Accounting for Observed Cycle Features with a Range of Statistical Models

This chapter looks at observed features of the cycle in a variety of time series. It sets out these features for the United States and a number of other countries, and then asks whether these features can be replicated by the use of a particular statistical model—a linear autoregression. For such linear models it is possible to broadly account for the observed features using moments of the series for growth rates, and this strategy is employed in the chapter. It then uses a particular nonlinear statistical model to see if it can match all the features, and further looks at two other nonlinear models first dealt with in Chapter 4. The chapter concludes with an examination of whether the binary indicators summarizing the recurrent states can be used in the context of standard multivariate methods such as vector autoregressions. This turns out not to be straightforward owing to the nature of the binary variables.

Estimasi Nonlinear Least Trimmed Squares (NLTS) pada Model Regresi Nonlinier yang Dikenai Outlier

<p>Constant Elasticity of Substitution (CES) production function is the intrinsic nonlinear regression models that are often used to estimate the data in an industry. Intrinsic nonlinear regression model is a kind<br />of nonlinear regression that can not be linearized, so as to estimate the beta parameters nonlinear statistical model used was Nonlinear Least Squares (NLS) using a first order taylor series approach used in the Gauss<br />Newton iteration. One of the problems often encountered in the analysis of data is an outlier, the presence of outliers in the data analysis greatly influence the results of the analysis so it becomes less valid and the estimation<br />become biased. One method that is resistant to outliers regression is a method of Nonlinear Least Trimmed Squares. This research aims to determine the characteristics of parameter CES production function which<br />contains outlier. The result shows that parameter of the production function CES which contains outliers are bias, inconsistent. So the CES production function which does not contain outliers better than the are contains<br />outliers.</p>

Robust Tracking Time-Varying High-Frequency CW Telegraph Signal

Continuous wave (CW) telegraph is a crucial communication means for high-frequency tactical communication. But there is serious frequency deviation and impulsive noise in High-frequency channel, thus the conventional tracking method based on Gaussian noise assumption may lose the track of time-varying CW signal. A new robust kalman filter-based tracker is proposed in this paper to extract the time-varying CW signal in presence of impulsive interference, which uses a nonlinear statistical model. Simulation studies show this method can dynamically track nonstationary CW signal and effectively suppress burst impulse noise.