IDEAS home Printed from https://ideas.repec.org/a/spr/jstada/v4y2017i1d10.1186_s40488-017-0066-3.html
   My bibliography  Save this article

Alternative approaches for econometric modeling of panel data using mixture distributions

Author

Listed:
  • Judex Hyppolite

    (Monmouth University)

Abstract

The economic researcher is sometimes confronted with panel datasets that come from a population made of a finite number of subpopulations. Within each subpopulation the individuals may also be heterogenous according to some unobserved characteristics. A good understanding of the behavior of the observed individuals may then require the ability to identify the groups to which they belong and to study their behavior across groups and within groups. This may not be a complicated exercise when a group indicator variable is available in the dataset. However, such a variable may not be included in the dataset; and as a result, the econometrician is forced to work with the marginal distribution of the observed response variable, which takes the form of a mixture distribution. One can model a given response variable with a variety of mixture distributions. In this paper, I present several related mixture models. The most flexible one is an extension of the model by Kim et al. (2008) to the panel data setting. I have reviewed the estimation of some of these models by the Expectation-Maximization (EM) algorithm. The intent is to exploit the nice convergence properties of this algorithm when it is difficult to find good starting values for a Newton-type algorithm. I have also discussed how to compare these models and ultimately identify the one that provides the best fit to the data set under investigation. As an application I examine the investment behavior of U.S. manufacturing firms.

