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Modelling the Hidden Economy and the Tax-Gap in New Zealand

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Abstract

This paper develops and estimates a structural, latent variable, model for the hidden economy in New Zealand, and a separate currency-demand model. The estimated latent variable model is used to generate an historical time-series index of hidden economic activity, which is calibrated via the information from the currency-demand model. Special attention is paid to data non-stationarity, and diagnostic testing. The size of the hidden economy is found to vary between 6.8% and 11.3% of measured GDP. This, in turn, implies that the total tax-gap is of the order of 6.4% to 10.2% of total tax liability in that country, though of course not all of this foregone revenue would be recoverable.

Suggested Citation

  • David E. A. Giles, 1999. "Modelling the Hidden Economy and the Tax-Gap in New Zealand," Econometrics Working Papers 9905, Department of Economics, University of Victoria.
  • Handle: RePEc:vic:vicewp:9905 Note: ISSN 1485-6441. Earlier versions of this paper was released as University of Victoria Department of Economics Discussion Paper 97-8, April 1997, and UVic EWP9807, EWP9810.
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    1. Swanson Norman, 1996. "Forecasting Using First-Available Versus Fully Revised Economic Time-Series Data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 1(1), pages 1-20, April.
    2. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, November.
    3. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, pages 111-130.
    4. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, pages 605-617.
    5. Sowell, Fallaw, 1992. "Modeling long-run behavior with the fractional ARIMA model," Journal of Monetary Economics, Elsevier, vol. 29(2), pages 277-302, April.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
    8. Mauro Gallegati & Domenico Mignacca, 1995. "Nonlinearities in business cycle: SETAR models and G7 industrial production data," Applied Economics Letters, Taylor & Francis Journals, vol. 2(11), pages 422-427.
    9. Christiano, Lawrence J & Eichenbaum, Martin, 1992. "Current Real-Business-Cycle Theories and Aggregate Labor-Market Fluctuations," American Economic Review, American Economic Association, pages 430-450.
    10. Bruce E. Hansen, 2000. "Sample Splitting and Threshold Estimation," Econometrica, Econometric Society, vol. 68(3), pages 575-604, May.
    11. Hansen Bruce E., 1997. "Inference in TAR Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(1), pages 1-16, April.
    12. Dean Croushore & Tom Stark, 1999. "Does data vintage matter for forecasting?," Working Papers 99-15, Federal Reserve Bank of Philadelphia.
    13. Trivellato, Ugo & Rettore, Enrico, 1986. "Preliminary Data Errors and Their Impact on the Forecast Error of Simultaneous-Equations Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 445-453, October.
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    More about this item

    Keywords

    Hidden Economy; Underground Economy; Tax Avoidance; Tax Evasion; Tax-Gap;

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • H1 - Public Economics - - Structure and Scope of Government
    • H2 - Public Economics - - Taxation, Subsidies, and Revenue

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