IDEAS home Printed from https://ideas.repec.org/p/hhs/umnees/0472.html
   My bibliography  Save this paper

Forecasting based on Very Small Samples and Additional Non-Sample Information

Author

Listed:
  • Brännäs, Kurt

    (Department of Economics, Umeå University)

  • Hellström, Jörgen

    (Department of Economics, Umeå University)

Abstract

Generalized method of moments estimation and forecasting is introduced for very small samples when additional non-sample information is available. Small simulation experiments are conducted for the linear model with errors-in-variables and for a Poisson regression model. Two empirical illustrations are included. One is based on Ukrainian imports and the other on private schools in a Swedish county.

Suggested Citation

  • Brännäs, Kurt & Hellström, Jörgen, 1998. "Forecasting based on Very Small Samples and Additional Non-Sample Information," Umeå Economic Studies 472, Umeå University, Department of Economics.
  • Handle: RePEc:hhs:umnees:0472
    as

    Download full text from publisher

    File URL: http://www.econ.umu.se/DownloadAsset.action?contentId=127148&languageId=3&assetKey=ues472
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Thomas A. Downes & Shane M. Greenstein, 1996. "Understanding the Supply Decisions of Nonprofits: Modelling the Location of Private Schools," RAND Journal of Economics, The RAND Corporation, vol. 27(2), pages 365-390, Summer.
    3. Gourieroux,Christian & Monfort,Alain, 1995. "Statistics and Econometric Models," Cambridge Books, Cambridge University Press, number 9780521405515.
    4. Kadane, Joseph B, 1971. "Comparison of k-Class Estimators when the Disturbances are Small," Econometrica, Econometric Society, vol. 39(5), pages 723-737, September.
    5. Arthur Lewbel, 1997. "Constructing Instruments for Regressions with Measurement Error when no Additional Data are Available, with an Application to Patents and R&D," Econometrica, Econometric Society, vol. 65(5), pages 1201-1214, September.
    6. Thursby, Jerry G & Thursby, Marie C, 1984. "How Reliable Are Simple, Single Equation Specifications of Import Demand?," The Review of Economics and Statistics, MIT Press, vol. 66(1), pages 120-128, February.
    7. Caroline Minter Hoxby, 1994. "Do Private Schools Provide Competition for Public Schools?," NBER Working Papers 4978, National Bureau of Economic Research, Inc.
    8. Boylan, T. A. & Cuddy, M. P. & O'Muircheartaigh, I., 1980. "The functional form of the aggregate import demand equation : A comparison of three European economies," Journal of International Economics, Elsevier, vol. 10(4), pages 561-566, November.
    9. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-720, May.
    10. Brannas, Kurt, 1995. "Prediction and control for a time-series count data model," International Journal of Forecasting, Elsevier, vol. 11(2), pages 263-270, June.
    11. Kennedy, Peter, 1991. "An Extension of Mixed Estimation, with an Application to Forecasting New Product Growth," Empirical Economics, Springer, vol. 16(4), pages 401-415.
    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. Nikas Rudholm, 2001. "Entry and the Number of Firms in the Swedish Pharmaceuticals Market," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 19(3), pages 351-364, November.
    2. Hellstrom, Jorgen, 2001. "Unit root testing in integer-valued AR(1) models," Economics Letters, Elsevier, vol. 70(1), pages 9-14, January.

    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. Gouriéroux, Christian & Monfort, Alain & Zakoian, Jean-Michel, 2017. "Pseudo-Maximum Likelihood and Lie Groups of Linear Transformations," MPRA Paper 79623, University Library of Munich, Germany.
    2. Andrei Zeleneev & Kirill Evdokimov, 2023. "Simple estimation of semiparametric models with measurement errors," CeMMAP working papers 10/23, Institute for Fiscal Studies.
    3. Kirill S. Evdokimov & Andrei Zeleneev, 2023. "Simple Estimation of Semiparametric Models with Measurement Errors," Papers 2306.14311, arXiv.org, revised Mar 2024.
    4. Stefan Boes, 2004. "Empirical Likelihood in Count Data Models: The Case of Endogenous Regressors," SOI - Working Papers 0404, Socioeconomic Institute - University of Zurich.
    5. Arthur Lewbel, 2012. "Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 67-80.
    6. Peñaranda, Francisco & Sentana, Enrique, 2012. "Spanning tests in return and stochastic discount factor mean–variance frontiers: A unifying approach," Journal of Econometrics, Elsevier, vol. 170(2), pages 303-324.
    7. Hajivassiliou, Vassilis A. & Ruud, Paul A., 1986. "Classical estimation methods for LDV models using simulation," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 40, pages 2383-2441, Elsevier.
    8. Inkmann, Joachim, 1997. "Circumventing multiple integration: A comparison of GMM and SML estimators for the panel probit model," Discussion Papers, Series II 339, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    9. Driessen, Joost & Melenberg, Bertrand & Nijman, Theo, 2005. "Testing affine term structure models in case of transaction costs," Journal of Econometrics, Elsevier, vol. 126(1), pages 201-232, May.
    10. David Welsch & David Zimmer, 2010. "The Effect of Health and Poverty on Early Childhood Cognitive Development," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 38(1), pages 37-49, March.
    11. C. Gouriéroux & A. Monfort & J.‐M. Zakoïan, 2019. "Consistent Pseudo‐Maximum Likelihood Estimators and Groups of Transformations," Econometrica, Econometric Society, vol. 87(1), pages 327-345, January.
    12. Christopher F Baum & Arthur Lewbel, 2019. "Advice on using heteroskedasticity-based identification," Stata Journal, StataCorp LP, vol. 19(4), pages 757-767, December.
    13. Blundell, Richard & Griffith, Rachel & Windmeijer, Frank, 2002. "Individual effects and dynamics in count data models," Journal of Econometrics, Elsevier, vol. 108(1), pages 113-131, May.
    14. Todd Prono, 2008. "GARCH-based identification and estimation of triangular systems," Supervisory Research and Analysis Working Papers QAU08-4, Federal Reserve Bank of Boston.
    15. Thomas Bassetti & Raul Caruso & Darwin Cortes, 2015. "Behavioral differences in violence: The case of intra-group differences of Paramilitaries and Guerrillas in Colombia," DISCE - Quaderni del Dipartimento di Politica Economica ispe0073, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    16. David T. Frazier & Eric Renault, 2016. "Indirect Inference With(Out) Constraints," Papers 1607.06163, arXiv.org, revised Aug 2019.
    17. Victor Aguirregabiria & Pedro Mira, 2002. "Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models," Econometrica, Econometric Society, vol. 70(4), pages 1519-1543, July.
    18. Kiviet, Jan F., 2020. "Testing the impossible: Identifying exclusion restrictions," Journal of Econometrics, Elsevier, vol. 218(2), pages 294-316.
    19. Stefan Boes, 2007. "Count Data Models with Unobserved Heterogeneity: An Empirical Likelihood Approach," SOI - Working Papers 0704, Socioeconomic Institute - University of Zurich.
    20. Xiaohong Chen & Yingyao Hu & Arthur Lewbel, 2007. "Nonparametric Identification and Estimation of Nonclassical Errors-in-Variables Models Without Additional Information," Boston College Working Papers in Economics 676, Boston College Department of Economics.

    More about this item

    Keywords

    Generalized method of moments; additional information; forecasting; Ukrainian imports; private schools;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation
    • I20 - Health, Education, and Welfare - - Education - - - General

    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:hhs:umnees:0472. 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: David Skog (email available below). General contact details of provider: https://edirc.repec.org/data/inumuse.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.