Linear Programming-Based Estimators in Simple Linear Regression

In this paper we introduce a linear programming estimator (LPE) for the slope parameter in a constrained linear regression model with a single regressor. The LPE is interesting because it can be superconsistent in the presence of an endogenous regressor and, hence, preferable to the ordinary least squares estimator (LSE). Two dierent cases are considered as we investigate the statistical properties of the LPE. In the rst case, the regressor is assumed to be xed in repeated samples. In the second, the regressor is stochastic and potentially endogenous. For both cases the strong consistency and exact nite-sample distribution of the LPE is established. Conditions under which the LPE is consistent in the presence of serially correlated, heteroskedastic errors are also given. Finally, we describe how the LPE can be extended to the case with multiple regressors and conjecture that the extended estimator is consistent under conditions analogous to the ones given herein. Finite-sample properties of the LPE and extended LPE in comparison to the LSE and instrumental variable estimator (IVE) are investigated in a simulation study. One advantage of the LPE is that it does not require an instrument.

Forecasting Realized Volatility Using A Nonnegative Semiparametric Model

This paper introduces a parsimonious and yet flexible nonnegative semiparametric model to forecast financial volatility. The new model extends the linear nonnegative autoregressive model of Barndorff-Nielsen & Shephard (2001) and Nielsen & Shephard (2003) by way of a power transformation. It is semiparametric in the sense that the dependency structure and distributional form of its error component are left unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Simulation studies validate the new estimation method and suggest that it works reasonably well in finite samples. The out-of-sample performance of the proposed model is evaluated against a number of standard methods, using data on S&P 500 monthly realized volatilities. The competing models include the exponential smoothing method, a linear AR(1) model, a log-linear AR(1) model, and two long-memory ARFIMA models. Various loss functions are utilized to evaluate the predictive accuracy of the alternative methods. It is found that the new model generally produces highly competitive forecasts.Autoregression, nonlinear/non-Gaussian time series, realized volatility, semiparametric model, volatility forecast.

Forecasting Realized Volatility Using A Nonnegative Semiparametric Model

This paper introduces a parsimonious and yet flexible nonnegative semiparametric model to forecast financial volatility. The new model extends the linear nonnegative autoregressive model of Barndorff-Nielsen & Shephard (2001) and Nielsen & Shephard (2003) by way of a power transformation. It is semiparametric in the sense that the dependency structure and distributional form of its error component are left unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Simulation studies validate the new estimation method and suggest that it works reasonably well in finite samples. The out-of-sample performance of the proposed model is evaluated against a number of standard methods, using data on S&P 500 monthly realized volatilities. The competing models include the exponential smoothing method, a linear AR(1) model, a log-linear AR(1) model, and two long-memory ARFIMA models. Various loss functions are utilized to evaluate the predictive accuracy of the alternative methods. It is found that the new model generally produces highly competitive forecasts.Autoregression, nonlinear/non-Gaussian time series, realized volatility, semiparametric model, volatility forecast

A mixture autoregressive model based on Student's t-distribution

A new mixture autoregressive model based on Student's t-distribution is proposed. A key feature of our model is that the conditional t-distributions of the component models are based on autoregressions that have multivariate t-distributions as their (low-dimensional) stationary distributions. That autoregressions with such stationary distributions exist is not immediate. Our formulation implies that the conditional mean of each component model is a linear function of past observations and the conditional variance is also time-varying. Compared to previous mixture autoregressive models our model may therefore be useful in applications where the data exhibits rather strong conditional heteroskedasticity. Our formulation also has the theoretical advantage that conditions for stationarity and ergodicity are always met and these properties are much more straightforward to establish than is common in nonlinear autoregressive models. An empirical example employing a realized kernel series constructed from S&P 500 high-frequency intraday data shows that the proposed model performs well in volatility forecasting. Our methodology is implemented in the freely available StMAR Toolbox for MATLAB.Peer reviewe

A constitucionalização do Direito Privado

No presente estudo são analisados os principais postulados da Constitucionalização do Direito Civil. Num primeiro momento, analisa-se o fenômeno da constitucionalização do Direito Civil, desde as premissas do Estado Liberal até a ascensão do Estado Social. Por sua vez, a constitucionalização do Direito Civil promove uma releitura dos estatutos fundamentais do Direito Privado clássico, baseada na aplicação científica dos princípios constitucionais, com o objetivo de por fim aos conflitos sociais e preservar o Estado democrático de direito. Tal fenômeno é conseqüência de uma nova realidade econômico-social, na qual, os padrões tradicionais foram drasticamente alterados

LEGISLAÇÃO E RECUPERAÇÃO AMBIENTAL: INSTRUMENTOS JURÍDICOS NA RECUPERAÇÃO DE ÁREAS DEGRADADAS PELA EXPLORAÇÃO CARBONÍFERA EM CRICIÚMA/SC

A atividade de exploração do carvão mineral no município de Criciúma proporcionou num primeiro momento o acúmulo de capital e o desenvolvimento de novas atividades produtivas no município