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Yonas Alem and Jonathan Colmer
When agents are unable to smooth consumption and have distorted beliefs about the likelihood of future income realisations, uncertainty about future states of the world has a direct effect on individual welfare. However,...Read more...
1 August 2015
Jonas Kolsrud, Camille Landais, Peter Nilsson and Johannes Spinnewijn
This paper provides a simple, yet general framework to analyze the optimal time profile of benefits during the unemployment spell. We derive simple sufficient-statistics formulae capturing the insurance value and incenti...Read more...
3 July 2015
Myung Hwan Seo and Yongcheol Shin
This paper addresses an important and challenging issue as how best to model nonlinear asymmetric dynamics and cross-sectional heterogeneity, simultaneously, in the dynamic threshold panel data framework, in which both t...Read more...
1 September 2014
Sorawoot Srisuma and Oliver Linton
We propose a general two-step estimation method for the structural parameters of popular semiparametric Markovian discrete choice models that include a class of Markovian Games and allow for continuous observable state s...Read more...
Myunghwan Seo and Oliver Linton
We propose a smoothed least squares estimator of the parameters of a threshold regression model. Our model generalizes that considered in Hansen (2000) to allow the thresholding to depend on a linear index of observed re...Read more...
Zongwu Cai, Jianqin Fan and Qiwei Yao
Varying-coefficient linear models arise from multivariate nonparametric regression, nonlinear time series modelling and forecasting, functional data analysis, longitudinal data analysis, and others. It has been a common ...Read more...
Oliver Linton, Enno Mammen and N Nielsen
We derive the asymptotic distribution of a new backfitting procedure for estimating the closest additive approximation to a nonparametric regression function. The procedure employs a recent projection interpretation of p...Read more...
Andrew C Harvey and N.G. Shephard
A stochastic variance model may be estimated by quasi-maximum likelihood procedure by transforming to a linear state space form. The properties of observations corrected for heteroscedasticity can be derived. A model wit...Read more...
Siem Jan Koopman and N.G. Shephard
The score vector for a time series model which fits into the Gaussian state space form can be approximated by numerically differentiating the log-likelihood. If the parameter vector is of length p, this involves the runn...Read more...