IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/93147.html
   My bibliography  Save this paper

Microeconometric Dynamic Panel Data Methods: Model Specification and Selection Issues

Author

Listed:
  • Kiviet, Jan

Abstract

A motivated strategy is presented to find step by step an adequate model specification and a matching set of instrumental variables by applying the programming tools provided by the Stata package Xtabond2. The aim is to implement generalized method of moment techniques such that useful and reasonably accurate inferences are extracted from an observational panel data set on a single microeconometric structural presumably dynamic behavioral relationship. In the suggested specification search three comprehensive heavily interconnected goals are pursued, namely: (i) to include all the relevant appropriately transformed possibly lagged regressors, as well as any interactions between these if it is required to relax the otherwise very strict homogeneity restrictions on the dynamic impacts of the explanatories in standard linear panel data models; (ii) to correctly classify all regressors as either endogenous, predetermined or exogenous, as well as being either effect-stationary or effect-nonstationary, implying which internal variables could represent valid and relatively strong instruments; (iii) to enhance the accuracy of inference in finite samples by omitting irrelevant regressors and by profitably reducing the space spanned by the full set of available internal instruments. For the various tests which trigger the decisions to be made in the sequential selection process the relevant considerations are spelled out to interpret the magnitude of p-values. Also the complexities to establish and interpret the ultimately established dynamic impacts are explained. Finally the developed strategy is applied to a classic data set and is shown to yield new insights.

Suggested Citation

  • Kiviet, Jan, 2019. "Microeconometric Dynamic Panel Data Methods: Model Specification and Selection Issues," MPRA Paper 93147, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:93147
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/93147/1/MPRA_paper_93147.pdf
    File Function: original version
    Download Restriction: no

