A framework for the comparison of maximum pseudo-likelihood and maximum likelihood estimation of exponential family random graph models

The statistical modeling of social network data is difficult due to the complex dependence structure of the tie variables. Statistical exponential families of distributions provide a flexible way to model such dependence. They enable the statistical characteristics of the network to be encapsulated within an exponential family random graph (ERG) model. For a long time, however, likelihood-based estimation was only feasible for ERG models assuming dyad independence. For more realistic and complex models inference has been based on the pseudo-likelihood. Recent advances in computational methods have made likelihood-based inference practical, and comparison of the different estimators possible.     

Family farmers and major retail chains in the Brazilian organic sector: Assessing new development pathways. A case study in a peri-urban district of São Paulo

The expansion of the organic sector in Brazil is seen as a leverage for the social emancipation of the small family farmers. Next to the traditional alternatives circuits of organic food and farming, new powerful capitalistic actors, such as supermarket chains, are rapidly entering the Brazilian organic arena. Can family farming benefit from the development of these “conventional” commercialisation circuits in the organic sector? Research undertaken in 2007, in a green belt rural community of São Paulo, shows how family farmers may have benefited from the implication of large retail chains in the organic sector and how an economically and ecologically outstanding agriculture may arise from these circumstances. However, we highlight the crucial role played by social regulation: only strong solidarity between farmers and the implication of technicians, militants and researchers in the process made it possible to counter the negative effects of the liberal logic governing the development of organic farming via the major retailers. Still, as tougher competition is expected on the regional organic market, the development of short supply chains involving “committed” consumers and the broader integration of the local farmers in networks of organic militancy appear crucial. It would guarantee a continuous enhancement of the local human and social capital, reinforce an emerging process of internal conversion and allow for a stronger social regulation of the future local development pattern.     

On the use of quasi-likelihood for estimation in hidden-Markov random fields

This paper illustrates the use of quasi-likelihood methods of inference for hidden Markov random fields. These are simple to use and can be employed under circumstances where only the model form and its covariance structure are specified. In particular they can be used to derive the same estimating equations as the E-M algorithm or change of measure methods, which make full distributional assumptions.     

Role of stylized features in constructing better estimators

This article discusses the role played by stylized features of financial time series in constructing better estimators for the model parameters. We study in detail one such estimator for the transition probabilities of a simple regime switching model. The estimator is based on the squared autocovariances of the time series, which has been discussed in several empirical studies of economic and financial time series. The effectiveness of this estimator in improving the estimation accuracy is investigated, using both finite sample and asymptotic computations. We also report simulation results to confirm our findings and to extend our conclusions over a bigger region of the parameter space.     

Identification-robust moment-based tests for Markov switching in autoregressive models

ABSTRACT

This paper develops tests of the null hypothesis of linearity in the context of autoregressive models with Markov-switching means and variances. These tests are robust to the identification failures that plague conventional likelihood-based inference methods. The approach exploits the moments of normal mixtures implied by the regime-switching process and uses Monte Carlo test techniques to deal with the presence of an autoregressive component in the model specification. The proposed tests have very respectable power in comparison with the optimal tests for Markov-switching parameters of Carrasco et al. (2014 Carrasco, M., Hu, L., Ploberger, W. (2014). Optimal test for Markov switching parameters. Econometrica 82(2):765–784.[Crossref], [Web of Science ®] , [Google Scholar]), and they are also quite attractive owing to their computational simplicity. The new tests are illustrated with an empirical application to an autoregressive model of USA output growth.     

Adaptive multiple importance sampling for Gaussian processes

In applications of Gaussian processes (GPs) where quantification of uncertainty is a strict requirement, it is necessary to accurately characterize the posterior distribution over Gaussian process covariance parameters. This is normally done by means of standard Markov chain Monte Carlo (MCMC) algorithms, which require repeated expensive calculations involving the marginal likelihood. Motivated by the desire to avoid the inefficiencies of MCMC algorithms rejecting a considerable amount of expensive proposals, this paper develops an alternative inference framework based on adaptive multiple importance sampling (AMIS). In particular, this paper studies the application of AMIS for GPs in the case of a Gaussian likelihood, and proposes a novel pseudo-marginal-based AMIS algorithm for non-Gaussian likelihoods, where the marginal likelihood is unbiasedly estimated. The results suggest that the proposed framework outperforms MCMC-based inference of covariance parameters in a wide range of scenarios.