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on Econometrics |
| By: | Renos Vakis (World Bank); Elisabeth Sadoulet (University of California, Berkeley); Alain de Janvry (University of California, Berkeley); Carlo Cafiero (Universita degli Studi di Napoli Federico II) |
| Abstract: | Knowing whether a household behaves according to separability or non-separability is needed for the correct modeling of production decisions. We propose a superior test to those found in the literature on separability by using a mixture distribution approach to estimate the probability that a farm household behaves according to non-separability, and test that the determinants of consumption affect production decisions for households categorized as non-separable. With non-separability attributed to labor market constraints, the switcher equation shows that Peruvian farm households that are indigenous and young, with low levels of education, and lack of local employment opportunities are more likely to be constrained on the labor market. |
| Keywords: | labor, separability, mixture distributions, Peru, |
| Date: | 2004–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:agrebk:1079&r=ecm |
| By: | Timothy Beatty (University of British Columbia); Jeffrey LaFrance (University of California, Berkeley); Muzhe Yang (University of California, Berkeley) |
| Abstract: | This paper proposes a straightforward, easy to implement approximate F-test which is useful for testing restrictions in multivariate regression models. We derive the asymptotics for our test statistic and investigate its finite sample properties through a series of Monte Carlo experiments. Both theory suggests and simulations confirm that our approach will result in strictly better inference than the leading alternative |
| Keywords: | econometric models, monte carlo analysis, multivariate analysis, regression models, |
| Date: | 2005–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:agrebk:1084&r=ecm |
| By: | Quoreshi, Shahiduzzaman (Department of Economics, Umeå University) |
| Abstract: | This thesis comprises two papers concerning modelling of financial count data. The papers advance the integer-valued moving average model (INMA), a special case of integer-valued autoregressive moving average (INARMA) model class, and apply the models to the number of stock transactions in intra-day data. <p> Paper [1] advances the INMA model to model the number of transactions in stocks in intra-day data. The conditional mean and variance properties are discussed and model extensions to include, e.g., explanatory variables are offered. Least squares and generalized method of moment estimators are presented. In a small Monte Carlo study a feasible least squares estimator comes out as the best choice. Empirically we find support for the use of long-lag moving average models in a Swedish stock series. There is evidence of asymmetric effects of news about prices on the number of transactions. <p> Paper [2] introduces a bivariate integer-valued moving average model (BINMA) and applies the BINMA model to the number of stock transactions in intra-day data. The BINMA model allows for both positive and negative correlations between the count data series. The study shows that the correlation between series in the BINMA model is always smaller than 1 in an absolute sense. The conditional mean, variance and covariance are given. Model extensions to include explanatory variables are suggested. Using the BINMA model for AstraZeneca and Ericsson B it is found that there is positive correlation between the stock transactions series. Empirically, we find support for the use of long-lag bivariate moving average models for the two series. |
| Keywords: | Count data; Intra-day; High frequency; Time series; Estimation; Long memory; Finance |
| JEL: | C13 C22 C25 C51 G12 G14 |
| Date: | 2005–04–20 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:umnees:0656&r=ecm |
| By: | Li, Mingliang; Tobias, Justin |
| Abstract: | We describe a new Bayesian estimation algorithm for fitting a binary treatment, ordered outcome selection model in a potential outcomes framework. We show how recent advances in simulation methods, namely data augmentation, the Gibbs sampler and the Metropolis-Hastings algorithm, can be used to fit this model efficiently, and also introduce a reparameterization to help accelerate the convergence of our posterior simulator. Several computational strategies which allow for non-Normality are also discussed. Conventional ``treatment effects'' such as the Average Treatment Effect (ATE), the effect of treatment on the treated (TT) and the Local Average Treatment Effect (LATE) are derived for this specific model, and Bayesian strategies for calculating these treatment effects are introduced. Finally, we review how one can potentially learn (or at least bound) the non-identified cross-regime correlation parameter and use this learning to calculate (or bound) parameters of interest beyond mean treatment effects. |
| Date: | 2005–04–15 |
| URL: | http://d.repec.org/n?u=RePEc:isu:genres:12293&r=ecm |
| By: | Kreider, Brent; Hill, Steven C. |
| Abstract: | We extend the nonparametric literature on partially identified probability distributions and use the framework to make inferences about relationships between health care utilization and insurance status in an environment of uncertainty about the accuracy of self-reported insurance data. Allowing for endogenous reporting error, we use information from the 1996 Medical Expenditure Panel Survey (MEPS) to study what can be learned about (a) the gap between the insured and uninsured in the use of health services and (b) the potential impact of universal insurance coverage on the use of services. We exploit outside information from insurance cards and follow-back interviews with employers, insurance companies, and medical providers to construct validation data for a nonrandom portion of the sample. We formally assess the identifying power of a variety of nonparametric assumptions that shrink identification regions compared with those provided in previous studies, such as Molinari’s (2002) treatment effect bounds under missing treatments. Under our strongest nonparametric assumptions, universal health insurance would increase the proportion of the population using health care in a month by no more than 2.5 percentage points for adults (an 11% increase) and by no more than 0.8 percentage points for children (a 4.8% increase). |
| JEL: | C1 I1 |
| Date: | 2005–04–17 |
| URL: | http://d.repec.org/n?u=RePEc:isu:genres:12296&r=ecm |
| By: | Orazio Di Miscia (Banca Intesa) |
| Abstract: | A Monte Carlo simulation is performed to investigate the finite sample properties of a nonparametric estimator, based on discretely sampled observations of continuous-time Ito diffusion process. Chapman and Pearson (2000) studies finite-sample properties of the nonparametric estimator of Aýt-Sahalia (1996) and Stanton (1997) and they find that nonlinearity of the short rate drift is not a robust stylized fact but it’s an artifacts of the estimation procedure. This paper examine the finite sample properties of a different nonparametric estimator within the Stanton (1997)’s framework. |
| Keywords: | ewp-mac/050417 |
| JEL: | G |
| Date: | 2005–04–19 |
| URL: | http://d.repec.org/n?u=RePEc:wpa:wuwpfi:0504016&r=ecm |