On the foundations of land use theory : Discrete versus continuous populations

Urban economists and location theorists have long employed land use models with a continuum of agents distributed over a continuum of locations. However, these continous models have been criticized on behavioral grounds in that individual households can consume only zero amounts of land in equilibrium. Hence the central purpose of this paper is to propose an alternative interpretation of these continous models as limiting approximations of discrete population models. In particular, it is shown that for large population sizes, the population distributions of the classical continuous model uniformly approximate the equilibrium population distributions generated by an appropriately defined class of discrete population models.     

An exact non-iterative sampling procedure for discrete missing data problems

Many statistical problems can be formulated as discrete missing data problems (MDPs). Examples include change-point problems, capture and recapture models, sample survey with non-response, zero-inflated Poisson models, medical screening/diagnostic tests and bioassay. This paper proposes an exact non-iterative sampling algorithm to obtain independently and identically distributed (i.i.d.) samples from posterior distribution in discrete MDPs. The new algorithm is essentially a conditional sampling, thus completely avoiding problems of convergence and slow convergence in iterative algorithms such as Markov chain Monte Carlo. Different from the general inverse Bayes formulae (IBF) sampler of Tan, Tian and Ng (Statistica Sinica, 13 , 2003, 625), the implementation of the new algorithm requires neither the expectation maximization nor the sampling importance resampling algorithms. The key idea is to first utilize the sampling-wise IBF to derive the conditional distribution of the missing data given the observed data, and then to draw i.i.d. samples from the complete-data posterior distribution. We first illustrate the method with a performing example and then apply the method to contingency tables with one supplemental margin for an human immunodeficiency virus study.     

Validating an agent-based model of the Zipf׳s Law: A discrete Markov-chain approach

This study discusses the validation of an agent-based model of emergent city systems with heterogeneous agents. To this end, it proposes a simplified version of the original agent-based model and subjects it to mathematical analysis. The proposed model is transformed into an analytically tractable discrete Markov model, and its city size distribution is examined. Its discrete nature allows the Markov model to be used to validate the algorithms of computational agent-based models. We show that the Markov chains lead to a power-law distribution when the ranges of migration options are randomly distributed across the agent population. We also identify sufficient conditions under which the Markov chains produce the Zipf׳s Law, which has never been done within a discrete framework. The conditions under which our simplified model yields the Zipf׳s Law are in agreement with, and thus validate, the configurations of the original heterogeneous agent-based model.     

Quasi-maximum likelihood estimation and testing for nonlinear models with endogenous explanatory variables

I propose a quasi-maximum likelihood framework for estimating nonlinear models with continuous or discrete endogenous explanatory variables. Joint and two-step estimation procedures are considered. The joint procedure is a quasi-limited information maximum likelihood procedure, as one or both of the log likelihoods may be misspecified. The two-step control function approach is computationally simple and leads to straightforward tests of endogeneity. In the case of discrete endogenous explanatory variables, I argue that the control function approach can be applied with generalized residuals to obtain average partial effects. I show how the results apply to nonlinear models for fractional and nonnegative responses.     

Precise computation of a competitive equilibrium of the discrete land market model

This paper provides a finite algorithm to compute a competitive equilibrium of the Alonso-type discrete land market model (known as bid-rent equilibrium) without assuming any condition on the spatial configuration such as monocentricity. The existence of the finite algorithm implies that the discrete land market model is sufficiently tractable for computer simulation analysis (sensitivity analysis) even if the spatial configuration is variable. Specifically, we can evaluate numerically the effects of new routes in a transportation network upon equilibrium rents and commuting mode.     

Large lot zoning, property taxes, and metropolitan area

The paper is a study in positive economics. Two- and three-sector models are used to determine the effects on urban land and housing markets of land use controls such as minimum lot size, maximum density, and maximum bulk requirements. The effects of these controls on rural-urban land conversion and on the effects of property taxes on urban land markets are also investigated. The land use requirements are found to accelerate rural-urban land conversion in some cases and affect the impact of property taxes on urban land and housing markets.     

Time varying VARs with inequality restrictions

In many applications involving time-varying parameter VARs, it is desirable to restrict the VAR coefficients at each point in time to be non-explosive. This is an example of a problem where inequality restrictions are imposed on states in a state space model. In this paper, we describe how existing MCMC algorithms for imposing such inequality restrictions can work poorly (or not at all) and suggest alternative algorithms which exhibit better performance. Furthermore, we show that previous algorithms involve an approximation relating to a key prior integrating constant. Our algorithms are exact, not involving this approximation. In an application involving a commonly used U.S. data set, we present evidence that the algorithms proposed in this paper work well.     

Modelling the long-term effects of transport and land use policies on industrial locational behaviour: A discrete choice model system

This paper describes a disaggregate behavioural model system developed for forecasting industrial locations. It is structured basically in terms of a nested logit model, covering relocation decisions, area-wide locational choices and local locational choices together with shipment destination choices. Several techniques are developed to overcome the difficulties in the application of discrete choice models to spatial problems. The model system was calibrated for the Nagoya metropolitan area in Japan and its validity was tested using another data set. It allows analysis of the effects of transport and land use policies not only by zone but also by firms of different attributes such as sector and capital size.     

The (mis)specification of discrete duration models with unobserved heterogeneity: A Monte Carlo study

Empirical researchers usually prefer statistical models that can be easily estimated with the help of commonly available software packages. Sequential binary models with or without normal random effects are an example of such models that can be adopted to estimate discrete duration models with unobserved heterogeneity. But an easy-to-implement estimation may incur a cost. In this paper we conduct a Monte Carlo simulation to evaluate the consequences of omitting or misspecifying the unobserved heterogeneity distribution in single-spell discrete duration models.     

