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on Forecasting |
| By: | Isengildina-Massa, Olga; Sharp, Julia L. |
| Keywords: | Research Methods/ Statistical Methods, Risk and Uncertainty, |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea13:150791&r=for |
| By: | Fujii, Tomoki; van der Weide, Roy |
| Abstract: | This paper considers the prediction estimator as an efficient estimator for the population mean. The study may be viewed as an earlier study that proved that the prediction estimator based on the iteratively weighted least squares estimator outperforms the sample mean. The analysis finds that a certain moment condition must hold in general for the prediction estimator based on a Generalized-Method-of-Moment estimator to be at least as efficient as the sample mean. In an application to cost-effective double sampling, the authors show how prediction estimators may be adopted to maximize statistical precision (minimize financial costs) under a budget constraint (statistical precision constraint). This approach is particularly useful when the outcome variable of interest is expensive to observe relative to observing its covariates. |
| Date: | 2013–06–01 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:wbrwps:6509&r=for |
| By: | Zhongxian Men (Department of Statistics & Actuarial Science, University of Waterloo, Canada); Tony S. Wirjanto (Department of Statistics & Actuarial Science, University of Waterloo, Canada; School of Accounting and Finance, University of Waterloo, Canada); Adam W. Kolkiewicz (Department of Statistics & Actuarial Science, University of Waterloo, Canada) |
| Abstract: | This paper proposes a threshold stochastic conditional duration (TSCD) model to capture the asymmetric property of financial transactions. The innovation of the observable duration equation is assumed to follow a threshold distribution with two component distributions switching between two regimes. The distributions in different regimes are assumed to be Exponential, Gamma or Weibull. To account for uncertainty in the unobserved threshold level, the observed durations are treated as self-exciting threshold variables. Adopting a Bayesian approach, we develop novel Markov Chain Monte Carlo algorithms to estimate all of the unknown parameters and latent states. To forecast the one-step ahead durations, we employ an auxiliary particle filter where the filter and prediction distributions of the latent states are approximated. The proposed model and the developed MCMC algorithms are illustrated by using both simulated and actual financial transaction data. For model selection, a Bayesian deviance information criterion is calculated to compare our model with other competing models in the literature. Overall, we find that the threshold SCD model performs better than the SCD model when a single positive distribution is assumed for the innovation of the duration equation. |
| Keywords: | Stochastic conditional duration; Threshold; Markov Chain Monte Carlo; Auxiliary particle filter; Deviance information criterion |
| Date: | 2013–05 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:30_13&r=for |
| By: | Wang, Jing; Dharmasena, Senarath; Bessler, David A. |
| Keywords: | Demand and Price Analysis, |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea13:151150&r=for |
| By: | Zhongxian Men (Department of Statistics & Actuarial Science, University of Waterloo, Canada); Adam W. Kolkiewicz (Department of Statistics & Actuarial Science, University of Waterloo, Canada); Tony S. Wirjanto (Department of Statistics & Actuarial Science, University of Waterloo, Canada) |
| Abstract: | This paper extends stochastic conditional duration (SCD) models for financial transaction data to allow for correlation between error processes or innovations of observed duration process and latent log duration process. Novel algorithms of Markov Chain Monte Carlo (MCMC) are developed to fit the resulting SCD models under various distributional assumptions about the innovation of the measurement equation. Unlike the estimation methods commonly used to estimate the SCD models in the literature, we work with the original specification of the model, without subjecting the observation equation to a logarithmic transformation. Results of simulation studies suggest that our proposed models and corresponding estimation methodology perform quite well. We also apply an auxiliary particle filter technique to construct one-step-ahead in-sample and out-of-sample duration forecasts of the fitted models. Applications to the IBM transaction data allows comparison of our models and methods to those existing in the literature. |
| Keywords: | Stochastic Duration; Bayesian Inference; Markov Chain Monte Carlo; Leverage Effect; Acceptance-rejection; Slice Sampler |
| Date: | 2013–05 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:28_13&r=for |
| By: | Li, Shuang; Ker, Alan P. |
| Abstract: | Research and observations indicate climate change has and will have an impact on On- tario eld crop production. Little research has done to forecast how climate change might in uence the Canadian Federal-Provincial Crop Insurance program, including its premium rates and reserve fund balances, in the future decades. This paper proposes using a mixture of two normal yield probability distribution model to model crop yield conditions under hypothetical climate change scenarios. Then superimposes Crop Insur- ance premium rate and reserve fund balance calculations onto the yield model to forecast their trends and uctuation situations in the future decades. We nd under the scenarios where climate change alters the probability of a lower yield year occurring and where climate change alters yield averages, both have more signicant impacts on premium rates and reserve fund balances, compared to the scenarios where climate change alters yield variations. The results of this research will help Agricorp Ltd. identify the likely frequency and magnitude of both insurance premium rate uctuations and reserve fund balance uctuations under dierent climate change scenarios. Therefore the results can be used to help Agricorp Ltd. identify and forecast both premium rate uctuation risk and reserve fund liquidity risk. |
| Keywords: | Crop Production/Industries, Environmental Economics and Policy, International Relations/Trade, Production Economics, Research and Development/Tech Change/Emerging Technologies, |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea13:151213&r=for |
| By: | Tony S. Wirjanto (Department of Statistics and Actuarial Science, University of Waterloo, Canada); Adam W. Kolkiewicz (Department of Statistics and Actuarial Science, University of Waterloo, Canada); Zhongxian Men (Department of Statistics and Actuarial Science, University of Waterloo, Canada) |
| Abstract: | This paper studies a stochastic conditional duration (SCD) model with a mixture of distribution processes for financial asset’s transaction data. Specifically it imposes a mixture of two positive distributions on the innovations of the observed duration process, where the mixture component distributions could be either Exponential, Gamma or Weibull. The model also allows for correlation between the observed durations and the logarithm of the latent conditionally expected durations in order to capture a leverage effect known to exist in the equity market. In addition the proposed mixture SCD model is shown to be able to accommodate possibly heavy tails of the marginal distribution of durations. Novel Markov Chain Monte Carlo (MCMC) algorithms are developed for Bayesian inference of parameters and duration forecasting of these models. Simulation studies and empirical applications to two stock duration data sets are provided to assess the performance of the proposed mixture SCD models and the accompanying MCMC algorithms. |
| Keywords: | Stochastic conditional duration; Mixture of distributions; Bayesian inference; Markov Chain Monte Carlo; Leverage effect; Slice sampler |
| Date: | 2013–05 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:29_13&r=for |