A Consistent Bootstrap Test for Conditional Density Functions with Time-Dependent Data
AbstractThis paper describes a new test for evaluating conditional density functions that remains valid when the data are time-dependent and that is therefore applicable to forecasting problems. We show that the test statistic is asymptotically distributed standard normal under the null hypothesis, and diverges to infinity when the null hypothesis is false. We use a bootstrap algorithm to approximate the distribution of the test statistic in finite samples, and show that the bootstrapped distribution converges to the asymptotic distribution in probability. A Monte Carlo simulation study reveals that the bootstrap test works well and is highly robust to the value of the smoothing parameter in the kernel density estimator. An application to inflation forecasting is also presented to demonstrate the use of the test.
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Bibliographic InfoPaper provided by Bank of Canada in its series Working Papers with number 01-21.
Length: 47 pages
Date of creation: 2001
Date of revision:
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Econometric and statistical methods;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
This paper has been announced in the following NEP Reports:
- NEP-ALL-2001-12-26 (All new papers)
- NEP-CMP-2001-12-26 (Computational Economics)
- NEP-ECM-2001-12-26 (Econometrics)
- NEP-ETS-2001-12-26 (Econometric Time Series)
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