Endogenous Growth and Structural Change in a Dynamic Input-Output Model

This paper introduces a simple dynamic input-output model, in which some of the most important properties of recent endogenous growth theory are included: innovation, knowledge spillovers, constant returns to scale at the macro level, and full employment. The wish to keep the hybrid model as tractable as possible (despite the industry detail) caused some substantial simplifications: contrary to most endogenous growth models, the model lacks an explicit microeconomic foundation and disregards any opportunity for instantaneous substitution. After the constituent equations are presented, the long-run behavior of the model is studied by a number of computer simulations for a hypothetical economy. The paper concludes with some illustrations of the potential practical power of future interindustry endogenous growth models in integrating issues like technology, investment, trade and education.     

Modeling multiple criteria in the faculty assignment problem

This paper describes two multiple criteria models for generating teaching assignments for faculty members. The first model requires faculty members to input utility values for possible teaching schedules while the second model requires that each faculty member only rank order possible teaching schedules. Unlike previously proposed models for this problem, the models described in this paper allow the user to evaluate the impacts and tradeoffs between goals that are associated with each individual faculty member and goals that are associated with the organization as a whole during the faculty assignment process.     

Graph theory and managing urban change

In almost any kind of empirical research, it is useful—some would say essential—to have in mind a “model” of the thing or process to be investigated [1]. Research may subsequently validate or invalidate this initial model. But some kind of model is useful from the outset of any research project, in order to provide a link between the precise directions of the research and its ultimate objectives.To examine and describe the organizational and institutional aspects of major urban systems, therefore, requires some kind of model of a major urban system. Such a model has two important functions. It serves as a framework for interpreting data which are gathered in the course of the research; and it also suggests possible hypotheses for testing against the data.The model in a piece of research may be either stated or implied. But for research into phenomena as complex as major urban systems, it is well to have a model which is spelled out in fairly explicit terms.There is really no shortage of explicit models of urban systems. (Indeed, it sometimes seems as if the difficulty lies more in the opposite direction!). Each of these models has its respective strengths and weaknesses and, consequently, is more appropriate for some kinds of research than for others [2]. The purpose of this paper is to describe a model which provides a suitable basis not merely for understanding but also for managing changes in major urban systems. In other words, this paper is intended to provide the basis for an essentially action-oriented model of major urban systems.     

Discrete-space agglomeration model with social interactions: Multiplicity,stability, and continuous limit of equilibria

This study examines the properties of equilibrium, including the stability, of discrete-space agglomeration models with social interactions. The findings reveal that while the corresponding continuous-space model has a unique equilibrium, the equilibrium in discrete space can be non-unique for any finite degree of discretization by characterizing the discrete-space model as a potential game. Furthermore, it indicates that despite the above result, any sequence of discrete-space models’ equilibria converges to the continuous-space model’s unique equilibrium as the discretization of space is refined.     

Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals,financial stress and economic uncertainty

We develop models for examining possible predictors of growth of China's foreign exchange reserves that embrace Chinese and global trade, financial and risk (uncertainty) factors. Specifically, by comparing with other alternative models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform not only linear models (such as random walk, recursive OLS-AR(1) models, recursive OLS with all predictive variables models) but also the Bayesian model averaging (BMA) model for examining possible predictors of growth of those reserves. The DMS is the best overall across all forecast horizons. While some predictors matter more than others over the forecast horizons, there are few that stand the test of time. The US–China interest rate differential has a superior predictive power among the 13 predictors considered, followed by the nominal effective exchange rate and the interest rate spread for most of the forecast horizons. The relative predictive prowess of the oil and copper prices alternates, depending on the commodity cycles. Policy implications are also provided.     

Short-term forecasting of crime

The major question investigated is whether it is possible to accurately forecast selected crimes 1 month ahead in small areas, such as police precincts. In a case study of Pittsburgh, PA, we contrast the forecast accuracy of univariate time series models with naïve methods commonly used by police. A major result, expected for the small-scale data of this problem, is that average crime count by precinct is the major determinant of forecast accuracy. A fixed-effects regression model of absolute percent forecast error shows that such counts need to be on the order of 30 or more to achieve accuracy of 20% absolute forecast error or less. A second major result is that practically any model-based forecasting approach is vastly more accurate than current police practices. Holt exponential smoothing with monthly seasonality estimated using city-wide data is the most accurate forecast model for precinct-level crime series.     

Minimax optimality of CUSUM for an autoregressive model

Different change point models for AR(1) processes are reviewed. For some models, the change is in the distribution conditional on earlier observations. For others, the change is in the unconditional distribution. Some models include an observation before the first possible change time – others not. Earlier and new CUSUM type methods are given, and minimax optimality is examined. For the conditional model with an observation before the possible change, there are sharp results of optimality in the literature. The unconditional model with possible change at (or before) the first observation is of interest for applications. We examined this case and derived new variants of four earlier suggestions. By numerical methods and Monte Carlo simulations, it was demonstrated that the new variants dominate the original ones. However, none of the methods is uniformly minimax optimal.     

