobenius norm of multivariate spectra," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 473-495, April.
    5. McElroy, Tucker S. & Holan, Scott H., 2016. "Computation of the autocovariances for time series with multiple long-range persistencies," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 44-56.
    6. van Delft, Anne & Eichler, Michael, 2020. "A note on Herglotz’s theorem for time series on function spaces," Stochastic Processes and their Applications, Elsevier, vol. 130(6), pages 3687-3710.

  3. McElroy, Tucker S. & Politis, Dimitris N., 2012. "Distribution Theory for the Studentized Mean for Long, Short, and Negative Memory Time Series," University of California at San Diego, Economics Working Paper Series qt35c7r55c, Department of Economics, UC San Diego.

    Cited by:

    1. Robinson Kruse & Christian Leschinski & Michael Will, 2016. "Comparing Predictive Accuracy under Long Memory - With an Application to Volatility Forecasting," CREATES Research Papers 2016-17, Department of Economics and Business Economics, Aarhus University.
    2. McElroy, Tucker S & Politis, D N, 2011. "Distribution Theory for the Studentized Mean for Long, Short, and Negative Memory Time Series," University of California at San Diego, Economics Working Paper Series qt0dr145dt, Department of Economics, UC San Diego.
    3. Wenger, Kai & Leschinski, Christian, 2018. "Fixed-Bandwidth CUSUM Tests Under Long Memory," Hannover Economic Papers (HEP) dp-647, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    4. Bai, Shuyang & Taqqu, Murad S. & Zhang, Ting, 2016. "A unified approach to self-normalized block sampling," Stochastic Processes and their Applications, Elsevier, vol. 126(8), pages 2465-2493.
    5. 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.
    6. Politis, Dimitris, 2012. "On The Behavior Of Nonparametric Density And Spectral Density Estimators At Zero Points Of Their Support," University of California at San Diego, Economics Working Paper Series qt40g0z0tz, Department of Economics, UC San Diego.
    7. McElroy, Tucker & Politis, Dimitris, 2013. "Spectral Density and Spectral Distribution Inference for Long Memory Time Series via Fixed-b Asymptotics," University of California at San Diego, Economics Working Paper Series qt6164c110, Department of Economics, UC San Diego.
    8. Fabrizio Iacone & Stephen J. Leybourne & A. M. Robert Taylor, 2014. "A FIXED- b TEST FOR A BREAK IN LEVEL AT AN UNKNOWN TIME UNDER FRACTIONAL INTEGRATION," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 40-54, January.
    9. Hualde, Javier & Iacone, Fabrizio, 2017. "Fixed bandwidth asymptotics for the studentized mean of fractionally integrated processes," Economics Letters, Elsevier, vol. 150(C), pages 39-43.
    10. Javier Hualde & Fabrizio Iacone, 2015. "Autocorrelation robust inference using the Daniell kernel with fixed bandwidth," Discussion Papers 15/14, Department of Economics, University of York.
    11. Efstathios Paparoditis & Dimitris N. Politis, 2016. "A Note on the Behaviour of Nonparametric Density and Spectral Density Estimators at Zero Points of their Support," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(2), pages 182-194, March.
    12. Hualde, Javier & Iacone, Fabrizio, 2017. "Revisiting inflation in the euro area allowing for long memory," Economics Letters, Elsevier, vol. 156(C), pages 145-150.

  4. Michael W. McCracken & Tucker S. McElroy, 2012. "Multi-step ahead forecasting of vector time series," Working Papers 2012-060, Federal Reserve Bank of St. Louis.

    Cited by:

    1. McElroy, Tucker S. & Wildi, Marc, 2020. "The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions," Econometrics and Statistics, Elsevier, vol. 14(C), pages 112-130.
    2. Chevillon, Guillaume, 2016. "Multistep forecasting in the presence of location shifts," International Journal of Forecasting, Elsevier, vol. 32(1), pages 121-137.
    3. 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.

  5. Tucker S. McElroy & Thomas M. Trimbur, 2012. "Signal extraction for nonstationary multivariate time series with illustrations for trend inflation," Finance and Economics Discussion Series 2012-45, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Tucker McElroy & Michael W. McCracken, 2017. "Multistep ahead forecasting of vector time series," Econometric Reviews, Taylor & Francis Journals, vol. 36(5), pages 495-513, May.
    2. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    3. Tucker S. McElroy & Thomas M. Trimbur, 2012. "Signal extraction for nonstationary multivariate time series with illustrations for trend inflation," Finance and Economics Discussion Series 2012-45, Board of Governors of the Federal Reserve System (U.S.).
    4. McElroy, Tucker S. & Wildi, Marc, 2020. "The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions," Econometrics and Statistics, Elsevier, vol. 14(C), pages 112-130.
    5. Tucker S. McElroy & Anindya Roy, 2022. "Model identification via total Frobenius norm of multivariate spectra," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 473-495, April.

  6. Politis, D N & McElroy, Tucker S, 2009. "Fixed-b asymptotics for the studentized mean from time series with short, long or negative memory," University of California at San Diego, Economics Working Paper Series qt70c4x0sq, Department of Economics, UC San Diego.

    Cited by:

    1. Robinson Kruse & Christian Leschinski & Michael Will, 2016. "Comparing Predictive Accuracy under Long Memory - With an Application to Volatility Forecasting," CREATES Research Papers 2016-17, Department of Economics and Business Economics, Aarhus University.
    2. McElroy, Tucker S & Politis, D N, 2011. "Distribution Theory for the Studentized Mean for Long, Short, and Negative Memory Time Series," University of California at San Diego, Economics Working Paper Series qt0dr145dt, Department of Economics, UC San Diego.
    3. Wenger, Kai & Leschinski, Christian, 2018. "Fixed-Bandwidth CUSUM Tests Under Long Memory," Hannover Economic Papers (HEP) dp-647, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    4. 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.
    5. Politis, Dimitris, 2012. "On The Behavior Of Nonparametric Density And Spectral Density Estimators At Zero Points Of Their Support," University of California at San Diego, Economics Working Paper Series qt40g0z0tz, Department of Economics, UC San Diego.
    6. McElroy, Tucker & Politis, Dimitris, 2013. "Spectral Density and Spectral Distribution Inference for Long Memory Time Series via Fixed-b Asymptotics," University of California at San Diego, Economics Working Paper Series qt6164c110, Department of Economics, UC San Diego.
    7. Fabrizio Iacone & Stephen J. Leybourne & A. M. Robert Taylor, 2014. "A FIXED- b TEST FOR A BREAK IN LEVEL AT AN UNKNOWN TIME UNDER FRACTIONAL INTEGRATION," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 40-54, January.
    8. Hualde, Javier & Iacone, Fabrizio, 2017. "Fixed bandwidth asymptotics for the studentized mean of fractionally integrated processes," Economics Letters, Elsevier, vol. 150(C), pages 39-43.
    9. Javier Hualde & Fabrizio Iacone, 2015. "Autocorrelation robust inference using the Daniell kernel with fixed bandwidth," Discussion Papers 15/14, Department of Economics, University of York.
    10. Efstathios Paparoditis & Dimitris N. Politis, 2016. "A Note on the Behaviour of Nonparametric Density and Spectral Density Estimators at Zero Points of their Support," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(2), pages 182-194, March.
    11. Hualde, Javier & Iacone, Fabrizio, 2017. "Revisiting inflation in the euro area allowing for long memory," Economics Letters, Elsevier, vol. 156(C), pages 145-150.

