Forecasting Nevada gross gaming revenue and taxable sales using coincident and leading employment indexes

This article provides out-of-sample forecasts of Nevada gross gaming revenue (GGR) and taxable sales using a battery of linear and non-linear forecasting models and univariate and multivariate techniques. The linear models include vector autoregressive and vector error-correction models with and without Bayesian priors. The non-linear models include non-parametric and semi-parametric models, smooth transition autoregressive models, and artificial neural network autoregressive models. In addition to GGR and taxable sales, we employ recently constructed coincident and leading employment indexes for Nevada’s economy. We conclude that the non-linear models generally outperform linear models in forecasting future movements in GGR and taxable sales.     

城市空间增长模型研究的趋势、类型与方法

城市空间分散化增长蔓延的趋势要求对未来城市空间的发展方向进行预测研究。文章在对城市空间模型的研究案例及发展趋势进行总结的基础上,将城市空间模型总结为四种类型,分别是基于因果关系的城市空间增长影响因素静态模型、基于空间发展理论和GIS空间分析的准动态城市空间增长模型、基于系统动力学微分方程(组)的城市空间增长动态模型、以及基于微观主体作用演变机制的城市空间增长模拟模型,并认为城市空间增长模型将向微观离散化发展。     

Positive public economics: reinterpreting ‘optimal’ policies

The standard positive/normative divide fails to capture the way economists use ‘optimal’ taxation models. This paper argues that the better way to understand public economics is through a three-part division between positive, normative, and instrumental models. An instrumental model is about means and ends. Once this additional dimension is acknowledged, one can see that ‘optimal’ taxation models are closely connected to what are generally seen as purely positive models. I argue that economists have been using similar standards to assess ‘optimal’ taxation models as they use to assess positive models. Recent advances in optimal taxation theory have embraced the positive aspects of models, even about social welfare functions, something that is generally classified as a normative.     

Modeling household fertility decisions: estimation and testing of censored regression models for count data

"This paper adds to the recent body of research on fertility by estimating and testing censored Poisson regression models and censored negative binomial regression models of household fertility decisions. A novel feature of this study is that in each case the censoring threshold varies from individual to individual. Also, a Lagrange multiplier or score test is used to investigate overdispersion. In these regression models the dependent variable is the number of children. In this situation, censored Poisson regression models and censored negative binomial regression models have statistical advantages over OLS, uncensored Poisson regression models, and uncensored negative binomial regression models. The censored models employed in this study are estimated using panel data collected from the Consumer Expenditure Survey compiled by the [U.S.] Bureau of Labor Statistics."     

First-purchase diffusion models of new-product acceptance

This article constitutes a review and exposition of first-purchase diffusion models of new-product acceptance. These models have been developed to predict the cumulative number of new-product adopters over time and serve the purpose of forecasting sales for durable goods and novelty items. In addition to considering traditional models, a number of recent models are reviewed and illustrated, and limitations and further extensions of the models are discussed.     

Differences and similarities between ecological and economic models for biodiversity conservation

In this paper we investigate an important obstacle which substantially complicates co-operation between ecologists and economists but which has received little attention so far: differences between the modelling approaches in economics and ecology. To understand these differences, 60 models addressing issues relevant to biodiversity conservation have been selected randomly from eight international economic and ecological journals. The models have been compared according to a number of criteria including the models' level of generality; the mathematical techniques employed for formulation and solution of the models; the level of complexity and the way time, space and uncertainty are taken into account. The economic models sampled are formulated and analysed analytically, tend to be relatively simple and are mostly used to investigate general questions. Furthermore, they often ignore space, dynamics and uncertainty. Although some ecological models have similar properties, there is also a substantial number of another type of ecological models that are relatively complex and analysed by simulation. These models tend to be rather specific and often explicitly consider dynamics, space and uncertainty. The integrated ecological-economic models are observed to lie “in the middle” between ecological and economic models. An unexpected result is that they are not more complex than ecological and economic models (as one could have expected from a simple “merger” of models from both disciplines), but have an intermediate complexity.     

From small to large: A systematic comparison of gradually more complex econometric models

This paper treats the problem of validating macroeconometric models. A set of nine models are estimated on the same data base and subjected to the same simulation tests. They range from the simple IS model to a relatively realistic model of the Swedish economy in the postwar period. The main conclusion is that minor specification changes may drastically alter the dynamic characteristics of models, despite the fact that the models are theoretically plausible. A second purpose of the paper is to try to bridge the gap between the simple IS or IS-LM models used in the classroom and the much larger and more complex models that are actually used.     

