... Carlo (MC) and Multilevel Monte Carlo (MLMC) methods applied to partial differential equations with random coefficients. These arise, for example, in groundwater flow modelling, where a commonly used model for the unknown parameter is a random field. We use the circulant embedding procedure for sampling...

... the results of Berry and Haile ( 2014 ) concerning the identification of structural demands, adapted to our setup. The third section contains the key novel result in this paper, the nonparametric (point-)identification of the distribution of random coefficients in the class of discrete choice demand model...

... transport neutron transport spatial heterogeneity random coefficients error estimate multilevel Monte Carlo EPSRC Centre for Doctoral Training in Statistical Applied Mathematics EP/L015684/1 The radiative transport equation (RTE) is a physically derived balance equation that models the angular...

..., Levinsohn, and Pakes, 1995 ) random-coefficients logit model to allow for data-driven selection among a high-dimensional set of control variables using the 'double-LASSO' procedure proposed by Belloni, Chernozhukov, and Hansen ( 2013 ). Economists often study consumers’ aggregate behaviour across markets...

... in Ethiopia that has no impact on yields, but that has nonetheless been widely adopted. Using three waves of panel data, we estimate a correlated random coefficient model and calculate the returns to improved chickpea in terms of yields, costs, and profits. We find that farmers’ comparative advantage does...

.../standard_publication_model ) Abstract Quasi–Monte Carlo (QMC) integration of output functionals of solutions of the diffusion problem with a log-normal random coefficient is considered. The random coefficient is assumed to be given by an exponential of a Gaussian random field that is represented by a series expansion...

...Matthew A Masten I then show how to extend the identification arguments to systems with more than two equations. I focus on a random coefficients generalization of the most widely used social interactions model—the linear-in-means model ( Manski, 1993 ). Specifically, I consider the model (2...

... experiments suggest that the LM‐type test can be substantially more powerful, in particular, when the number of time periods is small. Heterogeneous coefficients LM test Panel data model Random coefficients In classical panel data analysis, it is assumed that unobserved heterogeneity is captured...

... trade liberalization. Asymmetry Emerging markets Exchange rate pass-through Non-linear auto-regressive distributed lag model Random-coefficients panel data model A substantial literature has studied the transmission of exchange rate fluctuations into the prices of internationally traded goods...

...Andrew Chesher; Adam M. Rosen Endogeneity Incomplete models Instrumental variables Partial identification Random coefficients Random sets Set identification The model also builds on the IV models for binary outcomes considered in Chesher ( 2010 , 2013 ), where a single source of unobserved...

... documented in Neyman and Scott (1948) (see Arellano and Hahn, 2006 for a survey). Here, we show that all marginal effects, including the density of individual-specific effects, are identified for fixed-T in a model that is linear in random coefficients. Hence, our approach leads to full elimination...

... Multiple imputation Overidentified Random coefficients model email: [email protected] email: [email protected] 12 2005 02 2007 02 2007 © 2007 The International Biometric Society. 2007 This article is published and distributed under the terms of the Oxford University...

... there is a relation between the initial value and the slope. It also considers whether the initial value for an individual is a useful predictor of the slope for that individual. The problems are formulated in terms of regression models with random coefficients. The solutions are illustrated using data from...

...: (1) the unstructured covariance model, (2) unstructured antedependence models, (3) structured antedependence models, (4) autoregressive integrated moving average and similar models, and (5) random coefficients models. We evaluate the relative strengths and limitations of each model, emphasizing when...

...) estimation of the Hildreth–Houck random coefficients model. We show that the global ML estimator can be inconsistent. We develop an alternative LML (local ML) estimator and prove that it is consistent and asymptotically efficient for points in the interior of the parameters. Properties of the LML...

...N. T. Longford Count data Crossover trial Generalized linear models Maximum likelihood Random coefficients SUMMARY Count data are routinely assumed to have a Poisson distribution, especially when there are no straightforward diagnostic procedures for checking this assumption. We reanalyse two...

... under the terms of the Oxford University Press, Standard Journals Publication Model ( https://dbpia.nl.go.kr/journals/pages/open_access/funder_policies/chorus/standard_publication_model ) Summary Commonly applied diagnostic procedures in random-coefficient (multilevel) analysis are based...

... is discussed in the context of two-level (random-coefficient) models, and some remedies are proposed. Maximum likelihood Multilevel analysis Random coefficients Standard errors Address for Correspondence: Nicholas T. Longford, Department of Medical Statistics, De Montfort University, The Gateway...

...Daniel A. Lass; Conrado M. Gempesaw J430 Q120 The Supply of Off-Farm Labor: A Random Coefficients Approach Daniel A. Lass and Conrado M. Gempesaw II A joint model of off-farm labor decisions for farm operator and spouse is presented...

...Bruce L. Dixon; Calvin R. Berry; Dwi Susanto Q110 Q130 Q180 milk diversion and termination program effectiveness random coefficients Abstract A two-equation random coefficient regression model of commercial milk production is estimated using monthly observations from 1983 through June 1988...