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The Geometric Meaning of the Notion of Joint Unpredictability of a Bivariate VAR(1) Stochastic Process

Triacca, Umberto - In: Econometrics 1 (2013) 3, pp. 207-216

This paper investigates, in a particular parametric framework, the geometric meaning of joint unpredictability for a bivariate discrete process. In particular, the paper provides a characterization of the joint unpredictability in terms of distance between information sets in an Hilbert space.

Persistent link: https://www.econbiz.de/10010710610

Do Chinese stock markets share common information arrival processes?

Kostov, Philip; Wu, Ziping; McErlean, Seamus - EconWPA - 2004

According to the Mixture of Distributions Hypothesis (MDH), returns volatility and trading volume are driven by a …) volatility and trading volume changes in different financial markets. An implication is that returns volatility in one stock … market should show positive and contemporaneous correlation with returns volatility in another stock market. This paper tests …

Persistent link: https://www.econbiz.de/10005407887

Forecasting Volatility of Turkish Markets: A Comparison of Thin and Thick Models

Kilic, Ekrem - EconWPA - 2005

Volatility of financial markets is an important topic for academics, policy makers and market participants. In this … these specifications. Then assuming that the squared returns are the benchmark estimate for actual volatility of the day, I … compare all of the models with respect to how much efficient they are to mimic the realized volatility. At the same time I …

Persistent link: https://www.econbiz.de/10005556286

Data-driven jump detection thresholds for application in jump regressions

Davies, Robert; Tauchen, George - In: Econometrics 6 (2018) 2, pp. 1-25

This paper develops a method to select the threshold in threshold-based jump detection methods. The method is motivated by an analysis of threshold-based jump detection methods in the context of jump-diffusion models. We show that over the range of sampling frequencies a researcher is most...

Persistent link: https://www.econbiz.de/10011995217

Two-Part Models for Fractional Responses Defined as Ratios of Integers

Oberhofer, Harald; Pfaffermayr, Michael - In: Econometrics 2 (2014) 3, pp. 123-144

This paper discusses two alternative two-part models for fractional response variables that are defined as ratios of integers. The first two-part model assumes a Binomial distribution and known group size. It nests the one-part fractional response model proposed by Papke and Wooldridge (1996)...

Persistent link: https://www.econbiz.de/10010945729

Information Recovery in a Dynamic Statistical Markov Model

Miller, Douglas J.; Judge, George - In: Econometrics 3 (2015) 2, pp. 187-198

Although economic processes and systems are in general simple in nature, the underlying dynamics are complicated and seldom understood. Recognizing this, in this paper we use a nonstationary-conditional Markov process model of observed aggregate data to learn about and recover causal influence...

Persistent link: https://www.econbiz.de/10011211017

Plug-in Bandwidth Selection for Kernel Density Estimation with Discrete Data

Chu, Chi-Yang; Henderson, Daniel J.; Parmeter, … - In: Econometrics 3 (2015) 2, pp. 199-214

This paper proposes plug-in bandwidth selection for kernel density estimation with discrete data via minimization of mean summed square error. Simulation results show that the plug-in bandwidths perform well, relative to cross-validated bandwidths, in non-uniform designs. We further find that...

Persistent link: https://www.econbiz.de/10011220361

The SAR Model for Very Large Datasets: A Reduced Rank Approach

Burden, Sandy; Cressie, Noel; Steel, David G. - In: Econometrics 3 (2015) 2, pp. 317-338

The SAR model is widely used in spatial econometrics to model Gaussian processes on a discrete spatial lattice, but for large datasets, fitting it becomes computationally prohibitive, and hence, its usefulness can be limited. A computationally-efficient spatial model is the spatial random...

Persistent link: https://www.econbiz.de/10011276475

Selection Criteria in Regime Switching Conditional Volatility Models

Chuffart, Thomas - In: Econometrics 3 (2015) 2, pp. 289-316

A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a...

Persistent link: https://www.econbiz.de/10011276476

A Pitfall in Using the Characterization of Granger Non-Causality in Vector Autoregressive Models

Triacca, Umberto - In: Econometrics 3 (2015) 2, pp. 233-239

It is well known that in a vector autoregressive (VAR) model Granger non-causality is characterized by a set of restrictions on the VAR coefficients. This characterization has been derived under the assumption of non-singularity of the covariance matrix of the innovations. This note shows that...

Persistent link: https://www.econbiz.de/10011249490

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