173,575 research outputs found
The Hodrick-Prescott (HP) Filter as a Bayesian Regression Model
No full textThe Hodrick-Prescott (HP) method is a popular smoothing method for economic time series to get a smooth or long-term component of stationary series like growth rates. We show that the HP smoother can be viewed as a Bayesian linear model with a strong prior using differencing matrices for the smoothness component. The HP smoothing approach requires a linear regression model with a Bayesian conjugate multi-normalgamma distribution. The Bayesian approach also allows to make predictions of the HP smoother on both ends of the time series. Furthermore, we show how Bayes tests can determine the order of smoothness in the HP smoothing model. The extended HP smoothing approach is demonstrated for the non-stationary (textbook) airline passenger time series. Thus, the Bayesian extension of the HP model defines a new class of model-based smoothers for (non-stationary) time series and spatial models.Hodrick-Prescott (HP) smoothers, model selection by marginal likelihoods, multi-normal-gamma distribution, Spatial sales growth data, Bayesian econometrics
The Extended Hodrick-Prescott (HP) Filter for Spatial Regression Smoothing
No full textThe Hodrick-Prescott (HP) method is a popular smoothing method for economic time series to get a longterm component of stationary series like growth rates. The new extended HP smoothing model is applied to data-sets with an underlying metric and requires a Bayesian linear regression model with a strong prior based on differencing matrices for the smoothness parameter and a weak prior for the regression part. We define a Bayesian spatial smoothing model with neighbors for each observation and we define a smoothness prior similar to the HP filter in time series. This opens a new approach to model-based smoothers for time series and spatial models based on MCMC. We apply it to the NUTS-2 regions of the European Union for regional GDP and GDP per capita, where the fixed effects are removed by an extended HP smoothing model.Hodrick-Prescott (HP) smoothers, smoothed square loss function, spatial smoothing, smoothness prior, Bayesian econometrics
HP Bulmer Information Technology Division Strategy and Policies 1997
No full textIn recent years, the field of information technology (IT) has been one of increasingly rapid change. Advances in hardware, communications, database management systems (DBMSs) and graphical user interfaces (GUIs) have joined together to revolutionise the ways in which IT service providers can develop and deliver systems. At the same time expectations have grown for the rapid delivery, by IT, of systems that are both easy to use and yet flexible enough to develop with the changing needs of the business.
This document provides the necessary technical framework within which the company can continue to apply IT to deliver business benefits. It includes a brief description of each significant area of technology and states the HP Bulmer strategy and policy for this area, including any recommended standards and tools to be used.
This is not a full IT/IS Strategy document, but rather an essential policy statement that underpins and helps shape a more business focused top level strategy and systems plan. The policies embody the principles and practices pursued in the day-to-day implementation and running of IT within the company and will be reviewed on an annual basis
An hp-version discontinuous Galerkin method for integro-differential equations of parabolic type
No full textWe study the numerical solution of a class of parabolic integro-differential equations with weakly singular kernels. We use an -version discontinuous Galerkin (DG) method for the discretization in time. We derive optimal -version error estimates and show that exponential rates of convergence can be achieved for solutions with singular (temporal) behavior near caused by the weakly singular kernel. Moreover, we prove that by using nonuniformly refined time steps, optimal algebraic convergence rates can be achieved for the -version DG method. We then combine the DG time-stepping method with a standard finite element discretization in space, and present an optimal error analysis of the resulting fully discrete scheme. Our theoretical results are numerically validated in a series of test problems
FLOATING HP OPTIMIZATION WITH MACHINE LEARNING MODELS
No full textopenFLOATING HP OPTIMIZATION WITH MACHINE LEARNING MODEL
MCMC Estimation of Extended Hodrick-Prescott (HP) Filtering Models
No full textThe Hodrick-Prescott (HP) method was originally developed to smooth time series, i.e. to get a smooth (long-term) component. We show that the HP smoother can be viewed as a Bayesian linear model with a strong prior for the smoothness component. Extending this Bayesian approach in a linear model set-up is possible by a conjugate and a non-conjugate model using MCMC. The Bayesian HP smoothing model is also extended to a spatial smoothing model. We have to define spatial neighbors for each observation and we can use in a similar way a smoothness prior as for the HP filter in time series. The new smoothing approaches are applied to the (textbook) airline passenger data for time series and to the problem of smoothing spatial regional data. This new approach can be used for a new class of model-based smoothers for time series and spatial models.Hodrick-Prescott (HP) smoothers, Spatial econometrics, MCMC estimation, Airline passenger time series, Spatial smoothing of regional data, NUTS: nomenclature of territorial units for statistics
Método sem malha hp-clouds na análise de placas Reissner-Mindlin /
No full textDissertação (Mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico.O conteúdo deste trabalho trata da aplicação do método sem malha hp-Clouds ou simplesmente método de nuvens (C. A. Duarte e J. T. Oden [8]) à solução de problemas de placas de Reissner-Mindlin
HP-SAP cross-spectral indexes in PG and CG.
No full textHP-SAP cross-spectral indexes in PG and CG.</p
The Use of the HP-filter in Constructing Real Estate Cycle Indicators
No full textThe growing body of research focusing on real estate as an individual asset class puts the real estate cycle in the very center of strategic investment decisions and implications thereof. This article investigates if the non-structural definition of the cycle as defined by the Hodrick-Prescott (HP) filter can be used to construct indicators of the Swedish real estate cycle. The methodology of the HP-filter, which is to separate a time-series into a trend component and a growth component, is often used in analysis of aggregate economic growth (i.e., GDP). The article also evaluates the indicative characteristics of the indicator.
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