    File URL: https://mpra.ub.uni-muenchen.de/95159/1/MPRA_paper_93147.pdf
    File Function: revised version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Bun, Maurice J.G. & Kiviet, Jan F., 2006. "The effects of dynamic feedbacks on LS and MM estimator accuracy in panel data models," Journal of Econometrics, Elsevier, vol. 132(2), pages 409-444, June.
    2. Kiviet Jan F., 2017. "Discriminating between (in)valid External Instruments and (in)valid Exclusion Restrictions," Journal of Econometric Methods, De Gruyter, vol. 6(1), pages 1-9, January.
    3. David Roodman, 2009. "A Note on the Theme of Too Many Instruments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(1), pages 135-158, February.
    4. Stephen Bond, 2002. "Dynamic panel data models: a guide to microdata methods and practice," CeMMAP working papers CWP09/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LP, vol. 9(1), pages 86-136, March.
    6. Andrew C. Harvey, 1990. "The Econometric Analysis of Time Series, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026208189x, December.
    7. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164.
    8. Aris Spanos, 2018. "Mis†Specification Testing In Retrospect," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 541-577, April.
    9. Ronald L. Wasserstein & Nicole A. Lazar, 2016. "The ASA's Statement on p -Values: Context, Process, and Purpose," The American Statistician, Taylor & Francis Journals, vol. 70(2), pages 129-133, May.
    10. Jan Kiviet & Milan Pleus & Rutger Poldermans, 2017. "Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models," Econometrics, MDPI, vol. 5(1), pages 1-54, March.
    11. Andrews, Donald W. K. & Lu, Biao, 2001. "Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models," Journal of Econometrics, Elsevier, vol. 101(1), pages 123-164, March.
    12. Frank Windmeijer, 2018. "Testing Over- and Underidentification in Linear Models, with Applications to Dynamic Panel Data and Asset-Pricing Models," Bristol Economics Discussion Papers 18/696, School of Economics, University of Bristol, UK.
    13. Stephen R. Bond, 2002. "Dynamic panel data models: a guide to micro data methods and practice," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 1(2), pages 141-162, August.
    14. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    15. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
    16. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ardiyono, Sulistiyo K., 2022. "Covid-19 pandemic, firms’ responses, and unemployment in the ASEAN-5," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 337-372.
    2. Sebastian Kripfganz, 2019. "Generalized method of moments estimation of linear dynamic panel-data models," London Stata Conference 2019 17, Stata Users Group.
    3. Gürtler, Oliver & Struth, Lennart & Thon, Max, 2023. "Competition and risk-taking," European Economic Review, Elsevier, vol. 160(C).
    4. Ty Kreitman & Todd Kuethe & David B. Oppedahl & Francisco Scott, 2022. "The Supply and Demand of Agricultural Loans," Research Working Paper RWP 22-06, Federal Reserve Bank of Kansas City.
    5. Callado-Muñoz, Francisco J. & Hromcová, Jana & Utrero-González, Natalia, 2023. "Can buying weapons from your friends make you better off? Evidence from NATO," Economic Modelling, Elsevier, vol. 118(C).
    6. Kahsay Gerezihar Tsaedu & Zhihong Chen, 2021. "The Dynamics of Firm Growth in Sub-Saharan Africa: Evidence from Ethiopian Manufacturing Sector 1996–2017," Journal of Industry, Competition and Trade, Springer, vol. 21(3), pages 367-392, September.
    7. Sulistiyo K. Ardiyono & Arianto A. Patunru, 2022. "The impact of employment protection on FDI at different stages of economic development," The World Economy, Wiley Blackwell, vol. 45(12), pages 3679-3714, December.
    8. Agapova, Anna & Vishwasrao, Sharmila, 2020. "Financial sector foreign aid and financial intermediation," International Review of Financial Analysis, Elsevier, vol. 72(C).
    9. Philip Kerner & Torben Klarl & Tobias Wendler, 2021. "Green Technologies, Environmental Policy and Regional Growth," Bremen Papers on Economics & Innovation 2104, University of Bremen, Faculty of Business Studies and Economics.
    10. Piccoli, Luca & Tiezzi, Silvia, 2023. "Eggs When Young, Chicken When Old. Time Consistency and Addiction over the Life Cycle," IZA Discussion Papers 16372, Institute of Labor Economics (IZA).
    11. Hak Yeung & Jürgen Huber, 2022. "Further Evidence on China’s B&R Impact on Host Countries’ Quality of Institutions," Sustainability, MDPI, vol. 14(9), pages 1-17, May.
    12. Ty Kreitman & Todd Kuethe & David B. Oppedahl & Francisco Scott, 2022. "The Supply and Demand of Agricultural Loans," Research Working Paper RWP 22-06, Federal Reserve Bank of Kansas City.
    13. Gopane, Thabo J. & Gandanhamo, Tanyaradzwa & Mabejane, John-Baptiste, 2023. "Technology firms and capital structure adjustment: Application of two-step system generalised method of moments," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 34-54.
    14. Maria Elena Bontempi & Jan Ditzen, 2023. "GMM-lev estimation and individual heterogeneity: Monte Carlo evidence and empirical applications," Papers 2312.00399, arXiv.org, revised Dec 2023.
    15. Jan-Niklas Meier & Paul Lehmann & Bernd Süssmuth & Stephan Wedekind, 2024. "Wind power deployment and the impact of spatial planning policies," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(2), pages 491-550, February.
    16. Bruno Merlevede & Angelos Theodorakopoulos, 2021. "Productivity effects of internationalisation through the domestic supply chain," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 808-832, September.

