Author: Kok Haur NgPublisher:
ISBN:
Category : Autoregression (Statistics)
Languages : en
Pages :
Book Description
Efficient Estimation of Autoregressive Conditional Duration (ACD) Models Using Estimating Functions (EF)
Author: Kok Haur NgPublisher:
ISBN:
Category : Autoregression (Statistics)
Languages : en
Pages :
Book Description
On Estimation of Autoregressive Conditional Duration (ACD) Models Based on Different Error Distributions
Author: D. PathmanathanPublisher:
ISBN:
Category : Stocks
Languages : en
Pages : 19
Book Description
Efficient Estimation in Nonlinear Autoregressive Time Series Models
Author: Hira L. KoulNon-Gaussian Autoregressive-Type Time Series
Author: N. BalakrishnaPublisher: Springer Nature
ISBN: 9811681627
Category : Mathematics
Languages : en
Pages : 238
Book Description
This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates most of the available models in the field and provide their probabilistic and inferential properties. This book classifies the stationary time-series models into different groups such as linear stationary models with non-Gaussian innovations, linear stationary models with non-Gaussian marginal distributions, product autoregressive models and minification models. Even though several non-Gaussian time-series models are available in the literature, most of them are focusing on the model structure and the probabilistic properties.
Nonparametric Estimation and Testing in Semiparametric Autoregressive Conditional Duration Models
Author: Pipat WongsaartPublisher:
ISBN:
Category : Time-series analysis
Languages : en
Pages : 346
Book Description
The advent of the so-called transaction data in finance has given econometrician the tool to address a variety of issues surrounding the structure of the trading process and/or price discovery in nancial markets. However, transaction data pose a number of unique econometric challenges that do not easily fit into the traditional modeling framework that have been developed so far in the literature. The ultimate goal of this thesis is to establish a novel econometric method of estimating the conditional intensity of the arrival times of financial events. This goal can be broken down into a few research objectives. (1) Firstly, it is to establish a new generation (semiparametric) approach to efficiently model the dynamics of the waiting time between the arrivals of financial events or what is commonly known as duration. (2) Secondly, it is to derive a set of estimators, so that empirical estimates of the density, survival and the baseline intensity functions associated with duration processes can be calculated. (3) Thirdly, it is to develop a novel testing procedure to test the marginal density function of financial durations. While the first and second objectives are discussed in detail in Chapter 2, the third objective is considered in Chapter 3. These semiparametric estimation and nonparametric testing procedure are introduced in conjunction with the detailed theoretical and experimental examinations of their statistical validity. Furthermore, the usefulness and practicability of these methods are illustrated using various datasests from both foreign exchange and international stock markets.
Efficient 4. Estimation for Autoregressive Models with Conditional Heterogeneity
Author: The New Palgrave Dictionary of Economics
Author: Publisher: Springer
ISBN: 1349588024
Category : Law
Languages : en
Pages : 7493
Book Description
The award-winning The New Palgrave Dictionary of Economics, 2nd edition is now available as a dynamic online resource. Consisting of over 1,900 articles written by leading figures in the field including Nobel prize winners, this is the definitive scholarly reference work for a new generation of economists. Regularly updated! This product is a subscription based product.
Efficient Estimation for Periodic Autoregressive Coefficients Via Residuals
Author: L. TangPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
A two-step estimation method is proposed for periodic autoregressive parameters via residuals when the observations contain trend and periodic autoregressive time series. The oracle efficiency of the proposed Yule-Walker-type estimator is established. The performance is illustrated by simulation studies and real data analysis.
Prediction Interval for Autoregressive Time Series Via Oracally Efficient Estimation of Multi-Step-Ahead Innovation Distribution Function
Author: Juanjuan KongPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
A kernel distribution estimator (KDE) is proposed for multi-step-ahead prediction error distribution of autoregressive time series, based on prediction residuals. Under general assumptions, the KDE is proved to be oracally efficient as the infeasible KDE and the empirical cumulative distribution function (cdf) based on unobserved prediction errors. Quantile estimator is obtained from the oracally efficient KDE, and prediction interval for multi-step-ahead future observation is constructed using the estimated quantiles and shown to achieve asymptotically the nominal confidence levels. Simulation examples corroborate the asymptotic theory.
Author: Nikolaus Hautsch