This paper introduces a new agent-based model, which incorporates the actions of individual homeowners in a long-term domestic stock model, and details how it was applied in energy policy analysis. The results indicate that current policies are likely to fall significantly short of the 80%...

The UK has a target for an 80% reduction in CO2 emissions by 2050 from a 1990 base. Domestic energy use accounts for around 30% of total emissions. This paper presents a comprehensive review of existing models and modelling techniques and indicates how they might be improved by considering...

Technical efficiency is estimated and examined for a cross-section of Australian dairy farms using various frontier methodologies; Bayesian and Classical stochastic frontiers, and Data Envelopment Analysis. The results indicate technical inefficiency is present in the sample data. Also...

This paper compares the direct and indirect methods of predicting the money multiplier and velocity of circulation in the UK economy. Forecasts are generated using the autoregressive (AR) model and Harvey's structural time series model. In addition to point forecasts, prediction intervals...

Market efficiency is an important feature of successful financial markets. The aim of this paper is to analyze the available evidence on the efficient market hypothesis (EMH). Meta-regression analysis is applied to 1,560 estimates of the Variance Ratio test of the efficiency of Asian and...

Land use regulations often delay residential development processes and increase the development costs, although they contribute to addressing market failures and realizing a well-organized urban spatial structure. Raising barriers to development can prevent households from moving in response to...

This study presents an integrated socioeconomic forecasting and analysis framework: a spatial regional econometric input–output model and its application to the Chicago metropolitan area. The new framework is designed to overcome some limitations of existing models, particularly (1) limited...

A bootstrap bias-correction method is applied to statistical inference in the regression model with autocorrelated errors. It is found that this method substantially reduces small-sample size distortions relative to alternative methods proposed in the literature.

A bootstrap bias-correction method is applied to statistical inference in the regression model with autocorrelated errors. It is found that this method substantially reduces small-sample size distortions relative to alternative methods proposed in the literature.

This paper is concerned with statistical inference for the coefficient of the linear regression model when the error term follows an autoregressive (AR) model. Past studies have reported severe size distortions, when the data are trending and autocorrelation of the error term is high. In this...