  7. Tucker S. McElroy & Thomas M. Trimbur, 2007. "Continuous time extraction of a nonstationary signal with illustrations in continuous low-pass and band-pass filtering," Finance and Economics Discussion Series 2007-68, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Al-Zoubi, Haitham A., 2019. "Bond and option prices with permanent shocks," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 272-290.
    2. Tucker McElroy, 2013. "Forecasting continuous-time processes with applications to signal extraction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(3), pages 439-456, June.

Articles

  1. McElroy, Tucker S. & Jach, Agnieszka, 2023. "Identification of the differencing operator of a non-stationary time series via testing for zeroes in the spectral density," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).

    Cited by:

    1. Schneider, Nicolas & Strielkowski, Wadim, 2023. "Modelling the unit root properties of electricity data—A general note on time-domain applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).

  2. Carola Binder & Tucker S. Mcelroy & Xuguang S. Sheng, 2022. "The Term Structure of Uncertainty: New Evidence from Survey Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(1), pages 39-71, February.

    Cited by:

    1. Cahen-Fourot, Louison & Campiglio, Emanuele & Daumas, Louis & Miess, Michael Gregor & Yardley, Andrew, 2023. "Stranding ahoy? Heterogeneous transition beliefs and capital investment choices," Journal of Economic Behavior & Organization, Elsevier, vol. 216(C), pages 535-567.
    2. An, Zidong & Sheng, Xuguang Simon & Zheng, Xinye, 2023. "What is the role of perceived oil price shocks in inflation expectations?," Energy Economics, Elsevier, vol. 126(C).
    3. Alessandro Barbera & Dora Xia & Sonya Zhu, 2023. "The term structure of inflation forecasts disagreement and monetary policy transmission," BIS Working Papers 1114, Bank for International Settlements.

  3. McElroy Tucker, 2021. "A Diagnostic for Seasonality Based Upon Polynomial Roots of ARMA Models," Journal of Official Statistics, Sciendo, vol. 37(2), pages 367-394, June.

    Cited by:

    1. Tucker McElroy & Anindya Roy, 2022. "A Review of Seasonal Adjustment Diagnostics," International Statistical Review, International Statistical Institute, vol. 90(2), pages 259-284, August.
    2. McElroy, Tucker S. & Jach, Agnieszka, 2023. "Identification of the differencing operator of a non-stationary time series via testing for zeroes in the spectral density," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).

  4. McElroy, Tucker S. & Wildi, Marc, 2020. "The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions," Econometrics and Statistics, Elsevier, vol. 14(C), pages 112-130.

    Cited by:

    1. Amy Y. Guisinger & Michael T. Owyang & Daniel Soques, 2020. "Industrial Connectedness and Business Cycle Comovements," Working Papers 2020-052, Federal Reserve Bank of St. Louis, revised 04 Aug 2021.
    2. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.

  5. Scott R. Baker & Tucker S. McElroy & Xuguang S. Sheng, 2020. "Expectation Formation Following Large, Unexpected Shocks," The Review of Economics and Statistics, MIT Press, vol. 102(2), pages 287-303, May.

    Cited by:

