In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Providing a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. The only background required in order to understand the material presented in the book is a basic knowledge of classical linear regression models, of which a brief review is provided to refresh the reader's knowledge. Also, a few sections assume familiarity with matrix algebra, however, these sections may be skipped without losing the flow of the exposition. The book offers a step by step approach to the analysis of the salient features in time series such as the trend, seasonal, and irregular components. Practical problems such as forecasting and missing values are treated in some detail. This useful book will appeal to practitioners and researchers who use time series on a daily basis in areas such as the social sciences, quantitative history, biology and medicine. It also serves as an accompanying textbook for a basic time series course in econometrics and statistics, typically at an advanced undergraduate level or graduate level.

An Introduction to State Space Time Series Analysis, Chinese translation

In this paper we analyze the synchronization between the business cycles of US and Mexican regions. Regional economic activity in Mexico is measured using regional coincident indexes recently developed at Banco de Mexico, while US aggregate economic activity is measured with the national coincident index of the Federal Reserve Bank of Philadelphia. The framework for the empirical analysis is the structural linear times series model. We find a regional pattern in the covariance between cyclical disturbances in the US and in the Mexican regions: it is higher in the Northern than in the Central and Southern regions of the country. We also find that the elasticity of Mexican regional economic activity with respect to the US's aggregate economic activity exhibits a similar pattern. Moreover, while the variance of the business cycles in the Northern, North-Central, and Central regions is mostly associated with shocks to the US economy, in the Southern region it is mostly related to specific shocks to the Mexican economy.

https://www.econstor.eu/bitstream/10419/83695/1/717056678.pdf

Sincronización entre los cicles económicos de México y Estados Unidos: Nuevos resultados con base en el análisis de los indices coincidentes regionales de México

International synchronization of the Mexican states business cycles: Explaining factors

This paper examines the business cycle co‐movement in Mexican states over the period 2000–2014 by estimating an extended gravitational panel data model. Two different de‐trending filters are used to check the robustness of our results. The estimates suggest that the co‐movement increases as the size of the states’ economies does so as well as with the productive structure similarities and the relative level of development; however, the co‐movement decreases at a diminishing rate with geographical distance. There is also evidence of time‐dependent effects. In addition, the existence of moderate co‐movements among the states’ cycles suggests that common economic policies may not be suitable for all states, which implies there is a need for specific countercyclical policies to mitigate idiosyncratic shocks.

Mexican states' business cycles co‐movement over the period 2000–2014. A panel data model estimation

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.

En México

Papers

Comovimiento regional del empleo durante el ciclo económico