The economic costs of biological predation

This paper developes a bioeconomic model to analyse the economic losses from the reduced harvesting of prey species resulting from an increase in the stock of a natural predator. Examples of large mammals creating economic damage are whales and African elephants. The economic losses depend critically on the actual management of the prey stock, although the three measures we develop are equal when the stock is managed so as to maximize the sustained economic rent from the prey species. Predation losses are illustrated by the case of the Northeastern Atlantic Minke whale, where the estimate of the average predation cost per whale in 1991–1992 is between $US 1780 and $US 2370, using Norwegian cost and earnings data. A ten percent stock increase is estimated to cause a loss of almost $US 19 million to the fishers of the prey species. If half of this cost were assigned to Norway it would be equivalent to 2.8 and 6.7 percent of the gross profits of the Norwegian cod and herring fisheries, respectively.     

Asymptotic confidence bands for the estimated autocovariance and autocorrelation functions of vector autoregressive models

This paper provides closed-form formulae for computing the asymptotic covariance matrices of the estimated autocovariance and autocorrelation functions of stable VAR models by means of the delta method. These covariance matrices can be used to construct asymptotic confidence bands for the estimated autocovariance and autocorrelation functions to assess the underlying estimation uncertainty. The usefulness of the formulae for empirical work is illustrated by an application to inflation and output gap data for the U.S. economy indicating the existence of a significant short-run Phillips-curve tradeoff.First version received: November 2002/Final version received: September 2003     

Forecasting UK house prices: A time varying coefficient approach

Previous studies of UK house prices, developed from the demand and supply ofhousing or from the asset market approach have been poor in terms of robustness and ex-post forecasting ability. The UK housing market has suffered a number of structural changes, particularly since the early 1980s with substantial house price increases, financial market deregulation and the removal of mortgage market constraints through competition. Consequently, models which assume that the underlying data-generating process is stable and apply constant parameter techniques tend to suffer in terms of parameter instability. This article uses the Time Varying Coefficient (TVC) methodology where the underlying data-generating process in the UK housing market is treated as unstable. The estimation results of the TVC regression of UK house prices is compared with those obtained from three alternative constant parameter regressions. Comparisons of forecasting performance suggest the TVC regression out-performs forecasts from an Error Correction Mechanism (ECM), Vector Autoregressive (VAR) and an Autoregressive Time Series regression.     

Geometrical Interpretation of the Mean and the Constant in a Box-Jenkins Time Series Model

Certain manuals and computer programs mistakenly identify the mean with the constant in Box-Jenkins time series models. In this paper, it will be shown that (a) the mean and the constant have different values in autoregressive models, and (b) they have an algebraic and graphical relationship.     

环境艺术设计“课题制“教学模式实验

本文结合教学实践,就环境艺术设计教学中实施"课题制"教学模式进行了探索,着重对"课题制"教学模式的基本认识和运用进行了论述。     

Explaining Real Commercial Rents Using an Error Correction Model with Panel Data

This paper presents rent models for retail and office property in the United Kingdom. Panel data are used covering eleven regions for 29 years, enabling us to overcome the limitations of a relatively short time series. We use an error correction model (ECM) framework to estimate long-run equilibrium relationships and short-term dynamic corrections. The combination of panel data and an ECM is an innovative approach that is still being developed in economics. We construct new supply series that combine infrequent stock data with more frequent construction data. Separate regional models are estimated for retail and office properties. The regions are then combined into a number of panels on the basis of the income and price elasticities in the long-run and short-run models. Unlike previous studies, we find no evidence of a board north–south divide between low growth and high growth regions. Like these studies we do find a London effect: in London, demand elasticities for space with respect to both price (rent) and income are much lower in magnitude. We conclude that, while the economic drivers may vary, there is no evidence of differences in the operation of the regional property markets outside London. Elasticities for retail and office are similar. Our final models are parsimonious with single measures of economic activity and of supply and always support the use of an ECM.     

Aging and Retirement: Evidence Across Countries

How do demographic factors influence retirement? Using a large cross-country data set, I show that in countries with a larger share of elderly in their population the length of retirement is longer. This result holds true if I control for wealth effects, and when the effective labor force participation rate of the elderly is used instead of the official retirement age. Retirement policies and the social security size are strictly related: a new variable, representing the aggregate relevance of retirement policies, turns out to be significant in explaining the size of social security. Finally, the total amount of social security transfers is positively related with the increase of the elderly population, while in per capita terms this relation is not significant.