The Explanatory Power of Explanatory Variables
In: Review of Accounting Studies, forthcoming
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In: Review of Accounting Studies, forthcoming
SSRN
Working paper
In: Statistica Neerlandica, Band 34, Heft 3, S. 141-150
ISSN: 1467-9574
AbstractWe consider a linear regression model where some explanatory variables are unknown members of sets of alternative explanatory variables. It will be shown that under weak conditions the minimum residual variance criterion for selecting these explanatory variables has the property that the probability of selecting wrong explanatory variables vanishes if the number of observations increases to infmity. Moreover, the O.L.S. estimator of the resulting "specified" model turns out to be consistent, while in the case that all the parameters are nonzero it can be shown that this O.L.S. estimator has the same limiting distribution as the O.L.S. estimator of the true model.
In: Electoral Systems and Democratization in Southern Africa, S. 58-88
In: Journal of accounting and public policy, Band 10, Heft 4, S. 309-323
ISSN: 0278-4254
In: The Europeanization of National Polities?Citizenship and Support in a Post-Enlargement Union, S. 39-60
In: Statistica Neerlandica: journal of the Netherlands Society for Statistics and Operations Research, Band 73, Heft 1, S. 118-138
ISSN: 1467-9574
In this paper, we introduce a threshold stochastic volatility model with explanatory variables. The Bayesian method is considered in estimating the parameters of the proposed model via the Markov chain Monte Carlo (MCMC) algorithm. Gibbs sampling and Metropolis–Hastings sampling methods are used for drawing the posterior samples of the parameters and the latent variables. In the simulation study, the accuracy of the MCMC algorithm, the sensitivity of the algorithm for model assumptions, and the robustness of the posterior distribution under different priors are considered. Simulation results indicate that our MCMC algorithm converges fast and that the posterior distribution is robust under different priors and model assumptions. A real data example was analyzed to explain the asymmetric behavior of stock markets.
In: Eastern European economics: EEE, Band 42, Heft 6, S. 5-38
ISSN: 1557-9298
In: Eastern European economics, Band 42, Heft 6, S. 5-38
ISSN: 0012-8775
World Affairs Online
International audience We are interested in modeling the impact of media investments on automobile manufacturer's market shares. Regression models have been developed for the case where the dependent variable is a vector of shares. Some of them, from the marketing literature, are easy to interpret but quite simple (Model A). Other models, from the compositional data analysis literature, allow a large complexity but their interpretation is not straightforward (ModelB).This paper combines both literatures in order to obtain a performing market share model and develop relevant interpretations for practical use. We prove that Model A is a particular case of Model B, and that an intermediate specification is possible (Model AB). A model selection procedure is proposed. Several impact measures are presented and we show that elasticities are particularly useful: they can be computed from the transformed or from the original model, and they are linked to the simplicial derivatives.
BASE
In: International journal of forecasting, Band 7, Heft 2, S. 127-140
ISSN: 0169-2070
In: The Journal of social psychology, Band 123, Heft 1, S. 71-78
ISSN: 1940-1183
In: Decision sciences, Band 7, Heft 1, S. 57-65
ISSN: 1540-5915
ABSTRACTThe robustness of linear programming regression estimators is examined where the disturbance terms are normally distributed and there are observation errors in the explanatory variables. These errors are occasional gross biases between one set of observations and another. The simulation of short series data offers preliminary evidence that when these biases have a non‐zero mean, MSAE estimation is more robust than least squares.
SSRN
Working paper
In: Cross cultural management, Band 5, Heft 3, S. 5-30
ISSN: 1758-6089
In: Socio-economic planning sciences: the international journal of public sector decision-making, Band 18, Heft 6, S. 425-431
ISSN: 0038-0121