30 resultados para Small Sample

em Consorci de Serveis Universitaris de Catalunya (CSUC), Spain


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Properties of GMM estimators for panel data, which have become very popular in the empirical economic growth literature, are not well known when the number of individuals is small. This paper analyses through Monte Carlo simulations the properties of various GMM and other estimators when the number of individuals is the one typically available in country growth studies. It is found that, provided that some persistency is present in the series, the system GMM estimator has a lower bias and higher efficiency than all the other estimators analysed, including the standard first-differences GMM estimator.

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In this paper I explore the issue of nonlinearity (both in the datageneration process and in the functional form that establishes therelationship between the parameters and the data) regarding the poorperformance of the Generalized Method of Moments (GMM) in small samples.To this purpose I build a sequence of models starting with a simple linearmodel and enlarging it progressively until I approximate a standard (nonlinear)neoclassical growth model. I then use simulation techniques to find the smallsample distribution of the GMM estimators in each of the models.

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In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

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Small sample properties are of fundamental interest when only limited data is avail-able. Exact inference is limited by constraints imposed by speci.c nonrandomizedtests and of course also by lack of more data. These e¤ects can be separated as we propose to evaluate a test by comparing its type II error to the minimal type II error among all tests for the given sample. Game theory is used to establish this minimal type II error, the associated randomized test is characterized as part of a Nash equilibrium of a .ctitious game against nature.We use this method to investigate sequential tests for the di¤erence between twomeans when outcomes are constrained to belong to a given bounded set. Tests ofinequality and of noninferiority are included. We .nd that inference in terms oftype II error based on a balanced sample cannot be improved by sequential sampling or even by observing counter factual evidence providing there is a reasonable gap between the hypotheses.

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Regular stair climbing has well-documented health dividends, such as increased fitness and strength, weight loss and reduced body fat, improved lipid profiles and reduced risk of osteoporosis. The general absence of barriers to participation makes stair climbing an ideal physical activity (PA) for health promotion. Studies in the US and the UK have consistently shown that interventions to increase the accumulation of lifestyle PA by climbing stairs rather than using the escalators are effective. However, there are no previous in Catalonia. This project tested one message for their ability to prompt travelers on the Montjuïc site to choose the stairs rather than the escalator when climbing up the Monjuïc hill. One standard message, " Take the stairs! 7 minutes of stair climbing a day protects your heart" provided a comparison with previous research done in the UK. Translated into Catalan and Spanish, it was presented on a poster positioned at the point of choice between the stairs and the escalator. The study used a quasi-experimental, interrupted time series design. Travelers, during several and specific hours on two days of the week, were coded for stair or escalator use, gender, age, ethnic status, presence of accompanying children or bags by one observer. Overall, the intervention resulted in a 81% increase in stair climbing. In the follow-up period without messages, stair climbing dropped out to baseline levels. This preliminary study showed a significant effect on stair use. However, caution is needed since results are based on a small sample and, only a low percentage of the sample took the stairs at baseline or the intervention phase . Future research on stair use in Catalonia should focus on using bigger samples, different sites (metro stations, airports, shopping centers, etc) , different messages and techniques to promote stair climbing.

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Consider a model with parameter phi, and an auxiliary model with parameter theta. Let phi be a randomly sampled from a given density over the known parameter space. Monte Carlo methods can be used to draw simulated data and compute the corresponding estimate of theta, say theta_tilde. A large set of tuples (phi, theta_tilde) can be generated in this manner. Nonparametric methods may be use to fit the function E(phi|theta_tilde=a), using these tuples. It is proposed to estimate phi using the fitted E(phi|theta_tilde=theta_hat), where theta_hat is the auxiliary estimate, using the real sample data. This is a consistent and asymptotically normally distributed estimator, under certain assumptions. Monte Carlo results for dynamic panel data and vector autoregressions show that this estimator can have very attractive small sample properties. Confidence intervals can be constructed using the quantiles of the phi for which theta_tilde is close to theta_hat. Such confidence intervals are found to have very accurate coverage.

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De l’experiència professional de l’equip del SATAF al llarg dels anys, s’ha constatat que un dels conflictes més recurrents, existents en els procediments contenciosos, és el rebuig filial envers un dels progenitors, habitualment, el no-custodi. Un tipus de rebuig filial és la Síndrome d’Alienació Parental (SAP), descrita per Gardner, inicialment, l’any 1985. A partir dels estudis realitzats d’ençà l’any 2004, aquest grup d’investigació pretén centrar la present recerca en el perfil de competències parentals del progenitor alienat. Paral·lelament, s’ha elaborat una entrevista semiestructurada d’exploració de la SAP. Altrament, també s’ha creat una guia d’observadors per tal d’aconseguir certa operativitat diagnòstica. Els resultats obtinguts han partit d’una anàlisis estadística no-paramètrica, considerant el petit tamany de la mostra. Amb l’anàlisi, es constata que la gravetat de la SAP no manté relació quant a presència d’habilitats parentals en el progenitor alienat. D'altra banda, l’actitud d’aquests progenitors incideix en el manteniment de la Síndrome. Disposar d’eines específiques per a l’avaluació d’aquests casos optimitza i operatitvitza les intervencions amb aquestes famílies.

