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on Econometric Time Series |
| By: | Adam D. Bull |
| Abstract: | In quantitative finance, we often model asset prices as semimartingales, with drift, diffusion and jump components. The jump activity index measures the strength of the jumps at high frequencies, and is of interest both in model selection and fitting, and in volatility estimation. In this paper, we give a novel estimate of the jump activity, together with corresponding confidence intervals. Our estimate improves upon previous work, achieving near-optimal rates of convergence, and good finite-sample performance in Monte-Carlo experiments. |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1409.8150&r=ets |
| By: | Zhenlin Yang (School of Economics, Singapore Management University, Singapore, 178903) |
| Abstract: | It is well known that (quasi) MLE of dynamic panel data (DPD) models with short panels depends on the assumptions on the initial values; ignoring them or a wrong treatment of them will result in inconsistency or serious bias. This paper introduces a initial-condition free method for estimating the fixed-effects DPD models, through a simple modification of the quasi-score. An outer-product-of-gradients (OPG) method is also proposed for robust inference. The MLE of Hsiao, Pesaran and Tahmiscioglu (2002, Journal of Econometrics), where the initial observations are modeled, is extended to quasi MLE and an OPG method is proposed for robust inference. Consistency and asymptotic normality for both estimation strategies are established, and the two methods are compared through Monte Carlo simulations. The proposed method performs well in general, whether the panel is short or not. The quasi MLE performs comparably, except when model does not contain time-varying regressor, or the panel is not short and the dynamic parameter is small. The proposed method is much simpler and easier to apply. |
| Keywords: | Bias reduction; Consistency; Asymptotic normality; Dynamic panel; Fixed effects; Modified quasi-score; Robust standard error; Short panel |
| JEL: | C10 C13 C23 C15 |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:siu:wpaper:16-2014&r=ets |
| By: | Ulrich Hounyo (Oxford-Man Institute, University of Oxford, and Aarhus University and CREATES) |
| Abstract: | In this paper, a new resampling procedure, called the wild tapered block bootstrap, is introduced as a means of calculating standard errors of estimators and constructing confidence regions for parameters based on dependent heterogeneous data. The method consists in tapering each overlapping block of the series first, then applying the standard wild bootstrap for independent and heteroscedastic distributed observations to overlapping tapered blocks in an appropriate way. It preserves the favorable bias and mean squared error properties of the tapered block bootstrap, which is the state-of-the-art block-based method in terms of asymptotic accuracy of variance estimation and distribution approximation. For stationary time series, the asymptotic validity, and the favorable bias properties of the new bootstrap method are shown in two important cases: smooth functions of means, and M-estimators. The first-order asymptotic validity of the tapered block bootstrap as well as the wild tapered block bootstrap approximation to the actual distribution of the sample mean is also established when data are assumed to satisfy a near epoch dependent condition. The consistency of the bootstrap variance estimator for the sample mean is shown to be robust against heteroskedasticity and dependence of unknown form. Simulation studies illustrate the finite-sample performance of the wild tapered block bootstrap. This easy to implement alternative bootstrap method works very well even for moderate sample sizes. |
| Keywords: | Block bootstrap, Near epoch dependence, Tapering, Variance estimation |
| JEL: | C15 C22 |
| Date: | 2014–09–24 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2014-32&r=ets |
| By: | Joshua C C Chan (Australian National University); Eric Eisenstat (University of Bucharest); Gary Koop (Department of Economics, University of Strathclyde) |
| Abstract: | Abstract: Vector Autoregressive Moving Average (VARMA) models have many theoretical properties which should make them popular among empirical macroeconomists. However, they are rarely used in practice due to over-parameterization concerns, difficult - ties in ensuring identification and computational challenges. With the growing interest in multivariate time series models of high dimension, these problems with VARMAs become even more acute, accounting for the dominance of VARs in this field. In this paper, we develop a Bayesian approach for inference in VARMAs which surmounts these problems. It jointly ensures identification and parsimony in the context of an efficient Markov chain Monte Carlo (MCMC) algorithm. We use this approach in a macroeconomic application involving up to twelve dependent variables. We find our algorithm to work successfully and provide insights beyond those provided by VARs |
| Keywords: | VARMA identification, Markov Chain Monte Carlo, Bayesian, stochastic search variable selection |
| JEL: | C11 C32 E37 |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:str:wpaper:1409&r=ets |