most citedNonparametric Test for Volatility in Clustered Multiple Time Series

2 citations · 3 across the 8 of their papers we have counts for

collaborators
Showing stat.MEShow all

6 papers · 1 filter

stat.ME2026

Mixed Frequency Stochastic Frontier Model: with application to the linkage of weather extremes and firm efficiency

Erniel B. Barrios, Nur Syazwani Mazlan, Lim Foo Weng +3

A strategy for incorporating higher-frequency determinants of inefficiency into the stochastic frontier models with production function indicators compiled at lower frequencies is…

stat.ME2021

Testing for the Presence of Structural Change and Spatial Heterogeneity

Ruby Anne E. Lemence, Erniel B. Barrios

In a spatial-temporal model, structural change and/or spatial heterogeneity can easily affect estimation of parameters. Following the spatial-temporal model in [1], we develop a no…

stat.ME2021★ 1 cited

Semiparametric Volatility Model with Varying Frequencies

Jetrei Benedick R. Benito, Joseph Ryan G. Lansangan, Erniel B. Barrios

In extracting time series data from various sources, it is inevitable to compile variables measured at varying frequencies as this is often dependent on the source. Modeling from t…

stat.ME2021

Semiparametric Spatiotemporal Model with Mixed Frequencies

Vladimir A. Malabanan, Joseph Ryan G. Lansangan, Erniel B. Barrios

In modelling time series data coming from different sources, frequencies can easily vary since some variable can be measured at higher frequencies, others, at lower frequencies. Gi…

stat.ME2021

Statistical Inference in a Spatial-Temporal Stochastic Frontier Model

Erniel B. Barrios, John D. Eustaquio, Rouselle F. Lavado

The stochastic frontier model with heterogeneous technical efficiency explained by exoge-nous variables is augmented with a spatial-temporal component, a generalization relaxing th…

stat.ME2021

Estimation of Poisson Autoregressive Model for Multiple Time Series

Paolo Victor T. Redondo, Joseph Ryan G. Lansangan, Erniel B. Barrios

A Poisson autoregressive (PAR) model accounting for discreteness and autocorrelation of count time series data is typically estimated in the state-space modelling framework through…