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

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The Theory of Industrial Organization

Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.

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Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison

Econometric policy evaluation: A critique

How does a state's natural resource wealth influence its economic development? For the past fifty years, versions of this question have been explored by both economists and political scientists. New research suggests that resource wealth tends to harm economic growth, yet there is little agreement on why this occurs. This article reviews a wide range of recent attempts in both economics and political science to explain the “resource curse.” It suggests that much has been learned about the economic problems of resource exporters but less is known about their political problems. The disparity between strong findings on economic matters and weak findings on political ones partly reflects the failure of political scientists to carefully test their own theories.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0043887100008200

The Political Economy of the Resource Curse

Agricultural price booms are better explained by common factors than by market-specific factors such as supply shocks. A capital asset pricing model-type model shows why one should expect this and Granger causality analysis establishes the role of demand growth, monetary expansion and exchange rate movements in explaining price movements over the period since 1971. The demand for grains and oilseeds as biofuel feedstocks has been cited as the main cause of the price rise, but there is little direct evidence for this contention. Instead, index-based investment in agricultural futures markets is seen as the major channel through which macroeconomic and monetary factors generated the 2007–2008 food price rises.

How to Understand High Food Prices

The high food prices experienced over recent years have led to the widespread view that food price volatility has increased. However, volatility has generally been lower over the two most recent decades than previously. Variability over the most recent period has been high but, with the important exception of rice, not out of line with historical experience. There is weak evidence that grains price volatility more generally may be increasing but it is too early to say.

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Food price volatility

PRACTITIONERS' CORNER: Professor Hendry's Econometric Methodology

This paper examines the possible price impact of speculative bubbles and index-based investment activity on commodity futures prices over 2006–2008 I look specifically at crude oil, three non-ferrous metals (aluminium, copper and nickel) and three agricultural commodities (wheat, corn and soybeans) There is significant evidence for periods of explosive bubble behaviour in the copper market where I find three separate bubbles I also identify a bubble in the soybeans market The evidence for bubble behaviour is weaker for crude oil and nickel Aluminium, corn and wheat appear to have been bubble-free I also examine the effects of index-based investment on the same markets There is strong evidence that index-based investment did contribute to the rises in oil and metals prices over 2006–2008 but weaker evidence for similar effects on grains prices The maximum impact may have been to raise prices by the order of 15 per cent

Christopher L. Gilbert

Papers

How to Understand High Food Prices

Food price volatility

PRACTITIONERS' CORNER: Professor Hendry's Econometric Methodology

Speculative Influences on Commodity Futures Prices 2006-08

International Commodity Agreements: An obituary notice