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on Econometrics |
| By: | Akay, Alpaslan (IZA) |
| Abstract: | The Wooldridge method is based on a simple and novel strategy to deal with the initial values problem in the nonlinear dynamic random-effects panel data models. This characteristic of the method makes it very attractive in empirical applications. However, its finite sample performance is not known as of yet. In this paper we investigate the performance of this method in comparison with an ideal case in which the initial values are known constants, the worst scenario based on exogenous initial values assumption, and the Heckman's reduced-form approximation method which is widely used in the literature. The dynamic random-effects probit and tobit (type1) models are used as the working examples. Various designs of Monte Carlo Experiments with balanced and unbalanced panel data sets, and also two full length empirical applications are provided. The results suggest that the Wooldridge method works very well for the panels with moderately long durations (longer than 5-8 periods). In short panels Heckman's reduced-form approximation is suggested (shorter than 5 periods). It is also found that all methods perform equally well for panels of long durations (longer than 10-15 periods). |
| Keywords: | initial values problem, dynamic probit and tobit models, Monte Carlo experiment, Heckman's reduced-form approximation, Wooldridge method |
| JEL: | C23 C25 |
| Date: | 2009–01 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp3943&r=ecm |
| By: | David E. Giles (Department of Economics, University of Victoria) |
| Abstract: | We derive an analytic expression for the bias, to O(n-1) of the maximum likelihood estimator of the scale parameter in the half-logistic distribution. Using this expression to bias-correct the estimator is shown to be very effective in terms of bias reduction, without adverse consequences for the estimator’s precision. The analytic bias-corrected estimator is also shown to be dramatically superior to the alternative of bootstrap-bias-correction. |
| Keywords: | Half-logistic distribution, Life testing, Bias reduction |
| JEL: | C13 C16 C41 C46 |
| Date: | 2009–01–20 |
| URL: | http://d.repec.org/n?u=RePEc:vic:vicewp:0901&r=ecm |
| By: | Wolfgang Polasek (IHS, Austria and The Rimini Centre of Economic Analisys, Italy); Richard Sellner (IHS, Austria) |
| Abstract: | Completing data that are collected in disaggregated and heterogeneous spatial units is a quite frequent problem in spatial analyses of regional data. Chow and Lin (1971) (CL) were the rst to develop a uni ed framework for the three problems (interpolation, extrapolation and distribution) of predicting disaggregated times series by so-called indicator series. This paper develops a spatial CL procedure for disaggregating cross-sectional spatial data and compares the Maximum Likelihood and Bayesian spatial CL forecasts with the naive pro rata error distribution. We outline the error covariance structure in a spatial context, derive the BLUE for the ML estimator and the Bayesian estimation procedure by MCMC. Finally we apply the procedure to European regional GDP data and discuss the disaggregation assumptions. For the evaluation of the spatial Chow-Lin procedure we assume that only NUTS 1 GDP is known and predict it at NUTS 2 by using employment and spatial information available at NUTS 2. The spatial neighborhood is de ned by the inverse travel time by car in minutes. Finally, we present the forecast accuracy criteria comparing the predicted values with the actual observations. |
| Date: | 2008–01 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:38-08&r=ecm |
| By: | Guimaraes, Paulo (University of South Carolina); Portugal, Pedro (Universidade Nova de Lisboa) |
| Abstract: | In this paper we describe an alternative iterative approach for the estimation of linear regression models with high-dimensional fixed-effects such as large employer-employee data sets. This approach is computationally intensive but imposes minimum memory requirements. We also show that the approach can be extended to non-linear models and potentially to more than two high dimensional fixed effects. |
| Keywords: | high dimensional fixed effects, linked employer-employee data |
| JEL: | C01 C81 |
| Date: | 2009–01 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp3935&r=ecm |
| By: | Bo E. Honoré (Princeton University); Luojia Hu (Federal Reserve Bank of Chicago) |
| Abstract: | Abrevaya (1999b) considered estimation of a transformation model in the presence of left–truncation. This paper observes that a cross–sectional version of the statistical model considered in Frederiksen, Honoré, and Hu (2007) is a generalization of the model considered by Abrevaya (1999b) and the generalized model can be estimated by a pairwise comparison version of one of the estimators in Frederiksen, Honoré, and Hu (2007). Specifically, our generalization will allow for discretized observations of the dependent variable and for piecewise constant time–varying explanatory variables. |
| Date: | 2007–12 |
| URL: | http://d.repec.org/n?u=RePEc:kud:kuieca:2009_03&r=ecm |
| By: | Moral-Benito, Enrique |
| Abstract: | In the evaluation of transportation infrastructure projects, some non-tradable goods such as time are usually key determinants of the result. However, obtaining monetary values for these goods is not always easy. This paper introduces a novel approach based on the combination of bayesian posterior prediction and meta-analysis. This methodology will allow to obtain predictive distributions of the monetary values for this type of goods. Therefore, uncertainty is formally considered in the analysis. Moreover, the proposed method is easy to apply and inexpensive both in terms of time and money. Finally, an application to the value of travel time savings is also presented. |
| Keywords: | Bayesian Prediction; Meta-Analysis; Uncertainty; Value of Travel Time Savings. |
| JEL: | L91 D61 |
| Date: | 2008–12–31 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:12861&r=ecm |
| By: | Mishra, SK |
| Abstract: | In this paper we have proposed a method to conduct the ordinal canonical correlation analysis (OCCA) that yields ordinal canonical variates and the coefficient of correlation between them, which is analogous to (and a generalization of) the rank correlation coefficient of Spearman. The ordinal canonical variates are themselves analogous to the canonical variates obtained by the conventional canonical correlation analysis (CCCA). Our proposed method is suitable to deal with the multivariable ordinal data arrays. Our examples have shown that in finding canonical rank scores and canonical correlation from an ordinal dataset, the CCCA is suboptimal. The OCCA suggested by us outperforms the conventional method. Moreover, our method can take care of any of the five different schemes of rank ordering. It uses the Particle Swarm Optimizer which is one of the recent and prized meta-heuristics for global optimization. The computer program developed by us is fast and accurate. It has worked very well to conduct the OCCA. |
| Keywords: | Ordinal; Canonical correlation; rank order; rankings; scores; standard competition; modified competition; fractional; dense; Repulsive Particle Swarm; global optimization; computer program; FORTRAN |
| JEL: | C13 C43 C63 C14 C88 C61 |
| Date: | 2009–01–16 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:12796&r=ecm |