    1. Constantin Bürgi & Julio L. Ortiz, 2022. "Overreaction through Anchoring," CESifo Working Paper Series 10193, CESifo.
    2. An, Zidong & Liu, Dingqian & Wu, Yuzheng, 2021. "Expectation formation following pandemic events," Economics Letters, Elsevier, vol. 200(C).
    3. de Mendonça, Helder Ferreira & Vereda, Luciano & Araujo, Mateus de Azevedo, 2022. "What type of information calls the attention of forecasters? Evidence from survey data in an emerging market," Journal of International Money and Finance, Elsevier, vol. 129(C).
    4. CHEN Cheng & SENGA Tatsuro & SUN Chang & ZHANG Hongyong, 2018. "Uncertainty, Imperfect Information, and Expectation Formation over the Firm's Life Cycle," Discussion papers 18010, Research Institute of Economy, Trade and Industry (RIETI).
    5. Constantin Bürgi & Tara M. Sinclair, 2020. "What Does Forecaster Disagreement Tell Us about the State of the Economy?," Working Papers 2020-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    6. Bannigidadmath, Deepa & Narayan, Paresh Kumar, 2021. "Economic news and the cross-section of commodity futures returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 31(C).
    7. Marcin Rzeszutek & Jørgen Vitting Andersen & Adam Szyszka & Szymon Talaga, 2024. "Subjective Well-Being of Chief Executive Officers and Its Impact on Stock Market Volatility During the COVID-19 Pandemic in Poland: Agent-Based Model Perspective," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-04723512, HAL.
    8. Sarantis Tsiaplias, 2021. "Consumer inflation expectations, income changes and economic downturns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 784-807, September.
    9. An, Zidong & Binder, Carola & Sheng, Xuguang Simon, 2023. "Gas price expectations of Chinese households," Energy Economics, Elsevier, vol. 120(C).
    10. Meinerding, Christoph & Poinelli, Andrea & Schüler, Yves, 2022. "Inflation expectations and climate concern," Discussion Papers 12/2022, Deutsche Bundesbank.
    11. Imane El Ouadghiri & Remzi Uctum, 2020. "Macroeconomic expectations and time varying heterogeneity: Evidence from individual survey data," Post-Print hal-03319091, HAL.
    12. Xu, Xin & Xu, Xiaoguang, 2023. "Monetary policy transmission modeling and policy responses," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    13. An, Zidong & Sheng, Xuguang Simon & Zheng, Xinye, 2023. "What is the role of perceived oil price shocks in inflation expectations?," Energy Economics, Elsevier, vol. 126(C).
    14. Kuang, Pei & Luca, Davide & Wei, Zhiwu & Yao, Yao, 2023. "Great or grim? Disagreement about Brexit, economic expectations and household spending," LSE Research Online Documents on Economics 119200, London School of Economics and Political Science, LSE Library.
    15. Alexander Dietrich & Keith Kuester & Gernot J. Müller & Raphael Schoenle, 2020. "News and Uncertainty about COVID-19: Survey Evidence and Short-Run Economic Impact," Working Papers 20-12R, Federal Reserve Bank of Cleveland, revised 22 Dec 2021.
    16. Brent H. Meyer & Nicholas B. Parker & Xuguang Simon Sheng, 2021. "Unit Cost Expectations and Uncertainty: Firms' Perspectives on Inflation," Working Papers 2021-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting, revised Nov 2021.
    17. Christopher S. Sutherland, 2020. "Forward Guidance and Expectation Formation: A Narrative Approach," Staff Working Papers 20-40, Bank of Canada.
    18. Carola Binder & Tucker S. Mcelroy & Xuguang S. Sheng, 2022. "The Term Structure of Uncertainty: New Evidence from Survey Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(1), pages 39-71, February.
    19. Fabrizio Ferriani & Andrea Gazzani & Filippo Natoli, 2023. "Flight to climatic safety: local natural disasters and global portfolio flows," Temi di discussione (Economic working papers) 1420, Bank of Italy, Economic Research and International Relations Area.
    20. Marcin Rzeszutek & Jørgen Vitting Andersen & Adam Szyszka & Szymon Talaga, 2024. "Subjective Well-Being of Chief Executive Officers and Its Impact on Stock Market Volatility During the COVID-19 Pandemic in Poland: Agent-Based Model Perspective," Post-Print hal-04723512, HAL.
    21. Zidong An & Salem Abo‐Zaid & Xuguang Simon Sheng, 2023. "Inattention and the impact of monetary policy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 623-643, June.
    22. Aaronson, Daniel & Brave, Scott A. & Butters, R. Andrew & Fogarty, Michael & Sacks, Daniel W. & Seo, Boyoung, 2022. "Forecasting unemployment insurance claims in realtime with Google Trends," International Journal of Forecasting, Elsevier, vol. 38(2), pages 567-581.
    23. Glas, Alexander & Heinisch, Katja, 2021. "Conditional macroeconomic forecasts: Disagreement, revisions and forecast errors," IWH Discussion Papers 7/2021, Halle Institute for Economic Research (IWH).
    24. Andrew C. Chang & Trace J. Levinson, 2023. "Raiders of the lost high‐frequency forecasts: New data and evidence on the efficiency of the Fed's forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 88-104, January.
    25. Glas, Alexander & Heinisch, Katja, 2023. "Conditional macroeconomic survey forecasts: Revisions and errors," Journal of International Money and Finance, Elsevier, vol. 138(C).
    26. MORIKAWA Masayuki, 2019. "Uncertainty in Long-Term Macroeconomic Forecasts: Ex post Evaluation of Forecasts by Economics Researchers," Discussion papers 19084, Research Institute of Economy, Trade and Industry (RIETI).
    27. Christopher S Sutherland, 2022. "Forward guidance and expectation formation: A narrative approach," BIS Working Papers 1024, Bank for International Settlements.
    28. Anat Bracha & Jenny Tang, 2022. "Inflation Levels and (In)Attention," Working Papers 22-4, Federal Reserve Bank of Boston.
    29. Gründler, Klaus & Dräger, Lena & Potrafke, Niklas, 2023. "Political Shocks and Inflation Expectations: Evidence from the 2022 Russian Invasion of Ukraine," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277673, Verein für Socialpolitik / German Economic Association.
    30. Meinerding, Christoph & Poinelli, Andrea & Schüler, Yves, 2023. "Households’ inflation expectations and concern about climate change," European Journal of Political Economy, Elsevier, vol. 80(C).
    31. Vereda, Luciano & Savignon, João & Gouveia da Silva, Tarciso, 2021. "A new method to assess the degree of information rigidity using fixed-event forecasts," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1576-1589.

  6. Wei Lin & Jianhua Z. Huang & Tucker McElroy, 2020. "Time Series Seasonal Adjustment Using Regularized Singular Value Decomposition," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 487-501, July.

    Cited by:

    1. In Choi, 2023. "Does climate change affect economic data?," Empirical Economics, Springer, vol. 64(6), pages 2939-2956, June.
    2. Tucker McElroy & Anindya Roy, 2022. "A Review of Seasonal Adjustment Diagnostics," International Statistical Review, International Statistical Institute, vol. 90(2), pages 259-284, August.
    3. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    4. Malgorzata Grzywinska-Rapca & Aleksandra A. Olejarz, 2021. "The Level of Economic Development and the Savings Rate of Households," European Research Studies Journal, European Research Studies Journal, vol. 0(2B), pages 430-442.
    5. McElroy, Tucker S. & Jach, Agnieszka, 2023. "Identification of the differencing operator of a non-stationary time series via testing for zeroes in the spectral density," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).

  7. Wildi, Marc & McElroy, Tucker S., 2019. "The trilemma between accuracy, timeliness and smoothness in real-time signal extraction," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1072-1084.

    Cited by:

    1. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    2. McElroy, Tucker S. & Wildi, Marc, 2020. "The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions," Econometrics and Statistics, Elsevier, vol. 14(C), pages 112-130.

  8. Tucker S McElroy & Agnieszka Jach, 2019. "Testing collinearity of vector time series," The Econometrics Journal, Royal Economic Society, vol. 22(2), pages 97-116.

    Cited by:

    1. McElroy, Tucker S. & Jach, Agnieszka, 2023. "Identification of the differencing operator of a non-stationary time series via testing for zeroes in the spectral density," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    2. Tucker S. McElroy & Anindya Roy, 2022. "Model identification via total Frobenius norm of multivariate spectra," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 473-495, April.

  9. Henry R. Hyatt & Tucker S. McElroy, 2019. "Labor Reallocation, Employment, and Earnings: Vector Autoregression Evidence," LABOUR, CEIS, vol. 33(4), pages 463-487, December.
    See citations under working paper version above.
  10. Tucker McElroy, 2018. "Seasonal adjustment subject to accounting constraints," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 574-589, November.