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Important theoretical controversies remain unresolved in the literatire on occupational sex-segregation and the gender wage-gap. A useful way of summarising these controversies is viewing them as a debate between - cultural -socialisation. The paper discusses these theories in detail and carries out a preliminary test of the relative explanatory performance of some of their most consequential predictions. This is done by drawing on the Spanish sample of the second wave of the European Social Survey, ESS. The empirical analysis of ESS data illustrates the notable analytical pay-offs that can stem from using rich individual-level indicators, but also exemplifies the statistical llimitations generated by small sample size and high rates of non-response. Empirical results should, therefore, be taken as preliminary. They seem to suggest that the effect of occupational sex-segregation on wages could be explicable by workers' sex-role attitutes, their relative input in domestic production and the job-specific human capital requirements of their jobs. Of these three factors, job-specialisation seeems clearly the most important one.

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Several estimators of the expectation, median and mode of the lognormal distribution are derived. They aim to be approximately unbiased, efficient, or have a minimax property in the class of estimators we introduce. The small-sample properties of these estimators are assessed by simulations and, when possible, analytically. Some of these estimators of the expectation are far more efficient than the maximum likelihood or the minimum-variance unbiased estimator, even for substantial samplesizes.

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This paper discusses inference in self exciting threshold autoregressive (SETAR)models. Of main interest is inference for the threshold parameter. It iswell-known that the asymptotics of the corresponding estimator depend uponwhether the SETAR model is continuous or not. In the continuous case, thelimiting distribution is normal and standard inference is possible. Inthe discontinuous case, the limiting distribution is non-normal and cannotbe estimated consistently. We show valid inference can be drawn by theuse of the subsampling method. Moreover, the method can even be extendedto situations where the (dis)continuity of the model is unknown. In thiscase, also the inference for the regression parameters of the modelbecomes difficult and subsampling can be used advantageously there aswell. In addition, we consider an hypothesis test for the continuity ofthe SETAR model. A simulation study examines small sample performance.

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We study the statistical properties of three estimation methods for a model of learning that is often fitted to experimental data: quadratic deviation measures without unobserved heterogeneity, and maximum likelihood withand without unobserved heterogeneity. After discussing identification issues, we show that the estimators are consistent and provide their asymptotic distribution. Using Monte Carlo simulations, we show that ignoring unobserved heterogeneity can lead to seriously biased estimations in samples which have the typical length of actual experiments. Better small sample properties areobtained if unobserved heterogeneity is introduced. That is, rather than estimating the parameters for each individual, the individual parameters are considered random variables, and the distribution of those random variables is estimated.

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This paper demonstrates that, unlike what the conventional wisdom says, measurement error biases in panel data estimation of convergence using OLS with fixed effects are huge, not trivial. It does so by way of the "skipping estimation"': taking data from every m years of the sample (where m is an integer greater than or equal to 2), as opposed to every single year. It is shown that the estimated speed of convergence from the OLS with fixed effects is biased upwards by as much as 7 to 15%.

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Many theories, most famously Max Weber s essay on the Protestant ethic, have hypothesizedthat Protestantism should have favored economic development. With their considerablereligious heterogeneity and stability of denominational affiliations until the 19th century, theGerman Lands of the Holy Roman Empire present an ideal testing ground for this hypothesis.Using population figures in a dataset comprising 272 cities in the years 1300 1900, I find no effectsof Protestantism on economic growth. The finding is robust to the inclusion of a varietyof controls, and does not appear to depend on data selection or small sample size. In addition,Protestantism has no effect when interacted with other likely determinants of economic development.I also analyze the endogeneity of religious choice; instrumental variables estimates ofthe effects of Protestantism are similar to the OLS results.

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Several unit root tests in panel data have recently been proposed. The test developed by Harris and Tzavalis (1999 JoE) performs particularly well when the time dimension is moderate in relation to the cross-section dimension. However, in common with the traditional tests designed for the unidimensional case, it was found to perform poorly when there is a structural break in the time series under the alternative. Here we derive the asymptotic distribution of the test allowing for a shift in the mean, and assess the small sample performance. We apply this new test to show how the hypothesis of (perfect) hysteresis in Spanish unemployment is rejected in favour of the alternative of the natural unemployment rate, when the possibility of a change in the latter is considered.

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Several unit root tests in panel data have recently been proposed. The test developed by Harris and Tzavalis (1999 JoE) performs particularly well when the time dimension is moderate in relation to the cross-section dimension. However, in common with the traditional tests designed for the unidimensional case, it was found to perform poorly when there is a structural break in the time series under the alternative. Here we derive the asymptotic distribution of the test allowing for a shift in the mean, and assess the small sample performance. We apply this new test to show how the hypothesis of (perfect) hysteresis in Spanish unemployment is rejected in favour of the alternative of the natural unemployment rate, when the possibility of a change in the latter is considered.