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FORECASTING ACCURACY AND THE IMPORTANCE OF FINDING THE GLOBAL MAXIMA OF THE LIKELIHOOD FUNCTION

Title: FORECASTING ACCURACY AND THE IMPORTANCE OF FINDING THE GLOBAL MAXIMA OF THE LIKELIHOOD FUNCTION
Authors: Mahbuba Yeasmin; Maxwell L. King
Contributors: The Pennsylvania State University CiteSeerX Archives
Source: http://www.econometricsociety.org/meetings/am01/content/presented/papers/yeasmin.pdf.
Collection: CiteSeerX
Subject Terms: Key words; General linear model; Local maxima; MA errors; Marginal likelihood; Concentrated likelihood; Forecasting performance
Description: The method of maximum likelihood estimation is widely used in econometrics because it can be applied to a wide range of different parametric models and the resultant estimators have good asymptotic properties. Modern statistical computer packages allow one to carry out maximum likelihood estimation on reasonably complicated models with some degree of ease. An unfortunate drawback of the method, particularly when numerical methods are used to maximise the likelihood function, is that we can sometimes end up with a local maxima rather than the global maxima. This is a well-known problem in econometrics, although a survey of recent textbooks suggests that the consequences of accepting a local maxima instead of the global maxima are not well articulated. While the consequences for the estimation of parameters of interest might seem obvious, less obvious is what effect using parameter estimates from a local maximum could have on the small sample forecasting performance of a model. This paper considers this problem in the context of the linear regression model
Document Type: text
File Description: application/pdf
Language: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.596.1533; http://www.econometricsociety.org/meetings/am01/content/presented/papers/yeasmin.pdf
Availability: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.596.1533; http://www.econometricsociety.org/meetings/am01/content/presented/papers/yeasmin.pdf
Rights: Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Accession Number: edsbas.A836E7BB
Database: BASE