    Cited by:

    1. Tucker McElroy & Anindya Roy, 2022. "A Review of Seasonal Adjustment Diagnostics," International Statistical Review, International Statistical Institute, vol. 90(2), pages 259-284, August.

  11. Tucker McElroy, 2018. "Recursive Computation for Block†Nested Covariance Matrices," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(3), pages 299-312, May.

    Cited by:

    1. Tucker S. McElroy & Anindya Roy, 2022. "Model identification via total Frobenius norm of multivariate spectra," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 473-495, April.

  12. Tucker McElroy & Anindya Roy, 2018. "The Inverse Kullback–Leibler Method for Fitting Vector Moving Averages," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(2), pages 172-191, March.

    Cited by:

    1. McElroy Tucker, 2021. "A Diagnostic for Seasonality Based Upon Polynomial Roots of ARMA Models," Journal of Official Statistics, Sciendo, vol. 37(2), pages 367-394, June.

  13. Nagaraja, Chaitra H. & McElroy, Tucker, 2018. "The multivariate bullwhip effect," European Journal of Operational Research, Elsevier, vol. 267(1), pages 96-106.

    Cited by:

    1. Ojha, Divesh & Sahin, Funda & Shockley, Jeff & Sridharan, Sri V., 2019. "Is there a performance tradeoff in managing order fulfillment and the bullwhip effect in supply chains? The role of information sharing and information type," International Journal of Production Economics, Elsevier, vol. 208(C), pages 529-543.
    2. Agarwal, Manoj K. & Ma, Zecong & Park, Chang Hee & Zheng, Yilong, 2022. "The impact of a manufacturer’s financial liquidity on its market strategies and pricing and promotion decisions in retail grocery markets," Journal of Business Research, Elsevier, vol. 142(C), pages 844-857.
    3. Sinha, Priyank & Kumar, Sameer & Prakash, Surya, 2020. "Measuring and mitigating the effects of cost disturbance propagation in multi-echelon apparel supply chains," European Journal of Operational Research, Elsevier, vol. 282(1), pages 148-160.

  14. Tucker McElroy & Michael W. McCracken, 2017. "Multistep ahead forecasting of vector time series," Econometric Reviews, Taylor & Francis Journals, vol. 36(5), pages 495-513, May.
    See citations under working paper version above.
  15. Tucker McElroy, 2017. "Multivariate Seasonal Adjustment, Economic Identities, and Seasonal Taxonomy," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 611-625, October.

    Cited by:

    1. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    2. Rishab Guha & Serena Ng, 2019. "A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 403-436, National Bureau of Economic Research, Inc.
    3. McElroy, Tucker S. & Wildi, Marc, 2020. "The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions," Econometrics and Statistics, Elsevier, vol. 14(C), pages 112-130.
    4. Tucker S. McElroy & Anindya Roy, 2022. "Model identification via total Frobenius norm of multivariate spectra," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 473-495, April.
    5. Tucker McElroy, 2018. "Seasonal adjustment subject to accounting constraints," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 574-589, November.
    6. Brave, Scott A. & Butters, R. Andrew & Fogarty, Michael, 2022. "The perils of working with big data, and a SMALL checklist you can use to recognize them," Business Horizons, Elsevier, vol. 65(4), pages 481-492.

  16. McElroy, Tucker, 2017. "Computation of vector ARMA autocovariances," Statistics & Probability Letters, Elsevier, vol. 124(C), pages 92-96.

    Cited by:

    1. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.

  17. Chris Blakely & Tucker McElroy, 2017. "Signal extraction goodness-of-fit diagnostic tests under model parameter uncertainty: Formulations and empirical evaluation," Econometric Reviews, Taylor & Francis Journals, vol. 36(4), pages 447-467, April.

    Cited by:

    1. Tucker McElroy & Anindya Roy, 2022. "A Review of Seasonal Adjustment Diagnostics," International Statistical Review, International Statistical Institute, vol. 90(2), pages 259-284, August.
    2. Chen Baoline & McElroy Tucker S. & Pang Osbert C., 2022. "Assessing Residual Seasonality in the U.S. National Income and Product Accounts Aggregates," Journal of Official Statistics, Sciendo, vol. 38(2), pages 399-428, June.

  18. Wildi Marc & McElroy Tucker, 2016. "Optimal Real-Time Filters for Linear Prediction Problems," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 155-192, July.

    Cited by:

    1. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.

  19. Agnieszka Jach & Tucker S. McElroy & Dimitris N. Politis, 2016. "Corrigendum to ‘Subsampling Inference for the Mean of Heavy-Tailed Long-Memory Time Series’ by A. Jach, T. S. McElroy and D. N. Politis," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(5), pages 713-720, September.

    Cited by:

    1. Wen Cao & Clifford Hurvich & Philippe Soulier, 2012. "Drift in Transaction-Level Asset Price Models," Working Papers hal-00756372, HAL.

  20. T. S. McElroy, 2016. "Nonnested model comparisons for time series," Biometrika, Biometrika Trust, vol. 103(4), pages 905-914.

    Cited by:

    1. Francesco Bravo, 2022. "Misspecified semiparametric model selection with weakly dependent observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 558-586, July.

  21. McElroy, Tucker S. & Holan, Scott H., 2016. "Computation of the autocovariances for time series with multiple long-range persistencies," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 44-56.

    Cited by:

    1. Paul M. Beaumont & Aaron D. Smallwood, 2024. "Conditional sum of squares estimation of k-factor GARMA models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(3), pages 501-543, September.
    2. Manabu Asai & Shelton Peiris & Michael McAleer & David E. Allen, 2018. "Cointegrated Dynamics for A Generalized Long Memory Process: An Application to Interest Rates," Documentos de Trabajo del ICAE 2018-22, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    3. Asai, M. & McAleer, M.J. & Peiris, S., 2017. "Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory," Econometric Institute Research Papers EI2017-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Richard Hunt & Shelton Peiris & Neville Weber, 2022. "Estimation methods for stationary Gegenbauer processes," Statistical Papers, Springer, vol. 63(6), pages 1707-1741, December.
    5. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    6. Tommaso Proietti & Federico Maddanu, 2021. "Modelling Cycles in Climate Series: the Fractional Sinusoidal Waveform Process," CEIS Research Paper 518, Tor Vergata University, CEIS, revised 19 Oct 2021.
    7. Asai, M. & Peiris, S. & McAleer, M.J. & Allen, D.E., 2018. "Cointegrated Dynamics for A Generalized Long Memory Process," Econometric Institute Research Papers EI 2018-32, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Beaumont, Paul & Smallwood, Aaron, 2019. "Conditional Sum of Squares Estimation of Multiple Frequency Long Memory Models," MPRA Paper 96314, University Library of Munich, Germany.

