Model Selection in a System of Simultaneous Equations Model

Keerativibool, Warangkhana; Jitthavech, Jirawan; Lorchirachoonkul, Vichit

doi:10.1080/03610920903353717

Cited by 2 publications

(1 citation statement)

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“…When several estimate models are available, it is necessary to have a selection parameter criteria. There are a lot of useful information parameter criteria for comparing SEMs, such as Akaike Information Criteria (AIC) [20,21], its corrected version (AICc) [22], Schwarz Information Criteria (SIC) [23], Bayesian Information Criteria (BIC) [24], Hannan and Quinn (HQ) [25], and Model Selection Criterion based on Kullback-Leibler's Symmetric Divergence [26].…”

Section: Introductionmentioning

confidence: 99%

A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria

et al. 2021

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Simultaneous Equations Models (SEM) is a statistical technique widely used in economic science to model the simultaneity relationship between variables. In the past years, this technique has also been used in other fields such as psychology or medicine. Thus, the development of new estimating methods is an important line of research. In fact, if we want to apply the SEM to medical problems with the main goal being to obtain the best approximation between the parameters of model and their estimations. This paper shows a computational study between different methods for estimating simultaneous equations models as well as a new method which allows the estimation of those parameters based on the optimization of the Bayesian Method of Moments and minimizing the Akaike Information Criteria. In addition, an entropy measure has been calculated as a parameter criteria to compare the estimation methods studied. The comparison between those methods is performed through an experimental study using randomly generated models. The experimental study compares the estimations obtained by the different methods as well as the efficiency when comparing solutions by Akaike Information Criteria and Entropy Measure. The study shows that the proposed estimation method offered better approximations and the entropy measured results more efficiently than the rest.

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Section: Introductionmentioning

confidence: 99%

A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria

et al. 2021

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What drives interbank loans? Evidence from Canada

Bulusu

Guérin

2019

Journal of Banking & Finance

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We identify the drivers of unsecured and collateralized loan volumes, rates and haircuts in Canada using the Bayesian model averaging approach to deal with model uncertainty. Our results suggest that the key friction driving behaviour in this market is the collateral reallocation cost faced by borrowers. Borrowers therefore adjust unsecured lending in response to changes in short-term cash needs, and use repos to finance persistent liquidity demand. We also find that lenders set rates and haircuts taking into account counterparty credit risk and collateral market price volatility.

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Model Selection in a System of Simultaneous Equations Model

Cited by 2 publications

References 10 publications

A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria

A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria

What drives interbank loans? Evidence from Canada

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