New Introduction to Multiple Time Series Analysis

@inproceedings{Ltkepohl2007NewIT,
  title={New Introduction to Multiple Time Series Analysis},
  author={Helmut Ltkepohl},
  year={2007}
}
This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive, cointegrated, vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models. Least squares, maximum likelihood, and Bayesian methods are considered for estimating these models. Different procedures for model selection and… 
Vector Autoregresive Moving Average Identification for Macroeconomic Modeling: Algorithms and Theory
This paper develops a new methodology for identifying the structure of VARMA time series models. The analysis proceeds by examining the echelon canonical form and presents a fully automatic data
Business cycle analysis and VARMA models
COPAR-multivariate time series modeling using the copula autoregressive model
TLDR
A novel copula-based model is proposed that allows for the non-linear and non-symmetric modeling of serial as well as between-series dependencies and exploits the flexibility of vine copulas.
New methods in time series analysis : univariate testing and network autoregression modelling
TLDR
A bespoke stationarity test for use when univariate data has missing observations is described, based upon a second-generation wavelet method known as the non-decimated lifting transform, which allows for the analysis of irregularly spaced data.
Selected Methods of Economic Time Series Description
The article is focused on selected quantitative methods which can be used for description of economic time series. Vector autoregressive models and cointegration analysis play an important role in
Studying Co-movements in Large Multivariate Models Without Multivariate Modelling
We propose an approach for checking the data admissibility of non-stationary multivariate time series models (VAR or VARMA) through that of their implied individual ARIMA specifications. In
Multivariate Time Series
In this chapter vector time series models are considered for stationary processes. There is a brief discussion of stationarity, but we leave the reader to refer for further detail to Patterson (2010)
Modelling multivariate financial time series using vector autoregressive processes
TLDR
It is concluded that real-world data often does not fit the VAR model and VECM requirements and that further improved models should be considered as well.
...
1
2
3
4
5
...

References

SHOWING 1-7 OF 7 REFERENCES
Applied Econometric Time Series
PREFACE. ABOUT THE AUTHOR. Chapter DIFFERENCE EQUATIONS . 1 Time-Series Models. 2 Difference Equations and Their Solutions. 3 Solution by Iteration. 4 An Alternative Solution Methodology. 5 The
Co-integration and error correction: representation, estimation and testing
The relationship between cointegration and error correction models, first suggested by Granger, is here extended and used to develop estimation procedures, tests, and empirical examples. A vector of
Topics in structural VAR econometrics
This book provides a new approach to the identification and the estimation of structural VAR models. The role of deterministic variables and the connection with the concept of cointegration is
Sources of Business Cycle Fluctuations
What shocks account for the business cycle frequency and long-run movements of output and prices? This paper addresses this question using the identifying assumption that only supply shocks, such as
Time Series Analysis
A ordered sequence of events or observations having a time component is called as a time series. Some good examples of time series are daily opening and closing stock prices, daily humidity,
Alternative Explanations of the Money-Income Correlation
  • 1986