  22. Tucker McElroy, 2015. "When are Direct Multi‐step and Iterative Forecasts Identical?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(4), pages 315-336, July.

    Cited by:

    1. Mikkel Plagborg-Møller & Christian K. Wolf, 2020. "Local Projections and VARs Estimate the Same Impulse Responses," Working Papers 2020-16, Princeton University. Economics Department..

  23. Tucker McElroy & Brian Monsell, 2015. "Model Estimation, Prediction, and Signal Extraction for Nonstationary Stock and Flow Time Series Observed at Mixed Frequencies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1284-1303, September.

    Cited by:

    1. Tucker McElroy & Michael W. McCracken, 2017. "Multistep ahead forecasting of vector time series," Econometric Reviews, Taylor & Francis Journals, vol. 36(5), pages 495-513, May.

  24. Tucker McElroy & Thomas Trimbur, 2015. "Signal Extraction for Non-Stationary Multivariate Time Series with Illustrations for Trend Inflation," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(2), pages 209-227, March.
    See citations under working paper version above.
  25. McElroy Tucker S. & Maravall Agustin, 2014. "Optimal Signal Extraction with Correlated Components," Journal of Time Series Econometrics, De Gruyter, vol. 6(2), pages 237-273, July.

    Cited by:

    1. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    2. Tommaso Proietti, 2019. "Predictability, Real Time Estimation, and the Formulation of Unobserved Components Models," CEIS Research Paper 455, Tor Vergata University, CEIS, revised 22 Mar 2019.

  26. McElroy, Tucker S. & Politis, Dimitris N., 2014. "Spectral density and spectral distribution inference for long memory time series via fixed-b asymptotics," Journal of Econometrics, Elsevier, vol. 182(1), pages 211-225.
    See citations under working paper version above.
  27. McElroy, Tucker & Politis, Dimitris N., 2013. "Distribution theory for the studentized mean for long, short, and negative memory time series," Journal of Econometrics, Elsevier, vol. 177(1), pages 60-74.
    See citations under working paper version above.
  28. Tucker McElroy, 2013. "Forecasting continuous-time processes with applications to signal extraction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(3), pages 439-456, June.

    Cited by:

    1. Brockwell, Peter J. & Lindner, Alexander, 2015. "CARMA processes as solutions of integral equations," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 221-227.
    2. P. Brockwell, 2014. "Recent results in the theory and applications of CARMA processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(4), pages 647-685, August.
    3. Trimbur Thomas & McElroy Tucker, 2017. "Signal Extraction for Nonstationary Time Series with Diverse Sampling Rules," Journal of Time Series Econometrics, De Gruyter, vol. 9(1), pages 1-37, January.

  29. McElroy, Tucker & Wildi, Marc, 2013. "Multi-step-ahead estimation of time series models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 378-394.

    Cited by:

    1. Shahedul A. Khan, 2018. "Exponentiated Weibull regression for time-to-event data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(2), pages 328-354, April.
    2. Di Piazza, A. & Di Piazza, M.C. & La Tona, G. & Luna, M., 2021. "An artificial neural network-based forecasting model of energy-related time series for electrical grid management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 294-305.
    3. Wildi Marc & McElroy Tucker, 2016. "Optimal Real-Time Filters for Linear Prediction Problems," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 155-192, July.
    4. Chevillon, Guillaume, 2016. "Multistep forecasting in the presence of location shifts," International Journal of Forecasting, Elsevier, vol. 32(1), pages 121-137.

  30. Agnieszka Jach & Tucker McElroy & Dimitris N. Politis, 2012. "Subsampling inference for the mean of heavy‐tailed long‐memory time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(1), pages 96-111, January.

    Cited by:

    1. Wen Cao & Clifford Hurvich & Philippe Soulier, 2012. "Drift in Transaction-Level Asset Price Models," Working Papers hal-00756372, HAL.
    2. McElroy, Tucker S & Politis, D N, 2011. "Distribution Theory for the Studentized Mean for Long, Short, and Negative Memory Time Series," University of California at San Diego, Economics Working Paper Series qt0dr145dt, Department of Economics, UC San Diego.
    3. Bai, Shuyang & Taqqu, Murad S. & Zhang, Ting, 2016. "A unified approach to self-normalized block sampling," Stochastic Processes and their Applications, Elsevier, vol. 126(8), pages 2465-2493.
    4. Zhang, Rong-Mao & Sin, Chor-yiu (CY) & Ling, Shiqing, 2015. "On functional limits of short- and long-memory linear processes with GARCH(1,1) noises," Stochastic Processes and their Applications, Elsevier, vol. 125(2), pages 482-512.
    5. McElroy, Tucker & Jach, Agnieszka, 2012. "Tail index estimation in the presence of long-memory dynamics," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 266-282.
    6. Agnieszka Jach & Tucker S. McElroy & Dimitris N. Politis, 2016. "Corrigendum to ‘Subsampling Inference for the Mean of Heavy-Tailed Long-Memory Time Series’ by A. Jach, T. S. McElroy and D. N. Politis," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(5), pages 713-720, September.
    7. Mikihito Nishi, 2023. "Testing for Stationary or Persistent Coefficient Randomness in Predictive Regressions," Papers 2309.04926, arXiv.org, revised May 2024.
    8. Francesco Giordano & Pietro Coretto, 2020. "A Monte Carlo subsampling method for estimating the distribution of signal-to-noise ratio statistics in nonparametric time series regression models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(3), pages 483-514, September.

  31. Theodore Alexandrov & Silvia Bianconcini & Estela Bee Dagum & Peter Maass & Tucker S. McElroy, 2012. "A Review of Some Modern Approaches to the Problem of Trend Extraction," Econometric Reviews, Taylor & Francis Journals, vol. 31(6), pages 593-624, November.

