Open Access
Comovimiento regional del empleo durante el ciclo económico
En México,Marcelo Delajara +1 more
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In this paper, the extent of comovement in employment over the business cycle among the regions of Mexico by analyzing the covariance of the disturbances in regional cycles during the period July 1997-October 2009 was determined.Abstract:
We determine the extent of comovement in employment over the business cycle among the regions of Mexico by analyzing the covariance of the disturbances in regional cycles during the period July 1997-October 2009. Employment refers to the number of workers with permanent contracts affiliated to the Instituto Mexicano del Seguro Social. Trend and cycle decomposition and the variance-covariance matrix of the disturbances in regional cycles are obtained from the estimation —using state space methods— of a structural multivariate model of the employment time series. We find, for most regions, that employment comovement is high and that the variance of regional cycles’ disturbances is largely associated with the fluctuations in national employment. We do not find evidence, however, of a common underlying cycle, which means that employment comovement would arise from the geographical propagation of regional specific shocks.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.
MonographDOI
Sincronización entre los cicles económicos de México y Estados Unidos: Nuevos resultados con base en el análisis de los indices coincidentes regionales de México
TL;DR: In this paper, the synchronization between the business cycles of US and Mexican regions was analyzed using the structural linear times series model, and the covariance between cyclical disturbances in the US and in the Mexican regions is found to be higher in the Northern than in the Central and Southern regions of the country.
Journal ArticleDOI
International synchronization of the Mexican states business cycles: Explaining factors
TL;DR: In this article, the authors identify the explaining factors of the synchronization of the business cycles of the Mexican states and those of the US economy by de-trending the series of total formal employment (Mexican states) and non-farm employment and industrial production (US).
Journal ArticleDOI
Mexican states' business cycles co‐movement over the period 2000–2014. A panel data model estimation
TL;DR: In this paper, the authors examined the business cycle co-movement in Mexican states over the period 2000-2014 by estimating an extended gravitational panel data model and two different de-trending filters were used to check the robustness of their results.
References
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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.
Posted Content
A Tutorial on Principal Component Analysis.
TL;DR: This manuscript focuses on building a solid intuition for how and why principal component analysis works, and crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA.
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.