Regularised rank quasi-likelihood estimation for generalised additive models

Improved Quasi Maximum Likelihood Estimation for Stochastic Volatility Models

Jacquier Polson and Rossi J Business and Economic Statistics have pro posed a hierarchical model and Markov Chain Monte Carlo methodology for parameter estima tion and smoothing in a stochastic volatility model where the logarithm of the conditional vari ance follows an autoregressive process In sampling experiments their estimators perform par ticularly well relative to a quasi maximum likelih...

Quasi-Maximum Likelihood Estimation of Long-Memory Stochastic Volatility Models*

We analyze finite sample properties of the quasi-maximum likelihood estimators of longmemory stochastic volatility models. The estimates are done in the time domain using autoregressive and moving average in the state space representation. The results are compared with usual estimators of the long-memory parameter.

Quasi Maximum-Likelihood Estimation of Dynamic Panel Data Models

This paper establishes the almost sure convergence and asymptotic normality of levels and differenced quasi maximum-likelihood (QML) estimators of dynamic panel data models. The QML estimators are robust with respect to initial conditions, conditional and time-series heteroskedasticity, and misspecification of the log-likelihood. The paper also provides an ECME algorithm for calculating levels ...

Quasi Maximum Likelihood Estimation of Spatial Models with Heterogeneous Coefficients∗

This paper considers spatial autoregressive panel data models and extends their analysis to the case where the spatial coefficients differ across the spatial units. It derives conditions under which the spatial coefficients are identified and develops a quasi maximum likelihood (QML) estimation procedure. Under certain regularity conditions, it is shown that the QML estimators of individual spa...

Generalized Likelihood Ratio Tests for Additive Models