Suggested Citation

  • Judex Hyppolite, 2017. "Alternative approaches for econometric modeling of panel data using mixture distributions," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-34, December.
    • Handle: RePEc:spr:jstada:v:4:y:2017:i:1:d:10.1186_s40488-017-0066-3
    DOI: 10.1186/s40488-017-0066-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40488-017-0066-3
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1186/s40488-017-0066-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kiefer, Nicholas M, 1978. "Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model," Econometrica, Econometric Society, vol. 46(2), pages 427-434, March.
    2. Deb Partha & Trivedi Pravin K., 2013. "Finite Mixture for Panels with Fixed Effects," Journal of Econometric Methods, De Gruyter, vol. 2(1), pages 35-51, July.
    3. Jerome Adda & Russell W. Cooper, 2003. "Dynamic Economics: Quantitative Methods and Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262012014, April.
    4. Heitor Almeida & Murillo Campello, 2007. "Financial Constraints, Asset Tangibility, and Corporate Investment," The Review of Financial Studies, Society for Financial Studies, vol. 20(5), pages 1429-1460, 2007 12.
    5. Hovakimian, Gayane & Titman, Sheridan, 2006. "Corporate Investment with Financial Constraints: Sensitivity of Investment to Funds from Voluntary Asset Sales," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(2), pages 357-374, March.
    6. Asea, Patrick K. & Blomberg, Brock, 1998. "Lending cycles," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 89-128.
    7. Antonello Maruotti, 2014. "Latent Markov Models for longitudinal data," METRON, Springer;Sapienza Università di Roma, vol. 72(3), pages 367-368, October.
    8. Gonzalez, Jorge & Tuerlinckx, Francis & De Boeck, Paul & Cools, Ronald, 2006. "Numerical integration in logistic-normal models," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1535-1548, December.
    9. Judex Hyppolite & Pravin Trivedi, 2012. "Alternative Approaches For Econometric Analysis Of Panel Count Data Using Dynamic Latent Class Models (With Application To Doctor Visits Data)," Health Economics, John Wiley & Sons, Ltd., vol. 21(S1), pages 101-128, June.
    10. Hayashi, Fumio, 1982. "Tobin's Marginal q and Average q: A Neoclassical Interpretation," Econometrica, Econometric Society, vol. 50(1), pages 213-224, January.
    11. Xiaoqiang Hu & Fabio Schiantarelli, 1998. "Investment And Capital Market Imperfections: A Switching Regression Approach Using U.S. Firm Panel Data," The Review of Economics and Statistics, MIT Press, vol. 80(3), pages 466-479, August.
    12. Judex Hyppolite & Pravin Trivedi, 2012. "Alternative Approaches For Econometric Analysis Of Panel Count Data Using Dynamic Latent Class Models (With Application To Doctor Visits Data)," Health Economics, John Wiley & Sons, Ltd., vol. 21(S1), pages 101-128, June.
    13. Kim, Chang-Jin & Piger, Jeremy & Startz, Richard, 2008. "Estimation of Markov regime-switching regression models with endogenous switching," Journal of Econometrics, Elsevier, vol. 143(2), pages 263-273, April.
    14. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    15. G. Kapetanios, 2008. "A bootstrap procedure for panel data sets with many cross-sectional units," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 377-395, July.
    16. Hamilton, James D., 1988. "Rational-expectations econometric analysis of changes in regime : An investigation of the term structure of interest rates," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 385-423.
    17. Choi, K. C. & Zhou, X., 2002. "Large Sample Properties of Mixture Models with Covariates for Competing Risks," Journal of Multivariate Analysis, Elsevier, vol. 82(2), pages 331-366, August.
    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. Manzur Quader & Karl Taylor, 2018. "Corporate efficiency, credit status and investment," The European Journal of Finance, Taylor & Francis Journals, vol. 24(6), pages 439-457, April.
    2. Lawrenz, Jochen & Oberndorfer, Julia, 2018. "Firm size effects in trade credit supply and demand," Journal of Banking & Finance, Elsevier, vol. 93(C), pages 1-20.
    3. Ms. Marialuz Moreno Badia & Veerle Slootmaekers, 2009. "The Missing Link Between Financial Constraints and Productivity," IMF Working Papers 2009/072, International Monetary Fund.
    4. González, Andrés & Teräsvirta, Timo & van Dijk, Dick & Yang, Yukai, 2005. "Panel Smooth Transition Regression Models," SSE/EFI Working Paper Series in Economics and Finance 604, Stockholm School of Economics, revised 11 Oct 2017.
    5. Agnello Luca & Castro Vitor & Dufrénot Gilles & Jawadi Fredj & Sousa Ricardo M., 2020. "Unconventional monetary policy reaction functions: evidence from the US," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(4), pages 1-18, September.
    6. Agnello, Luca & Dufrénot, Gilles & Sousa, Ricardo M., 2013. "Using time-varying transition probabilities in Markov switching processes to adjust US fiscal policy for asset prices," Economic Modelling, Elsevier, vol. 34(C), pages 25-36.
    7. Aissata Boubacar Moumouni, 2020. "Investment Sensitivity to Inter-enterprises Payment Deadlines," AMSE Working Papers 1938, Aix-Marseille School of Economics, France.
    8. Anastasiya Shamshur, 2010. "Access to Capital and Capital Structure of the Firm," CERGE-EI Working Papers wp429, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    9. Yi Huang & Marco Pagano & Ugo Panizza, 2020. "Local Crowding‐Out in China," Journal of Finance, American Finance Association, vol. 75(6), pages 2855-2898, December.
    10. Chung-Hua Shen & Chih-Yung Lin, 2016. "Political connections, financial constraints, and corporate investment," Review of Quantitative Finance and Accounting, Springer, vol. 47(2), pages 343-368, August.
    11. Murat K. Munkin, 2022. "Count Roy model with finite mixtures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1160-1181, September.
    12. Benard Kipyegon Kirui & Nelson H.W. Wawire, 2018. "Measures of Financial Constraints in Kenya," International Journal of Economics and Financial Issues, Econjournals, vol. 8(1), pages 217-231.
    13. repec:lic:licosd:20808 is not listed on IDEAS
    14. Aissata Boubacar Moumouni, 2020. "Investment Sensitivity to Inter-enterprises Payment Deadlines," Working Papers hal-02889436, HAL.
    15. Li Donni, Paolo & Marino, Maria & Welzel, Christian, 2021. "How important is culture to understand political protest?," World Development, Elsevier, vol. 148(C).
    16. Hönig, Anja, 2012. "Financing Constraints Revisited - Is there a Role for Taxation and Internal Funds?," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 66053, Verein für Socialpolitik / German Economic Association.
    17. Gautam, Vikash, 2011. "Evidence on the dynamics of investment-cash flow sensitivity," MPRA Paper 35431, University Library of Munich, Germany, revised Dec 2011.
    18. Luca Agnello & Gilles Dufrénot & Ricardo M. Sousa, 2012. "Adjusting the U.S. Fiscal Policy for Asset Prices: Evidence from a TVP-MS Framework," NIPE Working Papers 20/2012, NIPE - Universidade do Minho.
    19. Vikash Gautam & Vikash Vaibhav, 2017. "Investment, Uncertainty and Credit Market Imperfection in India," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 15(2), pages 265-289, June.
    20. Chen, Yan-Shing & Chen, I-Ju, 2013. "The impact of labor unions on investment-cash flow sensitivity," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2408-2418.
    21. Sylvain Catherine & Thomas Chaney & Zongbo Huang & David Sraer & David Thesmar, 2022. "Quantifying Reduced‐Form Evidence on Collateral Constraints," Journal of Finance, American Finance Association, vol. 77(4), pages 2143-2181, August.

    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:spr:jstada:v:4:y:2017:i:1:d:10.1186_s40488-017-0066-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.