    Cited by:

    1. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas, 2015. "Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(7), pages 560-573, November.
    2. Xu Huang & Hossein Hassani & Mansi Ghodsi & Zinnia Mukherjee & Rangan Gupta, 2016. "Do Trend Extraction Approaches Affect Causality Detection in Climate Change Studies?," Working Papers 201660, University of Pretoria, Department of Economics.
    3. Linh Nguyen & Vilém Novák & Soheyla Mirshahi, 2020. "Trend‐cycle Estimation Using Fuzzy Transform and Its Application for Identifying Bull and Bear Phases in Markets," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(3), pages 111-124, July.
    4. Jonathan Olusegun Famoroti & Omolade Adeleke, 2023. "Analysis of Wamz’s Economic Growth and Monetary Policy Using the Markov Switching Approach," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 7(4), pages 142-156, April.
    5. Michel Grun-Rehomme & OLGA VASYECHKO, 2013. "Methodes De Lissage D’Une Serie Temporelle :Le Probleme Des Extremites," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 56(2), pages 163-174.
    6. Huang, Xuan & An, Haizhong & Gao, Xiangyun & Hao, Xiaoqing & Liu, Pengpeng, 2015. "Multiresolution transmission of the correlation modes between bivariate time series based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 493-506.
    7. Tung-Lam Dao, 2014. "Momentum Strategies with L1 Filter," Papers 1403.4069, arXiv.org.
    8. Herman O. Stekler & Yongchen Zhao, 2016. "Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set," Working Papers 2016-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    9. Feliu Serra-Burriel & Pedro Delicado & Fernando M. Cucchietti, 2021. "Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis," Mathematics, MDPI, vol. 9(11), pages 1-22, June.
    10. Elena Barton & Basad Al-Sarray & Stéphane Chrétien & Kavya Jagan, 2018. "Decomposition of Dynamical Signals into Jumps, Oscillatory Patterns, and Possible Outliers," Mathematics, MDPI, vol. 6(7), pages 1-13, July.
    11. Anusha, "undated". "Evaluating reliability of some symmetric and asymmetric univariate filters," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2015-030, Indira Gandhi Institute of Development Research, Mumbai, India.
    12. Riyadh Nazar Ali Algburi & Hongli Gao, 2019. "Health Assessment and Fault Detection System for an Industrial Robot Using the Rotary Encoder Signal," Energies, MDPI, vol. 12(14), pages 1-25, July.
    13. Cremaschini, Alessandro & Maruotti, Antonello, 2023. "A finite mixture analysis of structural breaks in the G-7 gross domestic product series," Research in Economics, Elsevier, vol. 77(1), pages 76-90.
    14. Bhaskar Jyoti Neog & Bimal Kishore Sahoo, 2020. "Job Reallocation Dynamics in India: Evidence from Large Manufacturing Plants," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 934-959, August.
    15. Yoon, Gawon, 2015. "Locating change-points in Hodrick–Prescott trends with an application to US real GDP: A generalized unobserved components model approach," Economic Modelling, Elsevier, vol. 45(C), pages 136-141.
    16. McElroy, Tucker S. & Wildi, Marc, 2020. "The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions," Econometrics and Statistics, Elsevier, vol. 14(C), pages 112-130.
    17. McElroy, Tucker S. & Jach, Agnieszka, 2023. "Identification of the differencing operator of a non-stationary time series via testing for zeroes in the spectral density," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    18. Wildi Marc & McElroy Tucker, 2016. "Optimal Real-Time Filters for Linear Prediction Problems," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 155-192, July.
    19. Fritz, Marlon, 2019. "Steady state adjusting trends using a data-driven local polynomial regression," Economic Modelling, Elsevier, vol. 83(C), pages 312-325.
    20. Trimbur Thomas & McElroy Tucker, 2017. "Signal Extraction for Nonstationary Time Series with Diverse Sampling Rules," Journal of Time Series Econometrics, De Gruyter, vol. 9(1), pages 1-37, January.
    21. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou, 2021. "Gold Against the Machine," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 5-28, January.

  32. McElroy, Tucker & Politis, Dimitris N., 2012. "Fixed-B Asymptotics For The Studentized Mean From Time Series With Short, Long, Or Negative Memory," Econometric Theory, Cambridge University Press, vol. 28(2), pages 471-481, April.
    See citations under working paper version above.
  33. McElroy, Tucker & Jach, Agnieszka, 2012. "Tail index estimation in the presence of long-memory dynamics," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 266-282.

    Cited by:

    1. Hubert, Mia & Dierckx, Goedele & Vanpaemel, Dina, 2013. "Detecting influential data points for the Hill estimator in Pareto-type distributions," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 13-28.
    2. Giuseppe Arbia & Riccardo Bramante & Silvia Facchinetti, 2020. "Least Quartic Regression Criterion to Evaluate Systematic Risk in the Presence of Co-Skewness and Co-Kurtosis," Risks, MDPI, vol. 8(3), pages 1-14, September.

  34. Tucker McElroy & Agnieszka Jach, 2012. "Subsampling inference for the autocovariances and autocorrelations of long-memory heavy- tailed linear time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(6), pages 935-953, November.

    Cited by:

    1. Wen Cao & Clifford Hurvich & Philippe Soulier, 2012. "Drift in Transaction-Level Asset Price Models," Working Papers hal-00756372, HAL.
    2. Zhang, Rong-Mao & Sin, Chor-yiu (CY) & Ling, Shiqing, 2015. "On functional limits of short- and long-memory linear processes with GARCH(1,1) noises," Stochastic Processes and their Applications, Elsevier, vol. 125(2), pages 482-512.

  35. Tucker McElroy & Thomas M. Trimbur, 2011. "On the Discretization of Continuous-Time Filters for Nonstationary Stock and Flow Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 30(5), pages 475-513, October.

    Cited by:

    1. Al-Zoubi, Haitham A., 2019. "Bond and option prices with permanent shocks," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 272-290.
    2. Trimbur Thomas & McElroy Tucker, 2017. "Signal Extraction for Nonstationary Time Series with Diverse Sampling Rules," Journal of Time Series Econometrics, De Gruyter, vol. 9(1), pages 1-37, January.
    3. Tucker McElroy, 2013. "Forecasting continuous-time processes with applications to signal extraction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(3), pages 439-456, June.

  36. Tucker McElroy, 2011. "A nonparametric method for asymmetrically extending signal extraction filters," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 597-621, November.

    Cited by:

    1. Luis Fernando Melo Velandia & Daniel Parra Amado, 2014. "Efectos calendario sobre la producción industrial en Colombia," Borradores de Economia 820, Banco de la Republica de Colombia.
    2. Wildi Marc & McElroy Tucker, 2016. "Optimal Real-Time Filters for Linear Prediction Problems," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 155-192, July.

