Operations Research
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Operations Research2014-07-13Walter FrischPartial Stochastic Dominance
http://d.repec.org/n?u=RePEc:ipg:wpaper:2014-403&r=ore
The stochastic dominance ordering over probability distributions is one of the most familiar concepts in economic and financial analysis. One difficulty with stochastic dominance is that many distributions are not ranked at all, even when arbitrarily close to other distributions that are. Because of this, several measures of ”partial” or ”near” stochastic dominance have been introduced into the literature—albeit on a somewhat ad hoc basis. This paper argues that there is a single measure of extent of stochastic dominance that can be regarded as the most natural default measure from the perspective of economic analysis.Takashi Kamihigashi, John Stachurski2014-06-27Stochastic dominance, stochastic orderEmpirical Bayes Methods for Dynamic Factor Models
http://d.repec.org/n?u=RePEc:dgr:uvatin:20140061&r=ore
We consider the dynamic factor model where the loading matrix, the dynamic factors and the disturbances are treated as latent stochastic processes. We present empirical Bayes methods that enable the efficient shrinkage-based estimation of the loadings and the factors. We show that our estimates have lower quadratic loss compared to the standard maximum likelihood estimates. We investigate the methods in a Monte Carlo study where we document the finite sample properties. Finally, we present and discuss the results of an empirical study concerning the forecasting of U.S. macroeconomic time series using our empirical Bayes methods.Siem Jan Koopman, and Geert Mesters2014-05-23Importance sampling, Kalman filtering, Likelihood-based analysis, Posterior modes, Rao-Blackwellization, ShrinkageTime Varying Transition Probabilities for Markov Regime Switching Models
http://d.repec.org/n?u=RePEc:dgr:uvatin:20140072&r=ore
We propose a new Markov switching model with time varying probabilities for the transitions. The novelty of our model is that the transition probabilities evolve over time by means of an observation driven model. The innovation of the time varying probability is generated by the score of the predictive likelihood function. We show how the model dynamics can be readily interpreted. We investigate the performance of the model in a Monte Carlo study and show that the model is successful in estimating a range of different dynamic patterns for unobserved regime switching probabilities. We also illustrate the new methodology in an empirical setting by studying the dynamic mean and variance behavior of U.S. Industrial Production growth. We find empirical evidence of changes in the regime switching probabilities, with more persistence for high volatility regimes in the earlier part of the sample, and more persistence for low volatility regimes in the later part of the sample.Marco Bazzi, Francisco Blasques, Siem Jan Koopman, and Andre Lucas2014-06-17Hidden Markov Models; observation driven models; generalized autoregressive score dynamicsMaximum Likelihood Estimation for Correctly Specified Generalized Autoregressive Score Models: Feedback Effects, Contraction Conditions and Asymptotic Properties
http://d.repec.org/n?u=RePEc:dgr:uvatin:20140074&r=ore
The strong consistency and asymptotic normality of the maximum likelihood estimator in observation-driven models usually requires the study of the model both as a filter for the time-varying parameter and as a data generating process (DGP) for observed data. The probabilistic properties of the filter can be substantially different from those of the DGP. This difference is particularly relevant for recently developed time varying parameter models. We establish new conditions under which the dynamic properties of the true time varying parameter as well as of its filtered counterpart are both well-behaved and We only require the verification of one rather than two sets of conditions. In particular, we formulate conditions under which the (local) invertibility of the model follows directly from the stable behavior of the true time varying parameter. We use these results to prove the local strong consistency and asymptotic normality of the maximum likelihood estimator. To illustrate the results, we apply the theory to a number of empirically relevant models.Francisco Blasques, Siem Jan Koopman, and André Lucas2014-06-20Observation-driven models; stochastic recurrence equations; contraction conditions; invertibility; stationarity; ergodicity; generalized autoregressive score modelsMarkovian Equilibrium in a Model of Investment Under Imperfect Competition.
http://d.repec.org/n?u=RePEc:mse:cesdoc:14039&r=ore
This paper develops and analyzes a dynamic model of partially irreversible investment under cournot competition and stochastic evolution of demand. In this framework, I characterize the markov perfect equilibrium in which player's strategies are continuous in the state variable. There exists a zone in the space of capacities, named the no-move zone, such that if firms capacity belongs to this area, no firm invest nor disinvest at the equilibrium. Thereby, initial asymmetry between firms capacity can be preserved. If firms are outside this area, they invest in order to reached the no-move zone. The equilibrium as an efficiency property: the point of this area which is reached by the firms minimizes the investment cost of the all industry.Thomas Fagart2014-05Capacity investment and disinvestment, dynamic stochastic games, Markov perfect equilibrium, real option games.