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Exponential-family Random Graph models (ERGM) are widely used in social network analysis when modelling data on the relations between actors. ERGMs are typically interpreted as a snapshot of a network at a given point in time or in a final state. The recently proposed Latent Order Logistic model (LOLOG) directly allows for a latent network formation process. We assess the real-world performance of these models when applied to typical networks modelled by researchers. Specifically, we model data from an ensemble of articles in the journal Social Networks with published ERGM fits, and compare the ERGM fit to a comparable LOLOG fit. We demonstrate that the LOLOG models are, in general, in qualitative agreement with the ERGM models, and provide at least as good a model fit. In addition they are typically faster and easier to fit to data, without the tendency for degeneracy that plagues ERGMs. Our results support the general use of LOLOG models in circumstances where ERGMs are considered.

Spline smoothing is a technique used to filter out noise in time series observations when predicting nonparametric regression models. Its performance depends on the choice of the smoothing parameter. Most of the existing smoothing methods applied to time series data tend to overfit in the presence of autocorrelated errors. This study aims to determine the optimum performance value, goodness of fit and model overfitting properties of the proposed Smoothing Method (PSM), Generalized Maximum Likelihood (GML), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR) smoothing parameter selection methods. A Monte Carlo experiment of 1,000 trials was carried out at three different sample sizes (20, 60, and 100) and three levels of autocorrelation (0.2, 05, and 0.8). The four smoothing methods' performances were estimated and compared using the Predictive Mean Squared Error (PMSE) criterion. The findings of the study revealed that: for a time series observation with autocorrelated errors, Adj. R²(PSM λ = 0.04)provides the best-fit smoothing method for the model, the PSM does not over-fit data at all the autocorrelation levels considered (ρ = 0.2, 0.5 and 0.8); the optimum value of the PSM was at the weighted value of 0.04 when there is autocorrelation in the error term, PSM performed better than the GCV, GML, and UBR smoothing methods were considered at all-time series sizes (T = 20, 60 and 100). For the real-life data employed in the study, PSM proved to be the most efficient among the GCV, GML, PSM, and UBR smoothing methods compared. The study concluded that the PSM method provides the best fit as a smoothing method, works well at autocorrelation levels (ρ=0.2, 0.5, and 0.8), and does not over fit timeseries observations. The study recommended that the proposed smoothing is appropriate for time series observations with autocorrelation in the error term and econometrics real-life data. This study can be applied to; non -- parametric regression, non -- parametric forecasting, spatial, survival, and econometrics observations. [ABSTRACT FROM AUTHOR]

The maximum entropy characterization of the von Mises distribution on the circle is exploited to derive a consistent goodness of fit test for the von Mises distribution. Monte Carlo simulation results are tabulated giving critical values of the test statistic for various sample sizes and values of the concentration parameter. A power analysis is presented for various alternative hypotheses, comparing this entropy statistic to two other competing goodness of fit statistics. The entropy statistic is shown to compare favorably and may be more attractive, especially considering its ease of computation.

Econometrics deals with the measurement of economic relationships. It may be considered as the integration of mathematics, economics and statistics for the purpose of providing estimation for the parameters of the economic relationships. Econometric methods designed to take an account of random disturbances or errors. The disturbances can be estimated by using different types of estimation procedures. In the present study an inferential aspects of linear regression model is considered, and it is fitted with an agricultural data, which was collected from a data mining. The fitted model was tested its goodness of fit by using Durbin-Watson test and estimated the error sum of squares. [ABSTRACT FROM AUTHOR]

We examine alternative generalized method of moments procedures for estimation of a stochastic autoregressive volatility model by Monte Carlo methods. We document the existence of a tradeoff between the number of moments, or information, included in estimation and the quality, or precision, of the objective function used for estimation. Furthermore, an approximation to the optimal weighting matrix is used to explore the impact of the weighting matrix for estimation, specification testing, and inference procedures. The results provide guidelines that help achieve desirable small-sample properties in settings characterized by strong conditional heteroscedasticity and correlation among the moments. [ABSTRACT FROM AUTHOR]