IDEAS home Printed from https://ideas.repec.org/a/spt/rmkjrc/v8y2021i1f8_1_1.html

Multiple Imputation for Missing Values with an Empirical Application

Author

Listed:
  • Theodora Sotiropoulou
  • Stefanos Giakoumatos
  • Antonios Georgopoulos

Abstract

Missing data are the most common problem in many research areas. For cross-section and time-series data, imputation can be a challenging problem. The most widely used method for filling missing observations is the multiple imputation which increase the number of the available data and thereby reducing biases that may occur when observations with missing values are simply deleted. The main purpose of this paper is to employ a bootstrapping expectation–maximization (EM) algorithm in order to impute missing values mainly to economic data. In the application we use a dataset that is consisted by annual panel data for the 27 countries of the European Union covering the period 2000-2017. The data were obtained from the databases of World Bank and Eurostat namely the Global Financial Development Database, The Standardized World Income Inequality Database by Solt (2019) and the World Development Indicators. Different indicators were chosen representing the development of banking system and stock markets, economic growth, economic inequality, innovation, fiscal policy, physical and human capital, and trade openness. Finally, diagnostic tools are used inspecting the imputations that are created.

Suggested Citation

  • Theodora Sotiropoulou & Stefanos Giakoumatos & Antonios Georgopoulos, 2021. "Multiple Imputation for Missing Values with an Empirical Application," Journal of Risk & Control, SCIENPRESS Ltd, vol. 8(1), pages 1-1.
    • Handle: RePEc:spt:rmkjrc:v:8:y:2021:i:1:f:8_1_1
    as

    Download full text from publisher

    File URL: http://www.scienpress.com/Upload/JRC%2fVol%208_1_1.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spt:rmkjrc:v:8:y:2021:i:1:f:8_1_1. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Eleftherios Spyromitros-Xioufis (email available below). General contact details of provider: http://www.scienpress.com/ .

    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.