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This paper analyzes the indentured servitude labor market of colonial America. It is argued that this system developed endogenous market responses to such things as limitations of the legal system [...]

I. INTRODUCTION The issue of decomposing output fluctuations into permanent (trend) and transitory (cycles) components has received much attention in the recent literature. The traditional practice until the early 1980s [...]

We introduce a new bootstrap-based prediction interval for missing responses in beta regressions and, using numerical experiments, compare it to two other resampling-based intervals that are available in the literature. Additionally, we evaluate the impact of model misspecification on the empirical coverages of different beta regression prediction intervals. The numerical evidence we provide shows that the prediction interval we propose typically displays coverage rates that are slightly closer to the desired coverage levels than the competing prediction intervals. We also investigate the impact of model misspecification on three bootstrap prediction intervals. The numerical evidence shows that in some cases interval prediction remains accurate. In particular, misspecification of the model link functions has little impact on interval prediction accuracy. In contrast, the prediction intervals display substantial overcoverages when one fails to account for varying precision or for existing nonlinearity. An empirical application is also presented and discussed. [ABSTRACT FROM AUTHOR]

We obtain local influence measures and residuals for the unit gamma regression model. In particular, we introduce four residuals that are based on Fisher's iterative scoring parameter estimation algorithm and develop local influence analysis based on several different perturbation schemes: cases weighting, response additive perturbation, and covariate(s) additive perturbation. An empirical application in which variables related to education and investment in research and development are used to explain the proportion of nonpoor people in a set of countries is presented and discussed. Residual and local influence analyses show that the unit gamma regression model yields a good fit to the data, even outperforming the beta regression model. The diagnostic analysis singles out countries whose data are worthy of further investigation. Our results reveal that lower poverty levels are associated with higher shares of investment in high technology. The statistical significance of such a relationship is not sensitive to atypical data points. [ABSTRACT FROM AUTHOR]

This paper deals with numerical problems arising when performing maximum likelihood parameter estimation in speckled imagery using small samples. The noise that appears in images obtained with coherent illumination, as is the case of sonar, laser, ultrasound-B, and synthetic aperture radar, is called speckle, and it can neither be assumed Gaussian nor additive. The properties of speckle noise are well described by the multiplicative model, a statistical framework from which stem several important distributions. Amongst these distributions, one is regarded as the universal model for speckled data, namely, the law. This paper deals with amplitude data, so the distribution will be used. The literature reports that techniques for obtaining estimates (maximum likelihood, based on moments and on order statistics) of the parameters of the distribution require samples of hundreds, even thousands, of observations in order to obtain sensible values. This is verified for maximum likelihood estimation, and a proposal based on alternate optimization is made to alleviate this situation. The proposal is assessed with real and simulated data, showing that the convergence problems are no longer present. A Monte Carlo experiment is devised to estimate the quality of maximum likelihood estimators in small samples, and real data is successfully analyzed with the proposed alternated procedure. Stylized empirical influence functions are computed and used to choose a strategy for computing maximum likelihood estimates that is resistant to outliers.

Regression analyses are commonly performed with doubly limited continuous dependent variables; for instance, when modeling the behavior of rates, proportions and income concentration indices. Several models are available in the literature for use with such variables, one of them being the unit gamma regression model. In all such models, parameter estimation is typically performed using the maximum likelihood method and testing inferences on the model's parameters are usually based on the likelihood ratio test. Such a test can, however, deliver quite imprecise inferences when the sample size is small. In this paper, we propose two modified likelihood ratio test statistics for use with the unit gamma regressions that deliver much more accurate inferences when the number of data points in small. Numerical (i.e. simulation) evidence is presented for both fixed dispersion and varying dispersion models, and also for tests that involve nonnested models. We also present and discuss two empirical applications. [ABSTRACT FROM AUTHOR]

We address the issue of performing portmanteau testing inference using time series data that assume values in the standard unit interval. The motivation involves modeling the time series dynamics of the proportion of stocked hydroelectric energy in the South of Brazil. Our focus lies in the class of beta autoregressive moving average (βARMA) models. In particular, we wish to test the goodness‐of‐fit of such models. We consider several testing criteria that have been proposed for Gaussian time series models and introduce two new tests. We derive the asymptotic null distribution of the two proposed test statistics in two different scenarios, namely, when the tests are applied to an observed time series and when they are applied to the residuals from a fitted βARMA model. It is worth noticing that our results imply the asymptotic validity of standard portmanteau tests in the class of βARMA models that are, under the null hypothesis, asymptotically equivalent to our test statistics. We use Monte Carlo simulation to assess the relative merits of the different portmanteau tests when used with fitted βARMA models. The simulation results we present show that the new tests are typically more powerful than a well‐known test whose test statistic is also based on residual partial autocorrelations. Overall, the tests we propose perform quite well. Finally, we model the dynamics of the proportion of stocked hydroelectric energy in Brazil. The results show that the βARMA model outperforms three alternative models and an exponential smoothing algorithm. [ABSTRACT FROM AUTHOR]

Extreme-value theory is useful for modelling extremal events. The behaviour of such extremal events may be impacted by other variables and such dependence is captured using a regression framework. In this paper, we develop residual based diagnostic analysis, generalized leverage, generalized Cook's distance and also global and local influence analysis for the generalized extreme-value nonlinear regression model. Two residuals for use with the model are proposed: the standardized and deviance residuals. Additionally, we present a model misspecification test that can be used to determine whether the fitted model is incorrectly specified. We also show how to perform nonnested testing inferences. Empirical applications based on simulated and observed data are presented and discussed. [ABSTRACT FROM AUTHOR]