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Comovimiento Regional del Empleo durante el Ciclo Economico en Mexico
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The article was published on 2010-01-01 and is currently open access. It has received 2 citations till now.read more
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Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this paper, the authors provide a unified and comprehensive theory of structural time series models, including a detailed treatment of the Kalman filter for modeling economic and social time series, and address the special problems which the treatment of such series poses.
An Introduction to State Space Time Series Analysis, Chinese translation
TL;DR: In this article, a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time-series models, is provided. But the authors do not provide a detailed analysis of the salient features in time series such as the trend, seasonal, and irregular components.
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Journal ArticleDOI
Co-integration and Error Correction: Representation, Estimation and Testing
TL;DR: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples.
Book
Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this article, the Kalman filter and state space models were used for univariate structural time series models to estimate, predict, and smoothen the univariate time series model.
Posted Content
Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this paper, the authors provide a unified and comprehensive theory of structural time series models, including a detailed treatment of the Kalman filter for modeling economic and social time series, and address the special problems which the treatment of such series poses.
Journal ArticleDOI
Testing for Common Trends
James H. Stock,Mark W. Watson +1 more
TL;DR: In this article, two tests for the number of common stochastic trends (i.e., for the order of cointegration) in a multiple time series with and without drift are developed.
A tutorial on Principal Components Analysis
TL;DR: PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension.