Long memory in economics discussion and comments

Joann Jasiak

Displaying similar documents to “Long memory in economics discussion and comments”

Signals and revisions in economic time series: a case study.

Agustín Maravall, David A. Pierce (1984)

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The paper estimates how much short-run monetary control may be affected by data noise and revisions, such as the ones implied by seasonal adjustment. The effects of the different types of data error are illustrated, and results on their empirical relevance and analytical properties are presented. The paper can be seen as an exercise that combines some elements of econometric, time series and economic analysis to answer a real world problem.

Instability and long memory in conditional variances

Ignacio Mauleón (1997)

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Application of SVR with improved ant colony optimization algorithms in exchange rate forecasting

Wei-Mou Hung, Wei-Chiang Hong (2009)

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The dynamic linear model: An application to flood forecasting.

M. A. GÓMEZ-VILLEGAS and J. TEJADA E. G. SÁNCHEZ-MANZANO (1999)

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Combining forecasts using the least trimmed squares

Jan Ámos Víšek (2001)

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Employing recently derived asymptotic representation of the least trimmed squares estimator, the combinations of the forecasts with constraints are studied. Under assumption of unbiasedness of individual forecasts it is shown that the combination without intercept and with constraint imposed on the estimate of regression coefficients that they sum to one, is better than others. A numerical example is included to support theoretical conclusions.

A simplified treatment of the restricted analysis of a slightly disproportionate factorial experiment.

José M. Prieto, José M. Caridad (1988)

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This paper considers a procedure to obtain effect estimators in the least squares analysis of a slightly disproportionate factorial design when a sample survey is made of the results of an extensive experiment. Explicit formulae have been found for the restricted estimators and their variances, when the constraints normally imposed upon a proportional model are used. In addition, an approximate analysis of the original model is used to perform that estimation, and an approximate analysis...

By nonparametric methods

Théo Gasser (1989)

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Learning from imprecise examples with GA-P algorithms.

Luciano Sánchez, Inés Couso (1998)

Mathware and Soft Computing

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GA-P algorithms combine genetic programming and genetic algorithms to solve symbolic regression problems. In this work, we will learn a model by means of an interval GA-P procedure which can use precise or imprecise examples. This method provides us with an analytic expression that shows the dependence between input and output variables, using interval arithmetic. The method also provides us with interval estimations of the parameters on which this expression depends. The...