Large Sample Inference For Long Memory Processes - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Large Sample Inference For Long Memory Processes write by Donatas Surgailis. This book was released on 2012-04-27. Large Sample Inference For Long Memory Processes available in PDF, EPUB and Kindle. Box and Jenkins (1970) made the idea of obtaining a stationary time series by differencing the given, possibly nonstationary, time series popular. Numerous time series in economics are found to have this property. Subsequently, Granger and Joyeux (1980) and Hosking (1981) found examples of time series whose fractional difference becomes a short memory process, in particular, a white noise, while the initial series has unbounded spectral density at the origin, i.e. exhibits long memory.Further examples of data following long memory were found in hydrology and in network traffic data while in finance the phenomenon of strong dependence was established by dramatic empirical success of long memory processes in modeling the volatility of the asset prices and power transforms of stock market returns.At present there is a need for a text from where an interested reader can methodically learn about some basic asymptotic theory and techniques found useful in the analysis of statistical inference procedures for long memory processes. This text makes an attempt in this direction. The authors provide in a concise style a text at the graduate level summarizing theoretical developments both for short and long memory processes and their applications to statistics. The book also contains some real data applications and mentions some unsolved inference problems for interested researchers in the field./a
Large Sample Inference for Long Memory Processes
Large Sample Inference for Long Memory Processes - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Large Sample Inference for Long Memory Processes write by Liudas Giraitis. This book was released on 2012. Large Sample Inference for Long Memory Processes available in PDF, EPUB and Kindle. A discrete-time stationary stochastic process with finite variance is said to have long memory if its autocorrelations tend to zero hyperbolically in the lag, i.e. like a power of the lag, as the lag tends to infinity. The absolute sum of autocorrelations of such processes diverges and their spectral density at the origin is unbounded. This is unlike the so-called weakly dependent processes, where autocorrelations tend to zero exponentially fast and the spectral density is bounded at the origin. In a long memory process, the dependence between the current observation and the one at a distant future is persistent; whereas in the weakly dependent processes, these observations are approximately independent. This fact alone is enough to warn a person about the validity of the classical inference procedures based on the square root of the sample size standardization when data are generated by a long-term memory process.The aim of this volume is to provide a text at the graduate level from which one can learn, in a concise fashion, some basic theory and techniques of proving limit theorems for numerous statistics based on long memory processes. It also provides a guide to researchers about some of the inference problems under long memory.
Large Sample Inference for Long Memory Processes
Large Sample Inference for Long Memory Processes - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Large Sample Inference for Long Memory Processes write by Liudas Giraitis. This book was released on 2011. Large Sample Inference for Long Memory Processes available in PDF, EPUB and Kindle.
Long-Memory Processes
Long-Memory Processes - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Long-Memory Processes write by Jan Beran. This book was released on 2013-05-14. Long-Memory Processes available in PDF, EPUB and Kindle. Long-memory processes are known to play an important part in many areas of science and technology, including physics, geophysics, hydrology, telecommunications, economics, finance, climatology, and network engineering. In the last 20 years enormous progress has been made in understanding the probabilistic foundations and statistical principles of such processes. This book provides a timely and comprehensive review, including a thorough discussion of mathematical and probabilistic foundations and statistical methods, emphasizing their practical motivation and mathematical justification. Proofs of the main theorems are provided and data examples illustrate practical aspects. This book will be a valuable resource for researchers and graduate students in statistics, mathematics, econometrics and other quantitative areas, as well as for practitioners and applied researchers who need to analyze data in which long memory, power laws, self-similar scaling or fractal properties are relevant.
Causal Inference in Econometrics
Causal Inference in Econometrics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Causal Inference in Econometrics write by Van-Nam Huynh. This book was released on 2015-12-28. Causal Inference in Econometrics available in PDF, EPUB and Kindle. This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.
