arXiv 2 Feb 2021 · Econometrics
arXiv:2102.01636 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates the size performance of Wald tests for CAViaR models (Engle and Manganelli, 2004). We find that the usual estimation strategy on test statistics yields inaccuracies. Indeed, we show that existing density estimation methods cannot adapt to the time-variation in the conditional probability densities of CAViaR models. Consequently, we develop a method called adaptive random bandwidth which can approximate time-varying conditional probability densities robustly for inference testing on CAViaR models based on the asymptotic normality of the model parameter estimator. This proposed method also avoids the problem of choosing an optimal bandwidth in estimating probability densities, and can be extended to multivariate quantile regressions straightforward.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Engle, R. F. and Manganelli, S (2004) Caviar: Conditional autoregressive value at risk by regression quantiles | 0.923 | 28 | 6 | 79% |
| 2 | Koenker, R (2005) Quantile regression | 0.811 | 4 | 2 | 100% |
| 3 | de Paula Ferrari, S. L. and Cribari-Neto, F (1993) On the corrections to the wald test of non-linear restrictions | 0.644 | 2 | 2 | 100% |
| 4 | Phillips, P. C. and Park, J. Y (1988) On the formulation of wald tests of nonlinear restrictions | 0.644 | 2 | 2 | 100% |
| 5 | Lagarias, J. C., Reeds, J. A., Wright, M. H., and Wright, P. E (1998) Convergence properties of the nelder–mead simplex method in low dimensions | 0.511 | 2 | 2 | 50% |
| 6 | Duffie, D. and Pan, J (1997) An overview of value at risk | 0.405 | 1 | 1 | 100% |
| 7 | Hecq, A. and Sun, L (2020) Selecting between causal and noncausal models with quantile autoregressions self | 0.405 | 1 | 1 | 100% |
| 8 | Hendricks, W. and Koenker, R (1992) Hierarchical spline models for conditional quantiles and the demand for electricity | 0.405 | 1 | 1 | 100% |
| 9 | Huber, P. J. et al (1967) The behavior of maximum likelihood estimates under nonstandard conditions | 0.405 | 1 | 1 | 100% |
| 10 | Koenker, R. and Xiao, Z (2006) Quantile autoregression | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 15 scored citations.