A Goodness Of Fit Test For Ergodic Markov Processes
We introduce a goodness of fit test for ergodic Markov processes. Our test compares the data against the set of stationary densities implied by the class of models specified in the null hypothesis, and rejects if no model in the class yields a stationary density that matches with the data. No alternative needs to be specified in order to implement the test. Although our test compares densities it involves no smoothing parameters, and is powerful against 1√n local alternatives.
|Date of creation:||Oct 2011|
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- John Stachurski, 2006.
"Computing the Distributions of Economic Models Via Simulation,"
KIER Working Papers
615, Kyoto University, Institute of Economic Research.
- John Stachurski & Vance Martin, 2008. "Computing the Distributions of Economic Models via Simulation," Econometrica, Econometric Society, vol. 76(2), pages 443-450, 03.
- John Stachurski & University of Melbourne, 2006. "Computing the Distributions of Economic Models via Simulation," Computing in Economics and Finance 2006 185, Society for Computational Economics.
- John Stachurski, 2005. "Computing the Distributions of Economic Models Via Simulation," Department of Economics - Working Papers Series 949, The University of Melbourne.
- Nishimura, Kazuo & Stachurski, John, 2005. "Stability of stochastic optimal growth models: a new approach," Journal of Economic Theory, Elsevier, vol. 122(1), pages 100-118, May.
- Chen, Song Xi & Gao, Jiti & Tang, Chenghong, 2005. "A test for model specification of diffusion processes," MPRA Paper 11976, University Library of Munich, Germany, revised Feb 2007.
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