IDEAS home Printed from https://ideas.repec.org/a/eee/ecosta/v13y2020icp16-45.html
   My bibliography  Save this article

Microeconometric dynamic panel data methods: Model specification and selection issues

Author

Listed:
  • Kiviet, Jan F.

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 can be 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: (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 redundant 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 estimate 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 F., 2020. "Microeconometric dynamic panel data methods: Model specification and selection issues," Econometrics and Statistics, Elsevier, vol. 13(C), pages 16-45.
  • Handle: RePEc:eee:ecosta:v:13:y:2020:i:c:p:16-45
    DOI: 10.1016/j.ecosta.2019.08.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2452306219300498
    Download Restriction: Full text for ScienceDirect subscribers only. Contains open access articles

    File URL: https://libkey.io/10.1016/j.ecosta.2019.08.003?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
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164.
    9. Aris Spanos, 2018. "Mis†Specification Testing In Retrospect," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 541-577, April.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    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. Agapova, Anna & Vishwasrao, Sharmila, 2020. "Financial sector foreign aid and financial intermediation," International Review of Financial Analysis, Elsevier, vol. 72(C).
    3. Sebastian Kripfganz, 2019. "Generalized method of moments estimation of linear dynamic panel-data models," London Stata Conference 2019 17, Stata Users Group.
    4. 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.
    5. 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).
    6. Oliver Gürtler & Lennart Struth & Max Thon, 2022. "Competition and Risk-Taking," ECONtribute Discussion Papers Series 181, University of Bonn and University of Cologne, Germany.
    7. 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.
    8. 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.
    9. 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.
    10. 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).
    11. 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.
    12. 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.
    13. 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.
    14. 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. Piccoli, Luca & Tiezzi, Silvia, 2021. "Rational addiction and time-consistency: An empirical test," Journal of Health Economics, Elsevier, vol. 80(C).
    3. Fendel Tanja, 2016. "Migration and Regional Wage Disparities in Germany," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(1), pages 3-35, February.
    4. Scott, K. Rebecca, 2015. "Demand and price uncertainty: Rational habits in international gasoline demand," Energy, Elsevier, vol. 79(C), pages 40-49.
    5. Heid, Benedikt & Langer, Julian & Larch, Mario, 2012. "Income and democracy: Evidence from system GMM estimates," Economics Letters, Elsevier, vol. 116(2), pages 166-169.
    6. Magrini, Emiliano & Morales-Opazo, Cristian & Balie, Jean, 2014. "Supply response along the value chain in selected SSA countries: the case of grains," 2014: Food, Resources and Conflict, December 7-9, 2014. San Diego, California 197193, International Agricultural Trade Research Consortium.
    7. 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.
    8. 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.
    9. 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.
    10. Marques Pereira, João André C. & Saito, Richard, 2015. "How banks respond to Central Bank supervision: Evidence from Brazil," Journal of Financial Stability, Elsevier, vol. 19(C), pages 22-30.
    11. Peñasco, Cristina & del Río, Pablo & Romero-Jordán, Desiderio, 2017. "Gas and electricity demand in Spanish manufacturing industries: An analysis using homogeneous and heterogeneous estimators," Utilities Policy, Elsevier, vol. 45(C), pages 45-60.
    12. 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.
    13. 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.
    14. Cavallo, Alberto F. & Cavallo, Eduardo A., 2010. "Are crises good for long-term growth? The role of political institutions," Journal of Macroeconomics, Elsevier, vol. 32(3), pages 838-857, September.
    15. 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.
    16. 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.
    17. Tsun Se Cheong & Yanrui Wu, 2013. "Globalization and Regional Inequality," Economics Discussion / Working Papers 13-10, The University of Western Australia, Department of Economics.
    18. 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.
    19. Ünal Töngür & Adem Yavuz Elveren, 2017. "The nexus of economic growth, military expenditures, and income inequality," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(4), pages 1821-1842, July.
    20. Charles Mawusi, 2021. "Economic Uncertainty and Remittances to Developing Economies: A System GMM Approach," Working Papers hal-03147813, HAL.

    More about this item

    Keywords

    Classification of regressors; Dynamic impacts; Interaction effects; Generalized method of moments; Labor demand; Panel data model building strategy;
    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

    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:eee:ecosta:v:13:y:2020:i:c:p:16-45. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/econometrics-and-statistics .

    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.