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Parsimonious Representation (parsimonious + representation)

Distribution by Scientific Domains
Mathematics and Statistics60%

Selected Abstracts

Modelling multivariate volatilities via conditionally uncorrelated components

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 4 2008
Jianqing Fan
Summary., We propose to model multivariate volatility processes on the basis of the newly defined conditionally uncorrelated components (CUCs). This model represents a parsimonious representation for matrix-valued processes. It is flexible in the sense that each CUC may be fitted separately with any appropriate univariate volatility model. Computationally it splits one high dimensional optimization problem into several lower dimensional subproblems. Consistency for the estimated CUCs has been established. A bootstrap method is proposed for testing the existence of CUCs. The methodology proposed is illustrated with both simulated and real data sets. [source]

Capturing Government Policy on the Left,Right Scale: Evidence from the United Kingdom, 1956,2006

POLITICAL STUDIES, Issue 4 2009
Armèn Hakhverdian
The left,right scheme is the most widely used and parsimonious representation of political competition. Yet, long time series of the left,right position of governments are sparse. Existing methods are of limited use in dynamic settings due to insufficient time points which hinders the proper specification of time-series regressions. This article analyses legislative speeches in order to construct an annual left,right policy variable for Britain from 1956 to 2006. Using a recently developed content analysis tool, known as Wordscores, it is shown that speeches yield valid and reliable estimates for the left,right position of British government policy. Long time series such as the one proposed in this article are vital to building dynamic macro-level models of politics. This measure is cross-validated with four independent sources: (1) it compares well to expert surveys; (2) a rightward trend is found in post-war British government policy; (3) Conservative governments are found to be more right wing in their policy outputs than Labour governments; (4) conventional accounts of British post-war politics support the pattern of government policy movement on the left,right scale. [source]

Falling and explosive, dormant, and rising markets via multiple-regime financial time series models

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 1 2010
Cathy W. S. Chen
Abstract A multiple-regime threshold nonlinear financial time series model, with a fat-tailed error distribution, is discussed and Bayesian estimation and inference are considered. Furthermore, approximate Bayesian posterior model comparison among competing models with different numbers of regimes is considered which is effectively a test for the number of required regimes. An adaptive Markov chain Monte Carlo (MCMC) sampling scheme is designed, while importance sampling is employed to estimate Bayesian residuals for model diagnostic testing. Our modeling framework provides a parsimonious representation of well-known stylized features of financial time series and facilitates statistical inference in the presence of high or explosive persistence and dynamic conditional volatility. We focus on the three-regime case where the main feature of the model is to capturing of mean and volatility asymmetries in financial markets, while allowing an explosive volatility regime. A simulation study highlights the properties of our MCMC estimators and the accuracy and favourable performance as a model selection tool, compared with a deviance criterion, of the posterior model probability approximation method. An empirical study of eight international oil and gas markets provides strong support for the three-regime model over its competitors, in most markets, in terms of model posterior probability and in showing three distinct regime behaviours: falling/explosive, dormant and rising markets. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Analysis of call centre arrival data using singular value decomposition

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2005
Haipeng Shen
Abstract We consider the general problem of analysing and modelling call centre arrival data. A method is described for analysing such data using singular value decomposition (SVD). We illustrate that the outcome from the SVD can be used for data visualization, detection of anomalies (outliers), and extraction of significant features from noisy data. The SVD can also be employed as a data reduction tool. Its application usually results in a parsimonious representation of the original data without losing much information. We describe how one can use the reduced data for some further, more formal statistical analysis. For example, a short-term forecasting model for call volumes is developed, which is multiplicative with a time series component that depends on day of the week. We report empirical results from applying the proposed method to some real data collected at a call centre of a large-scale U.S. financial organization. Some issues about forecasting call volumes are also discussed. Copyright © 2005 John Wiley & Sons, Ltd. [source]

A Closer Look at the Size and Value Premium in Emerging Markets: Evidence from the Kuala Lumpur Stock Exchange

ASIAN ECONOMIC JOURNAL, Issue 4 2002
Michael E. Drew
In this study of asset pricing in emerging markets, two questions are asked. First, Is there a size and value premium in markets outside the USA? Second, Can the multifactor model of Fama and French (1996) capture the cross,section of average stock returns for the Malaysian setting? The answers from this study suggest that size and value premium exist in markets outside the USA. We find that the two mimic portfolios, ,small minus big' (SMB) and ,high minus low' (HML), generate a return of 17.70% and 17.69% per annum, respectively, while the market generates a return of 1.92% per annum. Our findings suggest that the multi,factor model of Fama and French (1996) is a parsimonious representation of the risk factors for Malaysia, explaining returns in an economically meaningful manner. Our findings also reject the claim that the multifactor model results can be explained by the turn,of,the,year effect. [source]