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

This edition of Ronald Miller and Peter Blair's classic textbook is an essential reference for students and scholars in the input-output research and applications community. The book has been fully revised and updated to reflect important developments in the field since its original publication. New topics covered include SAMs (and extended input-output models) and their connection to input-output data, structural decomposition analysis (SDA), multiplier decompositions, identifying important coefficients, and international input-output models. A major new feature of this edition is that it is also supported by an accompanying website with solutions to all problems, wide-ranging real-world data sets, and appendices with further information for more advanced readers. Input-Output Analysis is an ideal introduction to the subject for advanced undergraduate and graduate students in a wide variety of fields, including economics, regional science, regional economics, city, regional and urban planning, environmental planning, public policy analysis and public management.

Input-Output Analysis

Recent literature on border effect has demonstrated that national trade (intra- as well as interregional trade) tends to be more intense than international trade. Unfortunately, owing to the dearth...

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Opening the Interregional Trade ‘‘Black Box’’: The C-Intereg Database for the Spanish Economy (1995—2005)

A major problem in developing interregional–interindustry models, including multiregional input–output (MRIO) models, is how to combine seemingly incompatible databases. This research aims to estimate state-to-state commodity trade flow tables by major industrial sectors for the US useful for creating an MRIO-type model, the National Interstate Economic Model (NIEMO). The model is based on IMPLAN and related data for 2001. Constructing NIEMO was challenging because of the limited availability of commodity freight shipment data between the states. This helps to explain why a Chenery-Moses-type model such as NIEMO has not been developed in recent years. Two basic sets are necessary to construct NIEMO. Interregional trade tables to estimate trade coefficients by states and industry had been available from the US Commodity Transportation Survey Data since 1977, but reporting was discontinued for some years. For the years since 1993, this data deficit can be met to some extent with the recent Commodity Flow Survey (CFS) data from the Bureau of Transportation Statistics (BTS). Available since 1993, the CFS data are widely used, but they have several inherent problems. Recent attempts to estimate interregional trade flows using data from the 1997 CFS, based on IMPLAN data made this clear to us. This study suggests a new approach to estimate the trade tables between all 50 states plus D.C. and the rest of the world. These tables are assembled via a two-step method, to adjust for incomplete reported trade flows, and to update the adjusted trade flows by estimating values via a Fratar Model based on the 1997 CFS data. Reconciliation of the IMPLAN and CFS databases presents various problems that are addressed in this paper.

A two-step approach to estimating state-to-state commodity trade flows

Inter-regional trade estimation has been pointed out as a crucial problem when constructing a multiregional input–output system. Knowledge of inter-regional trade flows, at least of the pooled volume of exports and imports by commodity, is critical in accounting for important spillover and feedback effects deriving from inter-regional linkages. However, in most countries, there are no completely reliable survey-based statistics on inter-regional trade. Thus, this paper intends to evaluate the reasonability of using indirect inter-regional trade estimates, comparing different estimating methods and assessing the sensitivity of the model results. Based on our empirical comparisons we conclude that input–output models are not greatly affected by the insertion of different trade values. Thus, our results support the use of indirect estimates for inter-regional trade, whenever survey-based data are unavailable.

Inter-regional trade flow estimation through non-survey models: an empirical assessment

This paper describes a methodology to estimate current U.S. commodity inflows to a substate region using a supply-side, commodity-by-industry, input-output model and commodity flow data for U.S. states. Because the 1993 Commodity Flow Survey does not capture data below the state level, the estimation of commodity flows to a particular substate region of the United States has always proven difficult. By combining state level commodity flow data with the supply side, commodity-by-industry, input-output model, an estimate of commodity flows to smaller regions can be carried out entirely based on the regional industrial structure. Since the actual substate flows are typically unobserved, the accuracy of the methodology is unknown. However, by applying the same methodology to larger regions, with actual states used as the forecast region, the estimates can be compared with actual flows while maintaining an acceptable level of accuracy.

L N Liu

Papers

Estimating commodity inflows to a substate region using input-output data: commodity flow survey accuracy tests