IDEAS home Printed from https://ideas.repec.org/a/rjr/romjef/vy2015i2p5-29.html
   My bibliography  Save this article

Net Indirect Taxes and Sectoral Structure of Economy

Author

Listed:
  • Emilian Dobrescu

    (Centre for Macroeconomic Modelling, NIER, Romanian Academy.)

Abstract

Usually, the sectoral structure of economy is measured as weights of the main branches - a) in the total gross value added or b) in the gross domestic product. Vector b differs from vector a by the sectoral net indirect taxes, as shown in the Input-Output Tables of Romania. This issue has been explored using the Input-Output Tables of Romania for almost a quarter of a century. The primary information that resulted from the extended branch nomenclatures (from 90 to 105 positions in different years) has been aggregated into ten sectors. The series were methodologically homogenized according to the last Eurostat classification The comparative analysis involved five structural coefficients derived from the Euclidean 1-norm distance, Bhattacharyya coefficient, Hellinger distance, Cosine similarity coefficient, and the so-called Jaccard index. Some computational problems of estimating - as autoregressive processes - the sectoral rates of the net indirect taxes are also examined.

Suggested Citation

  • Emilian Dobrescu, 2015. "Net Indirect Taxes and Sectoral Structure of Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 5-29, June.
    • Handle: RePEc:rjr:romjef:v::y:2015:i:2:p:5-29
    as

    Download full text from publisher

    File URL: http://www.ipe.ro/rjef/rjef2_15/rjef2_2015p5-29.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dobrescu, Emilian, 2013. "Modelling the Sectoral Structure of the Final Output," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 59-89, October.
    2. Hafer, R. W. & Sheehan, Richard G., 1989. "The sensitivity of VAR forecasts to alternative lag structures," International Journal of Forecasting, Elsevier, vol. 5(3), pages 399-408.
    3. Dobrescu, Emilian, 2011. "Sectoral Structure and Economic Growth," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 5-36, September.
    4. Emilian Dobrescu, 2013. "Updating the Romanian Economic Macromodel," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-31, December.
    5. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258, November.
    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. Emilian Dobrescu, 2019. "A Note on Estimating the Importance of Interactions among Economic Entities," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 21(52), pages 671-671, August.

    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. Jeffrey S. Racine & Qi Li & Dalei Yu & Li Zheng, 2023. "Optimal Model Averaging of Mixed-Data Kernel-Weighted Spline Regressions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1251-1261, October.
    2. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    3. Davide Fiaschi & Andrea Mario Lavezzi & Angela Parenti, 2020. "Deep and Proximate Determinants of the World Income Distribution," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(3), pages 677-710, September.
    4. Zhongqi Liang & Qihua Wang & Yuting Wei, 2022. "Robust model selection with covariables missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(3), pages 539-557, June.
    5. Kumar, V. & Leone, Robert P. & Gaskins, John N., 1995. "Aggregate and disaggregate sector forecasting using consumer confidence measures," International Journal of Forecasting, Elsevier, vol. 11(3), pages 361-377, September.
    6. Bhattacharya, Debopam & Dupas, Pascaline, 2012. "Inferring welfare maximizing treatment assignment under budget constraints," Journal of Econometrics, Elsevier, vol. 167(1), pages 168-196.
    7. Zeinab Partow, 1995. "Una Investigación Empírica sobre el Impacto de la Inflación en el Crecimiento Económico de Colombia 1951-1992," Borradores de Economia 017, Banco de la Republica de Colombia.
    8. Schomaker Michael & Heumann Christian, 2011. "Model Averaging in Factor Analysis: An Analysis of Olympic Decathlon Data," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(1), pages 1-15, January.
    9. Omer Ali Ibrahim & Sonal Devesh & Hisham Mohamed Hassan, 2019. "Sensitivity of Fiscal Balances to Oil Price Shocks: Short and Long Term Effects in the Context of Oman," International Journal of Energy Economics and Policy, Econjournals, vol. 9(2), pages 146-155.
    10. Tumala, Mohammed M & Olubusoye, Olusanya E & Yaaba, Baba N & Yaya, OlaOluwa S & Akanbi, Olawale B, 2017. "Forecasting Nigerian Inflation using Model Averaging methods: Modelling Frameworks to Central Banks," MPRA Paper 88754, University Library of Munich, Germany, revised Feb 2018.
    11. José Manuel Cordero Ferrera & Manuel Muñiz Pérez & Rosa Simancas Rodríguez, 2015. "The influence of socioeconomic factors on cognitive and non-cognitive educational outcomes," Investigaciones de Economía de la Educación volume 10, in: Marta Rahona López & Jennifer Graves (ed.), Investigaciones de Economía de la Educación 10, edition 1, volume 10, chapter 21, pages 413-438, Asociación de Economía de la Educación.
    12. Naoya Sueishi & Arihiro Yoshimura, 2017. "Focused Information Criterion for Series Estimation in Partially Linear Models," The Japanese Economic Review, Japanese Economic Association, vol. 68(3), pages 352-363, September.
    13. Liao, Jun & Zou, Guohua, 2020. "Corrected Mallows criterion for model averaging," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    14. Miao Han & Liuquan Sun & Yutao Liu & Jun Zhu, 2018. "Joint analysis of recurrent event data with additive–multiplicative hazards model for the terminal event time," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(5), pages 523-547, July.
    15. Gildas Mazo & François Portier, 2021. "Parametric versus nonparametric: The fitness coefficient," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1344-1383, December.
    16. Anwen Yin, 2024. "Predictive model averaging with parameter instability and heteroskedasticity," Bulletin of Economic Research, Wiley Blackwell, vol. 76(2), pages 418-442, April.
    17. Markus Eller & Jarmila Urvová, 2012. "How Sustainable Are Public Debt Levels in Emerging Europe?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 4, pages 48-79.
    18. Gerda Claeskens & Fabrizio Consentino, 2008. "Variable Selection with Incomplete Covariate Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1062-1069, December.
    19. Cornelius Fritz & Michael Lebacher & Göran Kauermann, 2020. "Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 275-299, August.
    20. Edvard Bakhitov, 2020. "Frequentist Shrinkage under Inequality Constraints," Papers 2001.10586, arXiv.org.

    More about this item

    Keywords

    sectoral structure; net indirect taxes; VAR;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • H2 - Public Economics - - Taxation, Subsidies, and Revenue

    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:rjr:romjef:v::y:2015:i:2:p:5-29. 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: Corina Saman (email available below). General contact details of provider: https://edirc.repec.org/data/ipacaro.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.