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Bootstrap Testing in Nonlinear Models

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Author Info
Russell Davidson
James G. MacKinnon

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Abstract

When a model is nonlinear, bootstrap testing can be expensive because of the need to perform at least one nonlinear estimation for every bootstrap sample. We show that it may be possible to reduce computational costs by performing only a fixed, small number of artificial regressions, or Newton steps, for each bootstrap sample. The number of iterations needed is smaller for likelihood ratio tests than for other types of classical tests. The suggested procedures are applied to tests of slope coefficients in the tobit model, where asymptotic procedures often work surprisingly poorly. In contrast, bootstrap tests work remarkably well, and very few iterations are needed to compute them.

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File URL: http://www.econ.queensu.ca/working_papers/papers/qed_wp_944.pdf
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File Function: First version 1997
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Publisher Info
Paper provided by Queen's University, Department of Economics in its series Working Papers with number 944.

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Date of creation: Feb 1997
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Publication status: Published in International Economic Review, 40, 1999
Handle: RePEc:qed:wpaper:944

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Related research
Keywords: bootstrapping hypothesis testing tobit model one-step estimation

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  1. Hiroyuki Kasahara & Katsumi Shimotsu, 2006. "Nested Pseudo-likelihood Estimation and Bootstrap-based Inference for Structural Discrete Markov Decision Models," Working Papers 1063, Queen's University, Department of Economics. [Downloadable!]
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  2. Valentina Corradi & Norman R. Swanson, 2003. "Bootstrap Conditional Distribution Tests In the Presence of Dynamic Misspecification," Departmental Working Papers 200311, Rutgers University, Department of Economics. [Downloadable!]
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  3. Martin Browning & Jens Bonke, 2006. "Allocation within the household: direct survey evidence," Economics Series Working Papers 286, University of Oxford, Department of Economics. [Downloadable!]
  4. Máximo Torero & Lorena Alcazar & Eduardo Nakasone, 2007. "Provision of Public Services and Welfare of the Poor: Learning from an Incomplete Electricity Privatization Process in Rural Peru," RES Working Papers 3232, Inter-American Development Bank, Research Department. [Downloadable!]
  5. Jurgen Doornik & Marius Ooms, 2004. "Inference and Forecasting for ARFIMA Models With an Application to US and UK Inflation," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 8(2), pages 1218-1218. [Downloadable!] (restricted)
  6. Russell Davidson & James G. MacKinnon, 2001. "Artificial Regressions," Working Papers 1038, Queen's University, Department of Economics. [Downloadable!]
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  7. Yuriy Gorodnichenko, 2005. "Reduced-Rank Identification of Structural Shocks in VARs," Macroeconomics 0512011, EconWPA. [Downloadable!]
  8. M. Ooms & J.A. Doornik, 1999. "Inference and forecasting for fractional autoregressive integrated moving average models; with an application to US and UK inflation," Econometric Institute Report 171, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
  9. Aaron Mehrotra & Tuomas Peltonen & Alvaro Santos Rivera, 2007. "Modelling inflation in China - a regional perspective," Working Paper Series 829, European Central Bank. [Downloadable!]
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  10. Silvia Goncalves & Halbert White, 2002. "Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models," University of California at San Diego, Economics Working Paper Series 2000-32R, Department of Economics, UC San Diego. [Downloadable!]
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  11. Jason Allen & Allan W. Gregory & Katsumi Shimotsu, 2008. "Empirical Likelihood Block Bootstrapping," Working Papers 1156, Queen's University, Department of Economics. [Downloadable!]
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  12. Máximo Torero & Lorena Alcazar & Eduardo Nakasone, 2007. "El suministro de servicios públicos y bienestar social para los pobres. Aprendizaje de la privatización incompleta del sector eléctrico en Perú," RES Working Papers 3233, Inter-American Development Bank, Research Department. [Downloadable!]
  13. Denis Bolduc & Lynda Khalaf & Clément Yélou, 2005. "Identification Robust Confidence Sets Methods for Inference on Parameter Ratios and their Application to Estimating Value-of-Time," Computing in Economics and Finance 2005 48, Society for Computational Economics. [Downloadable!]
  14. Jean-Marie Dufour & Lynda Khalaf, 2000. "Simulation Based Finite and Large Sample Tests in Multivariate Regressions," CIRANO Working Papers 2000s-15, CIRANO. [Downloadable!]
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  15. Russell Davidson & James G. MacKinnon, 2000. "Improving the Reliability of Bootstrap Tests," Working Papers 995, Queen's University, Department of Economics. [Downloadable!]
  16. Russell Davidson & James G. MacKinnon, 2006. "Improving the Reliability of Bootstrap Tests with the Fast Double Bootstrap," Working Papers 1044, Queen's University, Department of Economics. [Downloadable!]
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