Adaptive Lasso for Vector Multiplicative Error Models

2018 ◽  
Author(s):  
Luca Cattivelli ◽  
Giampiero M. Gallo
Keyword(s):  
Adaptive Lasso ◽  
Error Models ◽  
Multiplicative Error ◽  
Multiplicative Error Models

Adaptive Lasso for vector Multiplicative Error Models

2019 ◽  
Vol 20 (2) ◽  
pp. 255-274 ◽  
Author(s):  
Luca Cattivelli ◽  
Giampiero M. Gallo
Keyword(s):  
Adaptive Lasso ◽  
Error Models ◽  
Multiplicative Error ◽  
Multiplicative Error Models

scholarly journals Vector Multiplicative Error Models: Representation and Inference

2006 ◽  
Author(s):  
Fabrizio Cipollini ◽  
Robert Engle ◽  
Giampiero Gallo
Keyword(s):  
Error Models ◽  
Multiplicative Error ◽  
Multiplicative Error Models

Rainfall uncertainty in hydrological modelling: An evaluation of multiplicative error models

2011 ◽  
Vol 400 (1-2) ◽  
pp. 83-94 ◽  
Author(s):  
Hilary McMillan ◽  
Bethanna Jackson ◽  
Martyn Clark ◽  
Dmitri Kavetski ◽  
Ross Woods
Keyword(s):  
Hydrological Modelling ◽  
Error Models ◽  
Multiplicative Error ◽  
Multiplicative Error Models

SPECIFICATION TESTS FOR MULTIPLICATIVE ERROR MODELS

2016 ◽  
Vol 33 (2) ◽  
pp. 413-438 ◽  
Author(s):  
Indeewara Perera ◽  
Mervyn J. Silvapulle
Keyword(s):  
Trading Volume ◽  
Bootstrap Method ◽  
Realized Volatility ◽  
Error Distribution ◽  
Parametric Models ◽  
Error Models ◽  
Conditional Mean ◽  
Multiplicative Error ◽  
Financial Transactions ◽  
Multiplicative Error Models

The family of multiplicative error models is important for studying non-negative variables such as realized volatility, trading volume, and duration between consecutive financial transactions. Methods are developed for testing the parametric specification of a multiplicative error model, which consists of separate parametric models for the conditional mean and the error distribution. The same method can also be used for testing the specification of the error distribution provided the conditional mean is correctly specified. A bootstrap method is proposed for computing the p-values of the tests and is shown to be consistent. The proposed tests have nontrivial asymptotic power against a class of O(n−1/2)-local alternatives. The tests performed well in a simulation study, and they are illustrated using a data example on realized volatility.

scholarly journals Doubly Multiplicative Error Models with Long– and Short–run Components

2020 ◽  
Author(s):  
Alessandra Amendola ◽  
Vincenzo Candila ◽  
Fabrizio Cipollini ◽  
Giampiero M. Gallo
Keyword(s):  
Error Models ◽  
Multiplicative Error ◽  
Short Run ◽  
Multiplicative Error Models

LACK-OF-FIT TESTING OF THE CONDITIONAL MEAN FUNCTION IN A CLASS OF MARKOV MULTIPLICATIVE ERROR MODELS

2012 ◽  
Vol 28 (6) ◽  
pp. 1283-1312 ◽  
Author(s):  
Hira L. Koul ◽  
Indeewara Perera ◽  
Mervyn J. Silvapulle
Keyword(s):  
Stock Exchange ◽  
Testing Procedure ◽  
General Purpose ◽  
Test Statistic ◽  
Error Models ◽  
Multiplicative Error ◽  
Lack Of Fit ◽  
Hourly Rainfall ◽  
Asymptotically Distribution Free ◽  
Multiplicative Error Models

AbstractThe family of multiplicative error models, introduced by Engle (2002, Journal of Applied Econometrics 17, 425–446), has attracted considerable attention in recent literature for modeling positive random variables, such as the duration between trades at a stock exchange, volume transactions, and squared log returns. Such models are also applicable to other positive variables such as waiting time in a queue, daily/hourly rainfall, and demand for electricity. This paper develops a new method for testing the lack-of-fit of a given parametric multiplicative error model having a Markov structure. The test statistic is of Kolmogorov–Smirnov type based on a particular martingale transformation of a marked empirical process. The test is asymptotically distribution free, is consistent against a large class of fixed alternatives, and has nontrivial asymptotic power against a class of nonparametric local alternatives converging to the null hypothesis at the rate of O (n–1/2). In a simulation study, the test performed better overall than the general purpose Ljung–Box Q-test, a Lagrange multiplier type test, and a generalized moment test. We illustrate the testing procedure by considering two data examples.

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