Asymptotically efficient estimation of prior probabilities in multiclass finite mixtures

1991 ◽  
Vol 37 (3) ◽  
pp. 482-489 ◽  
Author(s):  
G.R. Dattatreya ◽  
L.N. Kanal
Keyword(s):  
Efficient Estimation ◽  
Finite Mixtures ◽  
Prior Probabilities ◽  
Asymptotically Efficient

Specification Tests for Nested Logit Models

1987 ◽  
Vol 19 (3) ◽  
pp. 395-402 ◽  
Author(s):  
J L Horowitz
Keyword(s):  
Sequential Estimation ◽  
Efficient Estimation ◽  
Nested Logit ◽  
Logit Models ◽  
Specification Tests ◽  
Tree Structures ◽  
Nested Logit Models ◽  
Random Components ◽  
Asymptotically Efficient ◽  
Parameter Values

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.

scholarly journals Asymptotically efficient estimation of the conditional expected shortfall

2012 ◽  
Vol 56 (4) ◽  
pp. 768-784 ◽  
Author(s):  
Samantha Leorato ◽  
Franco Peracchi ◽  
Andrei V. Tanase
Keyword(s):  
Expected Shortfall ◽  
Efficient Estimation ◽  
Asymptotically Efficient

scholarly journals EFFICIENCY IN LARGE DYNAMIC PANEL MODELS WITH COMMON FACTORS

2014 ◽  
Vol 30 (5) ◽  
pp. 961-1020 ◽  
Author(s):  
Patrick Gagliardini ◽  
Christian Gourieroux
Keyword(s):  
Likelihood Function ◽  
Solvency Ii ◽  
Efficient Estimation ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Panel Models ◽  
Insurance Contracts ◽  
Credit Risk Analysis ◽  
Asymptotically Efficient ◽  
Dynamic Panel Models

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.

scholarly journals Asymptotically efficient estimation of linear functionals in inverse regression models

2005 ◽  
Vol 17 (7) ◽  
pp. 819-831 ◽  
Author(s):  
Chris A. J. Klaassen ◽  
Eun-Joo Lee ◽  
Frits H. Ruymgaart
Keyword(s):  
Regression Models ◽  
Efficient Estimation ◽  
Inverse Regression ◽  
Linear Functionals ◽  
Asymptotically Efficient

Asymptotically Efficient Estimation of Cointegration Regressions

1991 ◽  
Vol 7 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Pentti Saikkonen
Keyword(s):  
Least Squares ◽  
Optimality Theory ◽  
Asymptotic Efficiency ◽  
Asymptotic Optimality ◽  
Ordinary Least Squares ◽  
Minimum Variance ◽  
Limiting Distribution ◽  
Efficient Estimation ◽  
Short Run ◽  
Asymptotically Efficient

An asymptotic optimality theory for the estimation of cointegration regressions is developed in this paper. The theory applies to a reasonably wide class of estimators without making any specific assumptions about the probability distribution or short-run dynamics of the data-generating process. Due to the nonstandard nature of the estimation problem, the conventional minimum variance criterion does not provide a convenient measure of asymptotic efficiency. An alternative criterion, based on the concentration or peakedness of the limiting distribution of an estimator, is therefore adopted. The limiting distribution of estimators with maximum asymptotic efficiency is characterized in the paper and used to discuss the optimality of some known estimators. A new asymptotically efficient estimator is also introduced. This estimator is obtained from the ordinary least-squares estimator by a time domain correction which is nonparametric in the sense that no assumption of a finite parameter model is required. The estimator can be computed with least squares without any initial estimations.

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