  37. McElroy Tucker S, 2010. "A Nonlinear Algorithm for Seasonal Adjustment in Multiplicative Component Decompositions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-23, September.

    Cited by:

    1. Alexander Dokumentov & Rob J. Hyndman, 2015. "STR: A Seasonal-Trend Decomposition Procedure Based on Regression," Monash Econometrics and Business Statistics Working Papers 13/15, Monash University, Department of Econometrics and Business Statistics.
    2. Wildi Marc & McElroy Tucker, 2016. "Optimal Real-Time Filters for Linear Prediction Problems," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 155-192, July.

  38. McElroy Tucker & Wildi Marc, 2010. "Signal Extraction Revision Variances as a Goodness-of-Fit Measure," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-32, June.

    Cited by:

    1. Agustín Maravall Herrero & Domingo Pérez Cañete, 2011. "Applying and interpreting model-based seasonal adjustment. The euro-area industrial production series," Working Papers 1116, Banco de España.
    2. McElroy, Tucker S. & Wildi, Marc, 2020. "The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions," Econometrics and Statistics, Elsevier, vol. 14(C), pages 112-130.
    3. Wildi Marc & McElroy Tucker, 2016. "Optimal Real-Time Filters for Linear Prediction Problems," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 155-192, July.

  39. McElroy, Tucker & Holan, Scott, 2009. "A local spectral approach for assessing time series model misspecification," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 604-621, April.

    Cited by:

    1. Wildi Marc & McElroy Tucker, 2016. "Optimal Real-Time Filters for Linear Prediction Problems," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 155-192, July.
    2. Tucker S. McElroy & Anindya Roy, 2022. "Model identification via total Frobenius norm of multivariate spectra," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 473-495, April.
    3. McElroy, Tucker & Wildi, Marc, 2013. "Multi-step-ahead estimation of time series models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 378-394.

  40. McElroy, Tucker, 2008. "Matrix Formulas For Nonstationary Arima Signal Extraction," Econometric Theory, Cambridge University Press, vol. 24(4), pages 988-1009, August.

    Cited by:

    1. Dermoune Azzouz & Djehiche Boualem & Rahmania Nadji, 2009. "Multivariate Extension of the Hodrick-Prescott Filter-Optimality and Characterization," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(3), pages 1-35, May.
    2. Blöchl, Andreas, 2014. "Penalized Splines as Frequency Selective Filters - Reducing the Excess Variability at the Margins," Discussion Papers in Economics 20687, University of Munich, Department of Economics.
    3. Stephen Pollock, 2014. "Trends Cycles and Seasons: Econometric Methods of Signal Extraction," Discussion Papers in Economics 14/04, Division of Economics, School of Business, University of Leicester.
    4. Dimitrios Thomakos, 2008. "Optimal Linear Filtering, Smoothing and Trend Extraction for Processes with Unit Roots and Cointegration," Working Papers 0024, University of Peloponnese, Department of Economics.
    5. McElroy Tucker S, 2010. "A Nonlinear Algorithm for Seasonal Adjustment in Multiplicative Component Decompositions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-23, September.
    6. Flaig Gebhard, 2015. "Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 235(6), pages 518-538, December.
    7. Guy Mélard, 2016. "On some remarks about SEATS signal extraction," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 53-98, March.
    8. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    9. Víctor M. Guerrero & Adriana Galicia‐Vázquez, 2010. "Trend estimation of financial time series," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(3), pages 205-223, May.
    10. Agustín Maravall Herrero & Domingo Pérez Cañete, 2011. "Applying and interpreting model-based seasonal adjustment. The euro-area industrial production series," Working Papers 1116, Banco de España.
    11. Irma Hindrayanto & John A.D. Aston & Siem Jan Koopman & Marius Ooms, 2010. "Modeling Trigonometric Seasonal Components for Monthly Economic Time Series," Tinbergen Institute Discussion Papers 10-018/4, Tinbergen Institute.
    12. Tucker S. McElroy & Thomas M. Trimbur, 2012. "Signal extraction for nonstationary multivariate time series with illustrations for trend inflation," Finance and Economics Discussion Series 2012-45, Board of Governors of the Federal Reserve System (U.S.).
    13. D. Stephen G. Pollock & Emi Mise, 2022. "A Wiener–Kolmogorov Filter for Seasonal Adjustment and the Cholesky Decomposition of a Toeplitz Matrix," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 913-933, March.
    14. Irene Nandutu & Marcellin Atemkeng & Nokubonga Mgqatsa & Sakayo Toadoum Sari & Patrice Okouma & Rockefeller Rockefeller & Theophilus Ansah-Narh & Jean Louis Ebongue Kedieng Fendji & Franklin Tchakount, 2022. "Error Correction Based Deep Neural Networks for Modeling and Predicting South African Wildlife–Vehicle Collision Data," Mathematics, MDPI, vol. 10(21), pages 1-31, October.
    15. David F. Findley & Demetra P. Lytras & Agustin Maravall, 2016. "Illuminating ARIMA model-based seasonal adjustment with three fundamental seasonal models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 11-52, March.
    16. McElroy, Tucker S. & Wildi, Marc, 2020. "The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions," Econometrics and Statistics, Elsevier, vol. 14(C), pages 112-130.
    17. McElroy Tucker S. & Maravall Agustin, 2014. "Optimal Signal Extraction with Correlated Components," Journal of Time Series Econometrics, De Gruyter, vol. 6(2), pages 237-273, July.
    18. McElroy, Tucker S. & Jach, Agnieszka, 2023. "Identification of the differencing operator of a non-stationary time series via testing for zeroes in the spectral density," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    19. Bell, William R., 2011. "REGCMPNT A Fortran Program for Regression Models with ARIMA Component Errors," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 41(i07).
    20. Bloechl, Andreas, 2014. "Reducing the Excess Variability of the Hodrick-Prescott Filter by Flexible Penalization," Discussion Papers in Economics 17940, University of Munich, Department of Economics.
    21. Bloechl, Andreas, 2014. "Penalized Splines, Mixed Models and the Wiener-Kolmogorov Filter," Discussion Papers in Economics 21406, University of Munich, Department of Economics.
    22. McElroy, Tucker & Wildi, Marc, 2013. "Multi-step-ahead estimation of time series models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 378-394.
    23. Dias, Maria Helena Ambrosio & Dias, Joilson, 2010. "Measuring the Cyclical Component of a Time Series: a New Proposed Methodology," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 30(1), October.
    24. Tucker McElroy, 2013. "Forecasting continuous-time processes with applications to signal extraction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(3), pages 439-456, June.
    25. Andreas Blöchl & Gebhard Flaig, 2014. "The Hodrick-Prescott Filter with a Time-Varying Penalization Parameter. An Application for the Trend Estimation of Global Temperature," CESifo Working Paper Series 4577, CESifo.
    26. McElroy Tucker & Wildi Marc, 2010. "Signal Extraction Revision Variances as a Goodness-of-Fit Measure," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-32, June.