Backup coverage models in nature reserve site selection with spatial spread risk heterogeneity

Set covering and maximal covering problems are well-known integer programming models in location analysis. Such models have also been used in reserve site selection modeling. They aim at selecting sites to conserve species, sometimes reflecting a desire to group protected sites together or to separate sites. This paper uses such models but considers the case of land heterogeneity in terms of the risk of large disturbances that threaten species even within a reserve, such as fires, diseases, pests or invasive species. It removes the classical assumption of homogeneous land sites and considers both adjacency in areas with a low risk of multi-parcel disturbance and distance between sites in areas with a high risk of a large destructive event. The models are explored in a stylized data set and applied to a portion of the state of Oregon with comparison between the standard covering models in homogeneous and heterogeneous risk settings.     

The housing question in Japan

This article first examines how bad housing conditions are in Japan based on international comparisons. The next question is whether the extremely high land prices in Japan can be explained by economic logic. We then turn to more specific housing policy questions that are peculiar to or important in Japan, such as the tax advantages of owning land that have caused under-utilization of land and the reasons why the average size of Japanese rental housing is so small.     

基于离散选择模型的推荐系统改进算法

准确地预估用户的点击率,并根据该概率对商品排序以供用户选择在推荐系统领域有着重要的意义。推荐系统中常用的因子分解机等机器学习模型一般只考虑用户选择单个商品的概率,忽略了候选商品之间的相互影响,离散选择模型则考虑将商品候选集作为整体进行考虑。提出了使用深度学习模型来改进离散选择模型,模型使用相对特征层、注意力机制等网络结构帮助深度学习模型进行不同商品间的特征比较,研究结果表明引入离散选择模型的深度学习模型表现优于梯度提升决策树、因子分解机等模型。     

The Marginalists Who Confronted Land

A bstract .   Although the neoclassical turn in economics demoted land as a factor, important economists of neoclassical thinking, from neoclassical predecessors such Hermann-Heinrich Gossen through figures such as Leon Walras, did view land as a distinct factor of production. Walras, in particular, favored the use of land rent for public revenue. This paper examines the treatment of land by several neoclassical and Austrian economists and shows how, although the neoclassical school today has managed to bury land, some of the key figures who founded these schools did confront land as a factor. The burial of land is thus not inherent in neoclassical economics, but is a historical development that can be reversed.     

Forecasting and stress testing credit card default using dynamic models

We present discrete time survival models of borrower default for credit cards that include behavioural data about credit card holders and macroeconomic conditions across the credit card lifetime. We find that dynamic models which include these behavioural and macroeconomic variables provide statistically significant improvements in model fit, which translate into better forecasts of default at both account and portfolio levels when applied to an out-of-sample data set. By simulating extreme economic conditions, we show how these models can be used to stress test credit card portfolios.     

Bayesian modeling of joint and conditional distributions

In this paper, we study a Bayesian approach to flexible modeling of conditional distributions. The approach uses a flexible model for the joint distribution of the dependent and independent variables and then extracts the conditional distributions of interest from the estimated joint distribution. We use a finite mixture of multivariate normals (FMMN) to estimate the joint distribution. The conditional distributions can then be assessed analytically or through simulations. The discrete variables are handled through the use of latent variables. The estimation procedure employs an MCMC algorithm. We provide a characterization of the Kullback–Leibler closure of FMMN and show that the joint and conditional predictive densities implied by the FMMN model are consistent estimators for a large class of data generating processes with continuous and discrete observables. The method can be used as a robust regression model with discrete and continuous dependent and independent variables and as a Bayesian alternative to semi- and non-parametric models such as quantile and kernel regression. In experiments, the method compares favorably with classical nonparametric and alternative Bayesian methods.     

Computationally simple lattice methods for option and bond pricing

We propose new lattice-based algorithms for option and bond pricing, which rely on computationally simple trees, i.e., trees with the number of nodes that grows at most linearly in the number of time intervals. Contrary to commonly used methods, the target diffusion is approximated directly, without having to transform the original process into a constant volatility process. The discrete approximating process converges to the target continuous process, and the proposed algorithms are shown to be efficient and accurate for pricing purposes.     

Masking identification of discrete choice models under simulation methods

We present examples based on actual and synthetic datasets to illustrate how simulation methods can mask identification problems in the estimation of discrete choice models such as mixed logit. Simulation methods approximate an integral (without a closed form) by taking draws from the underlying distribution of the random variable of integration. Our examples reveal how a low number of draws can generate estimates that appear identified, but in fact, are either not theoretically identified by the model or not empirically identified by the data. For the particular case of maximum simulated likelihood estimation, we investigate the underlying source of the problem by focusing on the shape of the simulated log-likelihood function under different conditions.     

Fast movement strategies for a step-and-scan wafer stepper

We describe algorithms for the determination of fast movement strategies for a step-and-scan wafer stepper, a device that is used for the photolithographic processing of integrated circuits. The proposed solution strategy consists of two parts. First, we determine the maximum number of congruent rectangular chips that can be packed on a wafer, subject to the restriction that the chips are placed in a rectangular grid. Second, we find fast movement strategies for scanning all chips of a given packing, given the mechanical restrictions of the wafer stepper. The corresponding combinatorial optimization problem is formulated as a generalized asymmetric traveling salesman problem. We show how feasible scan strategies are determined, and how these strategies are improved by local search techniques, such as iterative improvement based on 2- and 3-exchanges, and simulated annealing based on 2-exchanges.