1,720,984 research outputs found
On the reduced-rank model with leading index
No full text[[abstract]]The index model and the reduced-rank model are two useful models which adopt different views to explore a vector autoregressive (VAR) model structure. However, we expect that there will be more information when the two models are implemented simultaneously. This paper combines these two models to propose a VAR model with constraints. The model extracts the leading index and explores the reduced-rank structures simultaneously. In this study, theoretical results are presented, which include a necessary and sufficient condition for a transformation for parsimonious parameterizations and model identification, the maximum likelihood estimations and the likelihood ratio test. The empirical results for the analysis of famous US hog data using the proposed model display the unification of several results in the literature, revealing that the novel model is reasonable and works quite well.[[note]]SC
On the Pena-Box model
No full text[[abstract]]Pena and Box [Journal of Americal Statistical Association (1987) Vol. 82, PP. 836-843] proposed a factor model which aimed to explore the possibility of using lower-dimensional series to represent or explain an observed higher-dimensional multiple time series. However, there were no statistics with distribution results with which to build the model. In this paper, we derive a statistical procedure to build the model for stationary and first-order non-stationary series. The main idea, conducted by the canonical correlation analysis between present series and non-present series, is an extension of the concept of the scalar component model proposed by Tiao and Tsay [Journal of the Royal Statistical Society B (1989) Vol. 51, pp. 157-213]. Finally, simulation studies and reanalysis of two real data sets are illustrated.[[note]]SC
Likelihood Function and Canonical Correlation Analysis of the Pena-Box Model
No full text[[abstract]]The Pena-Box model is a type of dynamic factor model whose factors try to capture the time-effect movements of a multiple time series. The Pena-Box model can be expressed as a vector autoregressive (VAR) model with constraints. This article derives the maximum likelihood estimates and the likelihood ratio test of the VAR model for Gaussian processes. Then a test statistic constructed by canonical correlation coefficients is presented and adjusted for conditional heteroscedasticity. Simulations confirm the validity of adjustments for conditional heteroscedasticity, and show that the proposed statistics perform better than the statistics used in the existing literature.[[note]]SC
Identifying the time-effect factors of multiple time series
No full text[[abstract]]The Pena-Box model is considered for finding the time-effect factors of a multiple time series. This paper first establishes the connection between the Pena-Box model and the vector ARMA model. According to the Pena-Box model, some series can be ignored while modelling the vector ARMA model. A consistent estimator is then proposed to identify the model for nonlinear and nonstationary time series. Finally, the finite-sample behaviour of the estimator is illustrated via simulations. Copyright (c) 2005 John Wiley & Sons, Ltd.[[note]]SSC
A generalized time-effect factor model and its application: recovering trend of temperature by pollen data
No full text[[abstract]]In order to extract the time-effect factors of multiple time series, a generalized time-effect factor model (GTFM) and a statistic for identifying the model are proposed. This model is a general form of the Pena-Box model. Based on the model, a pollen data set from Taiwan is analyzed. A factor, named temperature, is extracted. Its trend is then used to recover the weather of Taiwan over the past 5000 years. Copyright (C) 2007 John Wiley & Sons, Ltd.[[note]]SC
Forecasting Volatility with Many Predictors
No full text[[abstract]]This study investigates the forecasting performance of the GARCH(1,1) model by adding an effective covariate. Based on the assumption that many volatility predictors are available to help forecast the volatility of a target variable, this study shows how to construct a covariate from these predictors and plug it into the GARCH(1,1) model. This study presents a method of building a covariate such that the covariate contains the maximum possible amount of predictor information of the predictors for forecasting volatility. The loading of the covariate constructed by the proposed method is simply the eigenvector of a matrix. The proposed method enjoys the advantages of easy implementation and interpretation. Simulations and empirical analysis verify that the proposed method performs better than other methods for forecasting the volatility, and the results are quite robust to model misspecification. Specifically, the proposed method reduces the mean square error of the GARCH(1,1) model by 30% for forecasting the volatility of S&P 500 Index. The proposed method is also useful in improving the volatility forecasting of several GARCH-family models and for forecasting the value-at-risk. Copyright (c) 2013 John Wiley & Sons, Ltd.[[note]]SSC
EVALUATING R&D PROJECTS WITH HEDGING BEHAVIOR
No full text[[abstract]]Many scholars suggest that, in general, successful R&D involves the systematic reduction of unique risk. Consequently, firms should adopt diversification or hedging behavior to reduce risk when investing in R&D projects. These projects can be evaluated through a real option method while considering the hedging behavior of firms. However, conventional real option methods may over- or under-state a project's value because they are apt to be influenced by the R&D firm's subjective expectations of the future market or technological prospect. The method proposed in this paper, which incorporates the hedging behavior of firms, would provide a more reasonable assessment of R&D projects, and would not be influenced by the arbitrary judgment of project evaluators. Additionally the results imply that the effective management of investment diversification can not old reduce the unique risk faced by firms, but also enhance the value of these R&D projects.[[note]]SC
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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