IMPORT FORECASTS FOR INPUT-OUTPUT MODELS*

The paper presents forecasting models for (1) the share of competitive imports in the total demand for a commodity group and (2) the level of demand for competitive imports of a commodity group. The two forecasting models are used, respectively, with (1) input-output models which incorporate market share parameters as one vector of coefficients and (2) input-output models which assume imports have been determined autonomously. It is shown that these two types of input-output models can be made workable by prefixing one or other of the import forecasting models to the input-output model. Tests are made of the forecasting ability of the combined models.     

我国国有企业治理模式的优化选择

本文从公司治理的基本模式出发，分析不同公司治理模式的理论前提、社会背景和基本特征；并通过对公司治理模式的再梳理，指出企业资本结构是影响公司治理模式选择的内生因素；进而从公司治理的本质入手，构建了一个一般性的模型．分析公司治理模式分化的原因以及选择的依据；最后，对我国国有企业公司治理模式的选择提出了一些建议。     

Short-term inflation forecasting models for Turkey and a forecast combination analysis

In this paper, we produce short term forecasts for the inflation in Turkey, using a large number of econometric models. In particular, we employ univariate models, decomposition based approaches (both in frequency and time domain), a Phillips curve motivated time varying parameter model, a suite of VAR and Bayesian VAR models and dynamic factor models. Our findings suggest that the models which incorporate more economic information outperform the benchmark random walk, and the relative performance of forecasts are on average 30% better for the first two quarters ahead. We further combine our forecasts by means of several weighting schemes. Results reveal that, the forecast combination leads to a reduction in forecast error compared to most of the models, although some of the individual models perform alike in certain horizons.     

So you want to build a trade model? Available resources and critical choices

This paper reviews recent developments in trade theory, data, and modeling to provide guidance to researchers who are building and using trade models. Our findings show that trade models have fallen behind the latest development in theory, in part because data collection has not (and arguably will not) be able to keep pace with the needs of models built to incorporate the latest theories. The direction, much less the magnitude, of the errors made due to the limitations of today's trade models is uncertain, which should be a grave concern to modelers and the policymakers who rely on these models.     

Consumer valuation of energy-saving features of residential air conditioners with hedonic and choice models

The promotion of energy-efficient appliances is necessary to reduce the energetic and environmental burden of the household sector. However, many studies have reported that a typical consumer underestimates the benefits of energy-saving investment on the purchase of household electric appliances. To analyze this energy-efficiency-gap problem, many scholars have estimated implicit discount rates that consumers use for energy-consuming durables. Although both hedonic and choice models have been used in previous studies, a comparison between the two models has not yet been made. This study uses point-of-sale data about Japanese residential air conditioners and estimates implicit discount rates with both hedonic and choice models. Both models demonstrate that a typical consumer underinvests in energy efficiency. Although choice models generally estimate a lower implicit discount rate than hedonic models, the latter models estimate the values of other product characteristics more consistently than choice models.     

Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S.

We propose three Realized-GARCH-Kernel-type models which do not make the distribution assumptions on the return disturbance terms. We use this type of model to predict the return volatilities of the 50ETF in China and the S&P500 index in the U.S. The semiparametric kernel density estimator of our models, which captures the skewness, asymmetry and fat-tail of financial assets, performs well both statistically and economically. Our models have more predictive power than other eight comparable volatility models that need to pre-specify the distribution of the disturbance terms. Our results are robust to eight measures of realized volatility. Using option straddle strategies, we show that our models generate larger trading profits and greater Sharpe ratios than the other competing models.     

