IDEAS home Printed from https://ideas.repec.org/a/rsr/supplm/v64y2016i11p136-144.html
   My bibliography  Save this article

General theoretical notions on univariate regression

Author

Listed:
  • Constantin ANGHELACHE

    (Academia de Studii Economice din Bucuresti/Universitatea „Artifex„ din Bucuresti)

  • Ion PARTACHI

    (Academia de Studii Economice a Moldovei, Chisinau)

  • Madalina-Gabriela ANGHEL

    (Universitatea „Artifex„ din Bucuresti)

  • Gyorgy BODO

    (Academia de Studii Economice din Bucuresti)

  • Radu STOIAN

    (Academia de Studii Economice din Bucuresti)

Abstract

In this article, the authors started from the fact that in general, the concept of conditional probability and the conditional linear probability in terms of orthogonal projections are common to the crowd of linear functions. Against this background, a presentation on the main conditionality involved in univariate regression was conducted. Thus, linearity, uncolinerity and conditional normality are presented and demonstrated. At the same time, homoscedasticitate conditioning is highlighed. Further, the presentation of conditions linearity and homoscedasticitate is based on the concept of error highlighting to be considered for univariate regression. Further, it points out that the estimation is performed by the method of least squares parameter which reduces to beta estimation. Another element considered and clarified concerns the replacement of the probability through the distribution probability sampling, that is subject to minimization criterion.

Suggested Citation

  • Constantin ANGHELACHE & Ion PARTACHI & Madalina-Gabriela ANGHEL & Gyorgy BODO & Radu STOIAN, 2016. "General theoretical notions on univariate regression," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(11), pages 136-144, November.
    • Handle: RePEc:rsr:supplm:v:64:y:2016:i:11:p:136-144
    as

    Download full text from publisher

    File URL: http://www.revistadestatistica.ro/supliment/wp-content/uploads/2016/12/rrss_11_2016_A07_en.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrew Ang & Geert Bekaert, 2007. "Stock Return Predictability: Is it There?," The Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 651-707.
    2. Elena Pesavento & Barbara Rossi, 2006. "Small‐sample confidence intervals for multivariate impulse response functions at long horizons," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1135-1155, December.
    3. Constantin ANGHELACHE & Alexandru MANOLE, 2016. "The use of Regression Model In analysing the correlation between the monetary situation and the balance of payments," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(7), pages 30-35, July.
    4. Constantin Anghelache & Cristina SACALÃ, 2016. "Multiple linear regression used to analyse the corelation between GDP and some variables," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(9), pages 94-99, September.
    5. Dan CRUCERU & Madalina Gabriela ANGHEL & Aurelian DIACONU, 2016. "The multiple linear regression used to analyse the correlation between variables," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(10), pages 114-117, October.
    6. Whitney K. Newey & James L. Powell, 2003. "Instrumental Variable Estimation of Nonparametric Models," Econometrica, Econometric Society, vol. 71(5), pages 1565-1578, September.
    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. Constantin Anghelache & Aurelian DIACONU & Andreea Ioana MARINESCU & Marius POPOVICI, 2016. "Comparative study of the evolution of the Gross Domestic Product indicator," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(12), pages 165-172, December.
    2. Ai, Chunrong & Chen, Xiaohong, 2007. "Estimation of possibly misspecified semiparametric conditional moment restriction models with different conditioning variables," Journal of Econometrics, Elsevier, vol. 141(1), pages 5-43, November.
    3. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    4. Xiaohong Chen & Andres Santos, 2018. "Overidentification in Regular Models," Econometrica, Econometric Society, vol. 86(5), pages 1771-1817, September.
    5. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    6. Hertrich Markus, 2019. "A Novel Housing Price Misalignment Indicator for Germany," German Economic Review, De Gruyter, vol. 20(4), pages 759-794, December.
    7. Zongwu Cai & Haiqiang Chen & Xiaosai Liao, 2020. "A New Robust Inference for Predictive Quantile Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202002, University of Kansas, Department of Economics, revised Feb 2020.
    8. Richard Blundell & Joel Horowitz & Matthias Parey, 2022. "Estimation of a Heterogeneous Demand Function with Berkson Errors," The Review of Economics and Statistics, MIT Press, vol. 104(5), pages 877-889, December.
    9. Clark, Todd E. & McCracken, Michael W., 2012. "In-sample tests of predictive ability: A new approach," Journal of Econometrics, Elsevier, vol. 170(1), pages 1-14.
    10. Xiaohong Chen & Demian Pouzo, 2012. "Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals," Econometrica, Econometric Society, vol. 80(1), pages 277-321, January.
    11. Salisu, Afees A. & Adekunle, Wasiu & Alimi, Wasiu A. & Emmanuel, Zachariah, 2019. "Predicting exchange rate with commodity prices: New evidence from Westerlund and Narayan (2015) estimator with structural breaks and asymmetries," Resources Policy, Elsevier, vol. 62(C), pages 33-56.
    12. 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.
    13. Fernando M. Duarte & Carlo Rosa, 2015. "The equity risk premium: a review of models," Economic Policy Review, Federal Reserve Bank of New York, issue 2, pages 39-57.
    14. Bekaert, Geert & Engstrom, Eric & Grenadier, Steven R., 2010. "Stock and bond returns with Moody Investors," Journal of Empirical Finance, Elsevier, vol. 17(5), pages 867-894, December.
    15. Xiaohong Chen & Victor Chernozhukov & Sokbae Lee & Whitney K. Newey, 2014. "Local Identification of Nonparametric and Semiparametric Models," Econometrica, Econometric Society, vol. 82(2), pages 785-809, March.
    16. Shoya Ishimaru, 2024. "Empirical Decomposition of the IV-OLS Gap with Heterogeneous and Nonlinear Effects," The Review of Economics and Statistics, MIT Press, vol. 106(2), pages 505-520, March.
    17. Arellano, Manuel & Blundell, Richard & Bonhomme, Stéphane & Light, Jack, 2024. "Heterogeneity of consumption responses to income shocks in the presence of nonlinear persistence," Journal of Econometrics, Elsevier, vol. 240(2).
    18. Ishani Chaudhuri & Parthajit Kayal, 2022. "Predicting Power of Ticker Search Volume in Indian Stock Market," Working Papers 2022-214, Madras School of Economics,Chennai,India.
    19. Ravi Bansal, 2007. "Long-run risks and financial markets," Review, Federal Reserve Bank of St. Louis, vol. 89(Jul), pages 283-300.
    20. Doraszelski, Ulrich & Jaumandreu, Jordi, 2006. "R&D and productivity: Estimating production functions when productivity is endogenous," MPRA Paper 1246, University Library of Munich, Germany.

    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:rsr:supplm:v:64:y:2016:i:11:p:136-144. 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: Adrian Visoiu (email available below). General contact details of provider: https://edirc.repec.org/data/stagvro.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.