Efficient estimators for alternating quasi-likelihood models

نویسندگان

  • Ursula U. Müller
  • Anton Schick
  • Wolfgang Wefelmeyer
چکیده

We consider time series described by Markov chains that alternate periodically between different transition distributions, with conditional constraints involving unknown parameters. We obtain variance bounds and characterize efficient estimators for these parameters. Efficient estimators can be obtained as solutions of randomly weighted martingale estimating equations. Our model includes alternating heteroskedastic nonlinear autoregressive models whose innovations are martingale increments, in other words, alternating quasi-likelihood models. We consider in particular submodels of these in which the transition distributions do not alternate except for the conditional means and variances, and show that this information leads to better estimators for the parameters.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Modified Maximum Likelihood Estimation in First-Order Autoregressive Moving Average Models with some Non-Normal Residuals

When modeling time series data using autoregressive-moving average processes, it is a common practice to presume that the residuals are normally distributed. However, sometimes we encounter non-normal residuals and asymmetry of data marginal distribution. Despite widespread use of pure autoregressive processes for modeling non-normal time series, the autoregressive-moving average models have le...

متن کامل

ASYMPTOTIC DISTRIBUTIONS OF QUASI-MAXIMUM LIKELIHOOD ESTIMATORS FOR SPATIAL AUTOREGRESSIVE MODELS BY LUNG-FEI LEE This paper investigates asymptotic properties of the maximim likelihood estimator and the quasi-maximum likelihood estimator for the spatial autore-

This paper investigates asymptotic properties of the maximim likelihood estimator and the quasi-maximum likelihood estimator for the spatial autoregressive model. The rates of convergence of those estimators may depend on some general features of the spatial weights matrix of the model. It is important to make the distinction with different spatial scenarios. Under the scenario that each unit w...

متن کامل

Local Polynomial Kernel Regression for Generalized Linear Models and Quasi-Likelihood Functions

Generalized linear models (Wedderburn and NeIder 1972, McCullagh and NeIder 1988) were introduced as a means of extending the techniques of ordinary parametric regression to several commonly-used regression models arising from non-normal likelihoods. Typically these models have a variance that depends on the mean function. However, in many cases the likelihood is unknown, but the relationship b...

متن کامل

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 ...

متن کامل

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.

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2013