    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. Pinkston, Joshua C., 2017. "The dynamic effects of obesity on the wages of young workers," Economics & Human Biology, Elsevier, vol. 27(PA), pages 154-166.
    2. Hak Yeung & Jürgen Huber, 2022. "Further Evidence on China’s B&R Impact on Host Countries’ Quality of Institutions," Sustainability, MDPI, vol. 14(9), pages 1-17, May.
    3. Linh T.D. Huynh & Hien Thanh Hoang, 2019. "Effects of exchange rate volatility on bilateral import performance of Vietnam: A dynamic Generalised method of Moments panel approach," International Economic Journal, Taylor & Francis Journals, vol. 33(1), pages 88-110, January.
    4. Jooste, Charl & Liu, Guangling (Dave) & Naraidoo, Ruthira, 2013. "Analysing the effects of fiscal policy shocks in the South African economy," Economic Modelling, Elsevier, vol. 32(C), pages 215-224.
    5. Heath Henderson & Leonardo Corral & Eric Simning & Paul Winters, 2015. "Land Accumulation Dynamics in Developing Country Agriculture," Journal of Development Studies, Taylor & Francis Journals, vol. 51(6), pages 743-761, June.
    6. Mateo Zokalj, 2016. "The impact of population aging on public finance in the European Union," Financial Theory and Practice, Institute of Public Finance, vol. 40(4), pages 383-412.
    7. Juan Federico & Joan-Lluis Capelleras, 2015. "The heterogeneous dynamics between growth and profits: the case of young firms," Small Business Economics, Springer, vol. 44(2), pages 231-253, February.
    8. Armey, Laura E. & McNab, Robert M., 2018. "Expenditure decentralization and natural resources," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 52-61.
    9. Tsun Se Cheong & Yanrui Wu, 2013. "Globalization and Regional Inequality," Economics Discussion / Working Papers 13-10, The University of Western Australia, Department of Economics.
    10. Scott, K. Rebecca, 2011. "Demand and Price Volatility: Rational Habits in International Gasoline Demand," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt2q87432b, Department of Agricultural & Resource Economics, UC Berkeley.
    11. Lenarčič, Črt & Masten, Igor, 2020. "Is there a Harrod-Balassa-Samuelson effect? New panel data evidence from 28 European countries," MPRA Paper 100647, University Library of Munich, Germany.
    12. Alan Piper, 2018. "Adult life satisfaction largely (though not wholly) contemporaneous," Discussion Papers 028, Europa-Universität Flensburg, International Institute of Management.
    13. Roberto Golinelli & Sandro Momigliano, 2009. "The Cyclical Reaction of Fiscal Policies in the Euro Area: The Role of Modelling Choices and Data Vintages," Fiscal Studies, Institute for Fiscal Studies, vol. 30(1), pages 39-72, March.
    14. Tabak, Benjamin M. & Fazio, Dimas M. & Cajueiro, Daniel O., 2011. "The effects of loan portfolio concentration on Brazilian banks' return and risk," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 3065-3076, November.
    15. Castro, Vítor & Martins, Rodrigo, 2021. "Government ideology and economic freedom," Journal of Comparative Economics, Elsevier, vol. 49(1), pages 73-91.
    16. Caporale, Guglielmo Maria & Sova, Anamaria Diana & Sova, Robert, 2022. "The direct and indirect effects of financial development on international trade: Evidence from the CEEC-6," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
    17. Roberto Dell'Anno & Adalgiso Amendola, 2015. "Social Exclusion and Economic Growth: An Empirical Investigation in European Economies," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(2), pages 274-301, June.
    18. Walther, Herbert & Stiassny, Alfred, 2013. "International Comparisons of Household Saving Rates and Hidden Income," Department of Economics Working Paper Series 148, WU Vienna University of Economics and Business.
    19. Sulistiyo K. Ardiyono & Arianto A. Patunru, 2022. "The impact of employment protection on FDI at different stages of economic development," The World Economy, Wiley Blackwell, vol. 45(12), pages 3679-3714, December.
    20. Youssef, Ahmed & Abonazel, Mohamed R., 2015. "Alternative GMM Estimators for First-order Autoregressive Panel Model: An Improving Efficiency Approach," MPRA Paper 68674, University Library of Munich, Germany.

    More about this item

    Keywords

    classification of explanatories; dynamic impacts; interactions; feedback mechanisms; generalized method of moments; labor demand; model building strategy; short panels.;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand

    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:pra:mprapa:93147. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.