Locally asymptotically efficient estimation for parametric PINAR ( p ) models

2021 ◽  
Author(s):  
Mohamed Sadoun ◽  
Mohamed Bentarzi

1987 ◽  
Vol 19 (3) ◽  
pp. 395-402 ◽  
Author(s):  
J L Horowitz

The nested or sequential logit model is the only computationally tractable randomutility model that permits correlation among the random components of the utility functions of different alternatives. In this paper, two specification tests are described for nested logit models. One is a test of a maintained model against a nonnested alternative. This test can be used, among other purposes, to discriminate among models with different tree structures. It can be implemented by hand using the results of sequential estimation of the models under consideration. The other test consists of comparing the sequentially estimated parameter values with values produced by an asymptotically efficient estimation technique. This test does not require estimating an alternative to the maintained model.





2012 ◽  
Vol 56 (4) ◽  
pp. 768-784 ◽  
Author(s):  
Samantha Leorato ◽  
Franco Peracchi ◽  
Andrei V. Tanase


2014 ◽  
Vol 30 (5) ◽  
pp. 961-1020 ◽  
Author(s):  
Patrick Gagliardini ◽  
Christian Gourieroux

This paper deals with asymptotically efficient estimation in exchangeable nonlinear dynamic panel models with common unobservable factors. These models are relevant for applications to large portfolios of credits, corporate bonds, or life insurance contracts. For instance, the Asymptotic Risk Factor (ARF) model is recommended in the current regulation in Finance (Basel II and Basel III) and Insurance (Solvency II) for risk prediction and computation of the required capital. The specification accounts for both micro- and macrodynamics, induced by the lagged individual observations and the common stochastic factors, respectively. For large cross-sectional and time dimensionsnandT, we derive the efficiency bound and introduce computationally simple efficient estimators for both the micro- and macroparameters. The results are based on an asymptotic expansion of the log-likelihood function in powers of 1/n, and are linked to granularity theory. The results are illustrated with the stochastic migration model for credit risk analysis.



2005 ◽  
Vol 17 (7) ◽  
pp. 819-831 ◽  
Author(s):  
Chris A. J. Klaassen ◽  
Eun-Joo Lee ◽  
Frits H. Ruymgaart


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