铁路成本代理模型研究

讨论了建立铁路成本代理模型的主要背景、设计思路、模型结构框架、主要内容、基本原理和实现的主要方法,以及模型的维护等问题。铁路成本代理模型主要作用在于运用工程分析的基本原理和方法,借助于财务会计核算数据,通过优化和控制策略的选择,科学准确地体现铁路运输生产活动的整体运输网络的联动特征,进而确定不同优化目标下的最优结果。     

Integrated bio-economic models as tools to support land-use decision making: a review of potential and limitations

Bio-economic modelling has become a useful tool for anticipating the outcomes of policies and technologies before their implementation. Advances in mathematical programming have made it possible to build more comprehensive models. In an overview of recent studies about bio-economic models applied to land-use problems in agriculture and forestry, we evaluated how aspects such as uncertainty, multiple objective functions, system dynamics and time have been incorporated into models. We found that single objective models were more frequently applied at the farm level, while multiple objective modelling has been applied to meet concerns at the landscape level. Among the objectives, social aspects are seldom represented in all models, when being compared to economic and environmental aspects. The integration of uncertainty is occasionally a topic, while stochastic approaches are more frequently applied than non-stochastic robust methods. Most multiple-objective models do not integrate uncertainty or sequential decision making. Static approaches continue to be more recurrent than truly dynamic models. Even though integrating multiple aspects may enhance our understanding of a system; it involves a tradeoff between complexity and robustness of the results obtained. Land-use models have to address this balance between complexity and robustness in order to evolve towards robust multiple-objective spatial optimization as a prerequisite to achieve sustainability goals.     

基于自组织理论的两种城市空间结构动态模型比较

传统的经济地理学模型由于在时间上是静止，并且忽略了空间各主体间的相互作用机制，因而是在均衡的状态下对交通，土地使用和环境系统进行描述，从而在指导规划实践过程中往往存在着一些问题。随着复杂科学的进展与对其对经济村理学的渗透，一些新的理论运用复杂科学的成果成功地对传统的经济地理学模型进行了改造，本文正是介绍并比较了这样的两种城市结构自组织模型：Krugman的自组织模型与Allen的自组织模型，这两种都是基于组织理论，但又从各自不同的角度出发，模拟了城市系统的形成与发展,助于规划者理解他们所必须面对的复杂现象，具有较高的理论与实践价值。     

Forecasting the US real house price index: Structural and non-structural models with and without fundamentals

We employ a 10-variable dynamic structural general equilibrium model to forecast the US real house price index as well as its downturn in 2006:Q2. We also examine various Bayesian and classical time-series models in our forecasting exercise to compare to the dynamic stochastic general equilibrium model, estimated using Bayesian methods. In addition to standard vector-autoregressive and Bayesian vector autoregressive models, we also include the information content of either 10 or 120 quarterly series in some models to capture the influence of fundamentals. We consider two approaches for including information from large data sets — extracting common factors (principle components) in factor-augmented vector autoregressive or Bayesian factor-augmented vector autoregressive models as well as Bayesian shrinkage in a large-scale Bayesian vector autoregressive model. We compare the out-of-sample forecast performance of the alternative models, using the average root mean squared error for the forecasts. We find that the small-scale Bayesian-shrinkage model (10 variables) outperforms the other models, including the large-scale Bayesian-shrinkage model (120 variables). In addition, when we use simple average forecast combinations, the combination forecast using the 10 best atheoretical models produces the minimum RMSEs compared to each of the individual models, followed closely by the combination forecast using the 10 atheoretical models and the DSGE model. Finally, we use each model to forecast the downturn point in 2006:Q2, using the estimated model through 2005:Q2. Only the dynamic stochastic general equilibrium model actually forecasts a downturn with any accuracy, suggesting that forward-looking microfounded dynamic stochastic general equilibrium models of the housing market may prove crucial in forecasting turning points.     

Are emotions to blame? — The impact of non-analytical information processing on decision-making and implications for fostering sustainability

Policy advice based on a rationalist perspective to foster sustainable behaviour has approached its limits; and gaps in the established models are becoming more and more obvious. To better understand how unsustainable choices are made, and to foster sustainable decision-making, alternatives to the rationalist models of human decision-making need to be investigated. Such alternative models have already demonstrated their usefulness in other fields than ecological economics. The paper begins with a presentation of conventional models of human behaviour, as well as their advances and limitations in ecological and behavioural economics. In most of these models, the dominance of analytical thinking still prevails. I identify this as problematic given the evidence for the influence of emotion and intuition in decision-making. To offer a perspective on human behaviour that acknowledges this influence, dual-process models are presented. Established applications of these models are then used to propose four basic types of explanations for unsustainable behaviour. Based on these explanations preliminary ideas to promote sustainable decision-making are developed. These ideas are considerably different from policy implications of the established economic model.