The four-parameter generalized beta distribution of the second kind (GBII) has been proposed for modelling insurance losses with heavy-tailed features. The aim of this paper is to present a parametric composite GBII regression modelling by splicing two GBII distributions using mode matching...

The aim of this paper is to study the symmetry properties of solutions of bi-harmonic differential equations of the type Δ𝟐+||-| 𝜶 𝒖 = 𝟎 and Δ𝟐 𝒖 + 𝒇(𝒖) = 𝟎 𝒊𝒏 𝛀 ⊂ 𝑹𝟐 We employ the method of moving planes, which is based on the Maximum principles in...

This paper develops an efficient estimation procedure for time-varying parameter autoregressive models with stochastic volatility. Necessary restrictions are imposed on the time-varying autoregressive parameters, thus stability conditions are satisfied. We show that a conditional Gaussian...

In this paper, we propose an intersection-union test for multivariate forecast accuracy based on the combination of a sequence of univariate tests. The testing framework evaluates a global null hypothesis of equal predictive ability using any number of univariate forecast accuracy tests under...

We develop novel forecasting methods for panel data with heterogeneous parameters and examine them together with existing approaches. We conduct a systematic comparison of their predictive accuracy in settings with different cross-sectional (N) and time (T) dimensions and varying degrees of...

We study the tail probability of linear factor models generated from non-identically distributed components with regularly-varying tails, a large subclass of heavy-tailed distributions. An efficient sampling method for tail probability estimation for this class is introduced and theoretically...

This paper investigates the estimation and inference of spatial panel data models in which the regression coefficient vector is a trending function. We use time differences to eliminate the individual effects and employ GMM estimations for regression coefficients with both linear and quadratic...

The literature on heteroskedasticity and autocorrelation robust (HAR) inference is extensive but its usefulness relies on stationarity of the relevant process, say Vt, usually a function of the data and estimated model residuals. Yet, a large body of work shows widespread evidence of various...

As George Box [1976] put it, "all models are wrong but some are useful." This is especially true for derivative models, As sown during the 2008 financial crisis, most collaterized debt obligation (CDO) models failed to reflect the true risk of those structures. Therefore, one always faces the...

As part of the Measure phase of a Six Sigma (DMAIC) project, it is key to determine the sample size appropriate given the statistical precision needed on the estimation of the process capability (defined here as the expected proportion of defective units). Six Sigma practitioners are generally...