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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
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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
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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
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Micro-macro connected stochastic dynamic economic behavior systems
Judge, George - In: Econometrics 6 (2018) 4, pp. 1-14
In this paper, we borrow some of the key concepts of nonequilibrium statistical systems, to develop a framework for analyzing a self-organizing-optimizing system of independent interacting agents, with nonlinear dynamics at the macro level that is based on stochastic individual behavior at the...
Persistent link: https://www.econbiz.de/10011995232
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Sensitivity analysis of an ols multiple regression inference with respect to possible linear endogeneity in the explanatory variables, for both modest and for extremely large sampl...
Ashley, Richard A.; Parmeter, Christopher F. - In: Econometrics 8 (2020) 1, pp. 1-24
This work describes a versatile and readily-deployable sensitivity analysis of an ordinary least squares (OLS) inference with respect to possible endogeneity in the explanatory variables of the usual k-variate linear multiple regression model. This sensitivity analysis is based on a derivation...
Persistent link: https://www.econbiz.de/10012696274
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Bayesian nonparametric measurement of factor betas and clustering with application to hedge fund returns
Garay, Urbi; ter Horst, Enrique; Molina, German; … - In: Econometrics 4 (2016) 1, pp. 1-23
alphas and betas, which exhibit high volatility, especially in periods which can be identified as times of stressful market …
Persistent link: https://www.econbiz.de/10011755320
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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
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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
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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
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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