  41. Tucker McElroy, 2008. "Exact formulas for the Hodrick-Prescott filter," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 209-217, March.

    Cited by:

    1. Coeurdacier, Nicolas & Kollmann, Robert Miguel W. K. & Martin, Philippe J., 2008. "International portfolios, capital accumulation and foreign assets dynamics," Discussion Paper Series 1: Economic Studies 2008,19, Deutsche Bundesbank.
    2. Henry R. Hyatt & Tucker S. McElroy, 2019. "Labor Reallocation, Employment, and Earnings: Vector Autoregression Evidence," LABOUR, CEIS, vol. 33(4), pages 463-487, December.
    3. Maravall, A. & del Rio, A., 2007. "Temporal aggregation, systematic sampling, and the Hodrick-Prescott filter," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 975-998, October.
    4. James D. Hamilton, 2017. "Why You Should Never Use the Hodrick-Prescott Filter," NBER Working Papers 23429, National Bureau of Economic Research, Inc.
    5. Peter C. B. Phillips & Sainan Jin, 2015. "Business Cycles, Trend Elimination, and the HP Filter," Cowles Foundation Discussion Papers 2005, Cowles Foundation for Research in Economics, Yale University.
    6. Dilip Nachane & Aditi Chaubal, 2022. "A Comparative Evaluation of Some DSP Filters vis-à-vis Commonly Used Economic Filters," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 161-190, September.
    7. Hall, Viv B & Thomson, Peter, 2022. "A boosted HP filter for business cycle analysis: evidence from New Zealand’s small open economy," Working Paper Series 21184, Victoria University of Wellington, School of Economics and Finance.
    8. McElroy, Tucker S. & Wildi, Marc, 2020. "The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions," Econometrics and Statistics, Elsevier, vol. 14(C), pages 112-130.
    9. Elżbieta Szaruga & Zuzanna Kłos-Adamkiewicz & Agnieszka Gozdek & Elżbieta Załoga, 2021. "Linkages between Energy Delivery and Economic Growth from the Point of View of Sustainable Development and Seaports," Energies, MDPI, vol. 14(14), pages 1-61, July.
    10. Hall, Viv B & Thomson, Peter, 2020. "Does Hamilton’s OLS regression provide a “better alternative” to the Hodrick-Prescott filter? A New Zealand Business Cycle Perspective," Working Paper Series 21070, Victoria University of Wellington, School of Economics and Finance.
    11. Wildi Marc & McElroy Tucker, 2016. "Optimal Real-Time Filters for Linear Prediction Problems," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 155-192, July.
    12. Kiki Verico, 2021. "Global Pandemic 2020: Indonesia’s Output Gap and Middle-Income Trap Scenario," LPEM FEBUI Working Papers 202157, LPEM, Faculty of Economics and Business, University of Indonesia, revised 2021.
    13. McElroy, Tucker & Wildi, Marc, 2013. "Multi-step-ahead estimation of time series models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 378-394.
    14. Eva Biswas & Farzad Sabzikar & Peter C. B. Phillips, 2022. "Boosting the HP Filter for Trending Time Series with Long Range Dependence," Cowles Foundation Discussion Papers 2347, Cowles Foundation for Research in Economics, Yale University.
    15. Nicolas Coeurdacier, 2011. "Limited participation and International Risk-Sharing," 2011 Meeting Papers 613, Society for Economic Dynamics.
    16. Kristian Jönsson, 2010. "Trend extraction with a judgement-augmented hodrick–prescott filter," Empirical Economics, Springer, vol. 39(3), pages 703-711, December.

  42. McElroy, Tucker & Sutcliffe, Andrew, 2006. "An iterated parametric approach to nonstationary signal extraction," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2206-2231, May.

    Cited by:

    1. Stephen Pollock, 2014. "Trends Cycles and Seasons: Econometric Methods of Signal Extraction," Discussion Papers in Economics 14/04, Division of Economics, School of Business, University of Leicester.
    2. McElroy Tucker S, 2010. "A Nonlinear Algorithm for Seasonal Adjustment in Multiplicative Component Decompositions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-23, September.
    3. Proietti, Tommaso, 2007. "Signal extraction and filtering by linear semiparametric methods," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 935-958, October.
    4. Tucker S. McElroy & Thomas M. Trimbur, 2012. "Signal extraction for nonstationary multivariate time series with illustrations for trend inflation," Finance and Economics Discussion Series 2012-45, Board of Governors of the Federal Reserve System (U.S.).
    5. David F. Findley & Demetra P. Lytras & Agustin Maravall, 2016. "Illuminating ARIMA model-based seasonal adjustment with three fundamental seasonal models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 11-52, March.
    6. McElroy Tucker S. & Maravall Agustin, 2014. "Optimal Signal Extraction with Correlated Components," Journal of Time Series Econometrics, De Gruyter, vol. 6(2), pages 237-273, July.
    7. Dias, Maria Helena Ambrosio & Dias, Joilson, 2010. "Measuring the Cyclical Component of a Time Series: a New Proposed Methodology," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 30(1), October.

  43. McElroy, Tucker & Politis, Dimitris N., 2002. "Robust Inference For The Mean In The Presence Of Serial Correlation And Heavy-Tailed Distributions," Econometric Theory, Cambridge University Press, vol. 18(5), pages 1019-1039, October.

    Cited by:

    1. Piotr Kokoszka & Michael Wolf, 2002. "Subsampling the mean of heavy-tailed dependent observations," Economics Working Papers 600, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Bai, Shuyang & Taqqu, Murad S. & Zhang, Ting, 2016. "A unified approach to self-normalized block sampling," Stochastic Processes and their Applications, Elsevier, vol. 126(8), pages 2465-2493.
    3. Piotr Kokoszka & Michael Wolf, 2004. "Subsampling the mean of heavy‐tailed dependent observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(2), pages 217-234, March.

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