Journal ArticleDOI
Asymptotic estimation and hypothesis testing results for vector linear time series models
TLDR
For a general vector linear time series model, this article proved the strong consistency and asymptotic normality of parameter estimates obtained by maximizing a particular time domain approximation to a Gaussian likelihood, although they do not assume that the observations are necessarily normally distributed.Abstract:
For a general vector linear time series model we prove the strong consistency and asymptotic normality of parameter estimates obtained by maximizing a particular time domain approximation to a Gaussian likelihood, although we do not assume that the observations are necessarily normally distributed. To solve the normal equations we set up a constrained Gauss-Newton iteration and obtain the properties of the iterates when the sample size is large. In particular we show that the iterates are efficient when the iteration begins with a VN- consistent estimator. We obtain similar results to the above for a frequency domain approximation to a Gaussian likelihood. We use the asymptotic estimation theory to obtain the asymptotic distribution of several familiar test statistics for testing nonlinear equality constraints.read more
Citations
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Journal ArticleDOI
Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models
Journal ArticleDOI
The relative information content of accruals and cash flows: combined evidence at the earnings announcement and annual report release date
TL;DR: There has been some controversy about the usefulness of the accrual components of earnings for assessing share values as discussed by the authors, however, there is general agreement that share values are related to future cash flows.
Posted Content
Unit Roots in Real Gnp: Do We Know, and Do We Care?
TL;DR: In this article, the authors pose the question: How much should an innovation to real GMP affect the optimal forecast of real GMPs into the infinite future? If the answer is zero, then real GMMP is trend stationary.
Book ChapterDOI
Chapter 20 Continuous time stochastic models and issues of aggregation over time
TL;DR: In this article, statistical methods that are applicable to a class of continuous time stochastic models are described and the theoretical foundations of these methods are discussed, and the structural parameters are estimated from a sample comprising a sequence of discrete observations of the variables.
References
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Book
Principles of mathematical analysis
TL;DR: The real and complex number system as discussed by the authors is a real number system where the real number is defined by a real function and the complex number is represented by a complex field of functions.
Journal ArticleDOI
Testing for Serial Correlation in Least-Squares Regression When Some of the Regressors are Lagged Dependent Variables
TL;DR: In this paper, it is shown that the asymptotic distribution of the serial correlation coefficient calculated from the least-squares residuals differs from that of the true disturbances in a regression model where some of the regressors are lagged dependent variables.
Journal ArticleDOI
Maximum likelihood identification of Gaussian autoregressive moving average models
TL;DR: It is shown that the procedure described by Hannan (1969) for the estimation of the parameters of one-dimensional autoregressive moving average processes is equivalent to a three-stage realization of one step of the NewtonRaphson procedure for the numerical maximization of the likelihood function, using the gradient and the approximate Hessian.