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Testing for Common Trends
 Journal of the American Statistical Association
, 1988
"... Cointegrated multiple time series share at least one common trend. Two tests are developed for the number of common stochastic trends (i.e., for the order of cointegration) in a multiple time series with and without drift. Both tests involve the roots of the ordinary least squares coefficient matrix ..."
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Cited by 464 (7 self)
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Cointegrated multiple time series share at least one common trend. Two tests are developed for the number of common stochastic trends (i.e., for the order of cointegration) in a multiple time series with and without drift. Both tests involve the roots of the ordinary least squares coefficient
The Common–Trend and Transitory
, 2005
"... This study explores the sources of real exchange rate fluctuations under the current float. Using a cointegration model of the real exchange rate, the innovations are decomposed into transitory and common–trend components. Both transitory and common–trend innovations are found to explain an appreci ..."
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This study explores the sources of real exchange rate fluctuations under the current float. Using a cointegration model of the real exchange rate, the innovations are decomposed into transitory and common–trend components. Both transitory and common–trend innovations are found to explain
The Case of Common Trends
, 2013
"... We analyze the forecasting performance of small mixed frequency factor models when the observed variables share stochastic trends. The indicators are observed at various frequencies and are tied together by cointegration so that valuable high frequency information is passed to low frequency series t ..."
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through the common factors. Differencing the data breaks the cointegrating link among the series and some of the signal leaks out to the idiosyncratic components, which do not contribute to the transfer of information among indicators. We find that allowing for common trends improves forecasting
Common Trends and Common Cycles
, 2009
"... An electronic version of the paper may be downloaded • from the SSRN website: www.SSRN.com • from the RePEc website: www.RePEc.org • from the CESifo website: Twww.CESifogroup.org/wp T ..."
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An electronic version of the paper may be downloaded • from the SSRN website: www.SSRN.com • from the RePEc website: www.RePEc.org • from the CESifo website: Twww.CESifogroup.org/wp T
Behavior recognition via sparse spatiotemporal features
 In VSPETS
, 2005
"... A common trend in object recognition is to detect and leverage the use of sparse, informative feature points. The use of such features makes the problem more manageable while providing increased robustness to noise and pose variation. In this work we develop an extension of these ideas to the spatio ..."
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Cited by 717 (4 self)
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A common trend in object recognition is to detect and leverage the use of sparse, informative feature points. The use of such features makes the problem more manageable while providing increased robustness to noise and pose variation. In this work we develop an extension of these ideas
Common trends, Cointegration and Competitive
, 2008
"... This article describes a characterisation of competitive market behaviour using the concepts of cointegration analysis. It requires all (n)
rms to set prices to follow a single stochastic trend (equivalently the vector of n prices should have cointegrating rank n 1). This implies that, in the lo ..."
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This article describes a characterisation of competitive market behaviour using the concepts of cointegration analysis. It requires all (n)
rms to set prices to follow a single stochastic trend (equivalently the vector of n prices should have cointegrating rank n 1). This implies that
COINTEGRATION AND COMMON TRENDS
, 2007
"... In this note we discuss some important issues in regression models for nonstationary time series. It is illustrated how linear combinations of nonstationary timeseries are nonstationary in general, and cointegration is defined as the special case where a linear combination is stationary. We emph ..."
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In this note we discuss some important issues in regression models for nonstationary time series. It is illustrated how linear combinations of nonstationary timeseries are nonstationary in general, and cointegration is defined as the special case where a linear combination is stationary. We emphasize that relations between nonstationary variables can only be interpreted as defining an equilibrium if the variables cointegrate, and we discuss errorcorrection as the force that sustain the equilibrium relation. We then present some singleequation tools for cointegration analysis, e.g. the socalled EngleGranger twostep procedure and cointegration analysis based on unrestricted ADL models. We show how to estimate the cointegrating parameters and how to test the hypothesis of nocointegration. Towards the end of the note we discuss some limitations of the singleequation approach.
Constraint Logic Programming: A Survey
"... Constraint Logic Programming (CLP) is a merger of two declarative paradigms: constraint solving and logic programming. Although a relatively new field, CLP has progressed in several quite different directions. In particular, the early fundamental concepts have been adapted to better serve in differe ..."
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Cited by 869 (25 self)
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in different areas of applications. In this survey of CLP, a primary goal is to give a systematic description of the major trends in terms of common fundamental concepts. The three main parts cover the theory, implementation issues, and programming for applications.
From Data Mining to Knowledge Discovery in Databases.
 AI Magazine,
, 1996
"... ■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in database ..."
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Cited by 538 (0 self)
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in KDD systems. Why Do We Need KDD? The traditional method of turning data into knowledge relies on manual analysis and interpretation. For example, in the healthcare industry, it is common for specialists to periodically analyze current trends and changes in healthcare data, say, on a quarterly basis
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