IDEAS home Printed from https://ideas.repec.org/a/sbe/breart/v40y2021i2a81082.html

Monte Carlo Test for Stochastic Trend in Space State Models for the Location-Scale Family

Author

Listed:
  • Silva, Ivair Ramos
  • Ernesto, Dulcidia
  • Oliveira, Fernando
  • Marques, Reinaldo
  • Oliveira, Anderson

Abstract

In space state models for time series, a key point is the decision between modeling the trend of non-stationary processes through a deterministic or a stochastic term. The present paper introduces a Monte Carlo hypothesis test procedure to guide in such a decision. The method works for any time series distribution belonging to the location-scale family. The proposed method provides an alpha-level test for any time series of length greater than 3 and it does not demand assumptions on the distribution of the trend term when it is actually stochastic.

Suggested Citation

  • Silva, Ivair Ramos & Ernesto, Dulcidia & Oliveira, Fernando & Marques, Reinaldo & Oliveira, Anderson, 2021. "Monte Carlo Test for Stochastic Trend in Space State Models for the Location-Scale Family," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 40(2), April.
    • Handle: RePEc:sbe:breart:v:40:y:2021:i:2:a:81082
    as

    Download full text from publisher

    File URL: https://periodicos.fgv.br/bre/article/view/81082
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Duczmal, Luiz & Assuncao, Renato, 2004. "A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 269-286, March.
    2. Kaddour Hadri & Yao Rao, 2009. "KPSS test and model misspecifications," Applied Economics Letters, Taylor & Francis Journals, vol. 16(12), pages 1187-1190.
    3. Perron, Pierre & Yabu, Tomoyoshi, 2009. "Estimating deterministic trends with an integrated or stationary noise component," Journal of Econometrics, Elsevier, vol. 151(1), pages 56-69, July.
    4. Chen, Yichao & Pun, Chi Seng, 2019. "A bootstrap-based KPSS test for functional time series," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    5. Graham Elliott & Ulrich K. Muller, 2006. "Efficient Tests for General Persistent Time Variation in Regression Coefficients," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(4), pages 907-940.
    6. Franco, Glaura C & Souza, Reinaldo C, 2002. "A Comparison of Methods for Bootstrapping in the Local Level Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(1), pages 27-38, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Dong Jin Lee, 2021. "Bootstrap tests for structural breaks when the regressors and the serially correlated error term are unstable," Bulletin of Economic Research, Wiley Blackwell, vol. 73(2), pages 212-229, April.
    3. Mohsen Bahmani-Oskooee & Tsangyao Chang & Zahra (Mila) Elmi & Omid Ranjbar, 2018. "Re-testing Prebisch–Singer hypothesis: new evidence using Fourier quantile unit root test," Applied Economics, Taylor & Francis Journals, vol. 50(4), pages 441-454, January.
    4. Pierre Perron & Yohei Yamamoto, 2022. "Structural change tests under heteroskedasticity: Joint estimation versus two‐steps methods," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(3), pages 389-411, May.
    5. László KÓNYA, 2023. "Per Capita Income Convergence and Divergence of Selected OECD Countries to and from the US: A Reappraisal for the period 1900-2018," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 23(1), pages 33-56.
    6. Jingjing Yang, 2017. "Consistency of Trend Break Point Estimator with Underspecified Break Number," Econometrics, MDPI, vol. 5(1), pages 1-19, January.
    7. Müller, Ulrich K. & Wang, Yulong, 2019. "Nearly weighted risk minimal unbiased estimation," Journal of Econometrics, Elsevier, vol. 209(1), pages 18-34.
    8. Atanasov, Victoria, 2021. "Unemployment and aggregate stock returns," Journal of Banking & Finance, Elsevier, vol. 129(C).
    9. Xu, Ke-Li, 2012. "Robustifying multivariate trend tests to nonstationary volatility," Journal of Econometrics, Elsevier, vol. 169(2), pages 147-154.
    10. Ivan Mendieta-Muñoz, 2014. "Is there any relationship between the rates of interest and profit in the U.S. economy?," Studies in Economics 1416, School of Economics, University of Kent.
    11. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    12. Ben Omrane, Walid & Savaser, Tanseli & Welch, Robert & Zhou, Xinyao, 2019. "Time-varying effects of macroeconomic news on euro-dollar returns," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    13. Esteve Vicente & Prats Maria A., 2021. "Structural Breaks and Explosive Behavior in the Long-Run: The Case of Australian Real House Prices, 1870–2020," Economics - The Open-Access, Open-Assessment Journal, De Gruyter, vol. 15(1), pages 72-84, January.
    14. Demetrescu, Matei & Georgiev, Iliyan & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2022. "Testing for episodic predictability in stock returns," Journal of Econometrics, Elsevier, vol. 227(1), pages 85-113.
    15. Zaklan, Aleksandar & Abrell, Jan & Neumann, Anne, 2016. "Stationarity changes in long-run energy commodity prices," Energy Economics, Elsevier, vol. 59(C), pages 96-103.
    16. Dong Jin Lee, 2009. "Testing Parameter Stability in Quantile Models: An Application to the U.S. Inflation Process," Working papers 2009-26, University of Connecticut, Department of Economics.
    17. Lee, Dong Jin & Son, Jong Chil, 2013. "Nonlinearity and structural breaks in monetary policy rules with stock prices," Economic Modelling, Elsevier, vol. 31(C), pages 1-11.
    18. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 207-229, August.
    19. Yu, Deshui & Huang, Difang & Chen, Li & Li, Luyang, 2023. "Forecasting dividend growth: The role of adjusted earnings yield," Economic Modelling, Elsevier, vol. 120(C).
    20. Yamamoto, Yohei & Tanaka, Shinya, 2015. "Testing for factor loading structural change under common breaks," Journal of Econometrics, Elsevier, vol. 189(1), pages 187-206.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sbe:breart:v:40:y:2021:i:2:a:81082. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Núcleo de Computação da FGV EPGE (email available below). General contact details of provider: https://edirc.repec.org/data/sbeeeea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.