Evolving national park models: The emergence of an economic imperative and its effect on the contested nature of the 'national' park concept in Northern Ireland.

National park models have evolved in tandem with the emergence of a multifunctional countryside. Sustainable development has been added to the traditional twin aims of conservation and recreation. This is typified by recent national park designations, such as the Cairngorms National Park in Scotland. A proposed Mournes national park in Northern Ireland has evolved a stage further with a model of national park to deliver national economic goals envisaged by government. This seeks to commodify the natural landscape. This paper compares Cairngorm and Mourne stakeholders’ views on the principal features of both models: park aims, management structures and planning functions. While Cairngorm stakeholders were largely positive from the outset, the model of national park introduced is not without criticism. Conversely, Mourne stakeholders have adopted an anti-national park stance. Nevertheless, the model of national park proposed possessing a strong economic imperative, an absence of the Sandford Principle as a means to manage likely conflicts, and lacking any planning powers in its own right, may still be insufficient to bring about widespread support for a Mourne national park. Such a model is also likely to accelerate the degradation of the Mourne landscape. Competing national identities (British and Irish) provide an additional dimension to the national park debate in Northern Ireland. Deep ideological cleavages are capable of derailing the introduction of a national park irrespective of the model proposed. In Northern Ireland the national park debate is not only about reconciling environmental and economic interests but also political and ethno-national differences.

Forecasting economic and financial time series with non-linear models

In this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among non-linear forecasting models for economic and financial time series. We review theoretical and empirical issues, including predictive density, interval and point evaluation and model selection, loss functions, data-mining, and aggregation. In addition, we argue that although the evidence in favor of constructing forecasts using non-linear models is rather sparse, there is reason to be optimistic. However, much remains to be done. Finally, we outline a variety of topics for future research, and discuss a number of areas which have received considerable attention in the recent literature, but where many questions remain.

Estimating the economic value of Mount Buffalo National Park with the travel cost and contingent valuation models

The Mount Buffalo National Park is the oldest national park in Victoria, Australia. There has been a rapid increase in the number of visitors to the park during the last decade and park management has been a concern, especially in the light of declining budgetary allocations and potential damage due to the increased visitor numbers. Policy options to increase park revenue remain unclear because of a lack of information on demand parameters and user costs. This study estimates the economic value of the park using the travel cost method (TCM) and the contingent valuation method (CVM). The TCM gives higher consumer surplus (CS) than the CVM. The CS shows that the economic value of the park is high and that there are opportunities to introduce innovative fee schemes to enhance its revenue. Present entry fee systems do not capture the economic value of the park.

Forecasting accuracy and estimation uncertainty using VAR models with short- and long-term economic restrictions: a Monte-Carlo study

Using vector autoregressive (VAR) models and Monte-Carlo simulation methods we investigate the potential gains for forecasting accuracy and estimation uncertainty of two commonly used restrictions arising from economic relationships. The Örst reduces parameter space by imposing long-term restrictions on the behavior of economic variables as discussed by the literature on cointegration, and the second reduces parameter space by imposing short-term restrictions as discussed by the literature on serial-correlation common features (SCCF). Our simulations cover three important issues on model building, estimation, and forecasting. First, we examine the performance of standard and modiÖed information criteria in choosing lag length for cointegrated VARs with SCCF restrictions. Second, we provide a comparison of forecasting accuracy of Ötted VARs when only cointegration restrictions are imposed and when cointegration and SCCF restrictions are jointly imposed. Third, we propose a new estimation algorithm where short- and long-term restrictions interact to estimate the cointegrating and the cofeature spaces respectively. We have three basic results. First, ignoring SCCF restrictions has a high cost in terms of model selection, because standard information criteria chooses too frequently inconsistent models, with too small a lag length. Criteria selecting lag and rank simultaneously have a superior performance in this case. Second, this translates into a superior forecasting performance of the restricted VECM over the VECM, with important improvements in forecasting accuracy ñreaching more than 100% in extreme cases. Third, the new algorithm proposed here fares very well in terms of parameter estimation, even when we consider the estimation of long-term parameters, opening up the discussion of joint estimation of short- and long-term parameters in VAR models.