IDEAS home Printed from https://ideas.repec.org/p/udg/wpeudg/011.html
   My bibliography  Save this paper

Time-series regression models to study the short-term effects of environmental factors on health

Author

Listed:
  • Tobías, Aureli
  • Saez, Marc

Abstract

Time series regression models are especially suitable in epidemiology for evaluating short-term effects of time-varying exposures on health. The problem is that potential for confounding in time series regression is very high. Thus, it is important that trend and seasonality are properly accounted for. Our paper reviews the statistical models commonly used in time-series regression methods, specially allowing for serial correlation, make them potentially useful for selected epidemiological purposes. In particular, we discuss the use of time-series regression for counts using a wide range Generalised Linear Models as well as Generalised Additive Models. In addition, recently critical points in using statistical software for GAM were stressed, and reanalyses of time series data on air pollution and health were performed in order to update already published. Applications are offered through an example on the relationship between asthma emergency admissions and photochemical air pollutants in Madrid for the period 1995-1998, of how these methods are employed.

Suggested Citation

  • Tobías, Aureli & Saez, Marc, 2004. "Time-series regression models to study the short-term effects of environmental factors on health," Working Papers of the Department of Economics, University of Girona 11, Department of Economics, University of Girona.
    • Handle: RePEc:udg:wpeudg:011
    as

    Download full text from publisher

    File URL: http://www3.udg.edu/fcee/economia/n11.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. M. J. Campbell, 1994. "Time Series Regression for Counts: An Investigation into the Relationship between Sudden Infant Death Syndrome and Environmental Temperature," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(2), pages 191-208, March.
    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. Hope Corman & Dhaval Dave & Nancy E. Reichman, 2018. "Evolution of the Infant Health Production Function," Southern Economic Journal, John Wiley & Sons, vol. 85(1), pages 6-47, July.
    2. HEINEN, Andréas, 2003. "Modelling time series count data: an autoregressive conditional Poisson model," LIDAM Discussion Papers CORE 2003062, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Neil Shephard, 1995. "Generalized linear autoregressions," Economics Papers 8., Economics Group, Nuffield College, University of Oxford.
    4. Shang, Zuofeng, 2012. "On latent process models in multi-dimensional space," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1259-1266.
    5. Richard A. Davis & Thiago do Rêgo Sousa & Claudia Klüppelberg, 2021. "Indirect inference for time series using the empirical characteristic function and control variates," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(5-6), pages 653-684, September.
    6. Chigozie E. Utazi, 2017. "Bayesian Single Changepoint Estimation in a Parameter-driven Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 765-779, September.
    7. Wu, Rongning, 2012. "On variance estimation in a negative binomial time series regression model," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 145-155.

    More about this item

    Keywords

    Time-series; Poisson; GLM; GAM; autocorrelation; overdispersion; air pollution;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:udg:wpeudg:011. 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: Germà Coenders (email available below). General contact details of provider: https://edirc.repec.org/data/deudges.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.