On the relationship between cost and service models for general inventory systems

In this paper, we present a systematic overview of possible relations between cost and service models for fairly general single- and multi-stage inventory systems. In particular, we relate various types of penalty costs in pure cost models to equivalent types of service measures in service models. We show how an optimal policy for a service model may be obtained from cost-optimal policies in a related pure cost model. Pure cost models have been studied extensively in the literature. By our results it seems possible to transform many of the known optimal solutions for pure cost models to service models, which are more appropriate from a practical point of view. A number of examples are discussed to show the generality and the possibly far reaching consequences of the results.     

Stochastic search variable selection in vector error correction models with an application to a model of the UK macroeconomy

This paper develops methods for stochastic search variable selection (currently popular with regression and vector autoregressive models) for vector error correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with a single unrestricted model and either do model selection or model averaging in an automatic and computationally efficient manner. We apply our methods to a large UK macroeconomic model. Copyright © 2011 John Wiley & Sons, Ltd.     

Weighted least squares estimators in possibly misspecified nonlinear regression

The behavior of estimators for misspecified parametric models has been well studied. We consider estimators for misspecified nonlinear regression models, with error and covariates possibly dependent. These models are described by specifying a parametric model for the conditional expectation of the response given the covariates. This is a parametric family of conditional constraints, which makes the model itself close to nonparametric. We study the behavior of weighted least squares estimators both when the regression function is correctly specified, and when it is misspecified and also involves possible additional covariates.     

Rules for building statistical models*

“Statistics uses the empirical hypothesis that apparatus (‘lotteries’) exist, admitting random choices of one among any given number of elements. Such apparatus do not exist in absolute perfection and their degree of perfection can only be defined after development of their theory. Their role is analogous to that of rigid bodies in euclidean geometry and of perfect clocks in dynamics. Empirical interpretation of probability statements is only possible with reference to such random apparatus or to natural phenomena empirically found to behave statistically sufficiently tike these”. D. van Dantzig [1] Summary One of the fundamental questions in statistical model building is when to use the same model for different situations or experiments. Axioms, however useful mathematically, say nothing about this. The author therefore proposes to introduce rules for the choice of a statistical model which have the character of instructions for use of the statistical toolkit. One of the basic rules proposed is the “principle of equivalence”. Two repeatable experiments are called statistically equivalent if they cannot be distinghuished from one another by means of sequences of outcomes of arbitrary length. This principle is elaborated and illustrated by means of an example. If experiments are (deemed to be) statistically equivalent the use of the same statistical model for all of them is justified. This principle is then used for the introduction of conditional probabilities and composite models, with symmetric probability spaces as models for randomizers as a startingpoint.     

Some models of truck allocation for household refuse collection*

A number of models is developed for the selection and allocation of trucks for household refuse collection. The models vary from a simple model related to the optimal batch-size formula to a linear programming model. A minimum-cost selection is made on the basis of time per pick-up, weight per pick-up, driving time from collection area to landfill site, capacity and costs of the relevant truck-crew combinations and lengths of possible shifts. The models are equally applicable to other house-to-house collecting and distributing activities.     

Testing for structural stability in the whole sample

The paper examines a Lagrange Multiplier type test for the constancy of the parameter in general models with dependent data without imposing any artificial choice of the possible location of the break. In order to prove the asymptotic behaviour of the test, we extend a strong approximation result for partial sums of a sequence of random variables. We also present a Monte-Carlo experiment to examine the finite sample performance of the test and how it compares with tests which assume some knowledge of the possible location of the break.     

Seemingly Unrelated Negative Binomial Regression

This paper discusses the specification and estimation of seemingly unrelated multivariate count data models. A new model with negative binomial marginals is proposed. In contrast to a previous model based on the multivariate Poisson distribution, the new model allows for over-dispersion, a phenomenon that is frequently encountered in economic count data. Semi-parametric estimation is possible if some of the assumption of the fully specified model are violated.     

A new non‐randomized response model: The parallel model

Despite certain advances for non‐randomized response (NRR) techniques in the past 6 years, the existing non‐randomized crosswise and triangular models have several limitations in practice. In this paper, I propose a new NRR model, called the parallel model with a wider application range. Asymptotical properties of the maximum likelihood estimator (and its modified version) for the proportion of interest are explored. Theoretical comparisons with the crosswise and triangular models show that the parallel model is always more efficient than the two existing NRR models for most of the possible parameter ranges. Bayesian methods for analyzing survey data from the parallel model are developed. A case study on college students' premarital sexual behavior at Wuhan and a case study on plagiarism at the University of Hong Kong are conducted and are used to illustrate the proposed methods. © 2014 The Authors. Statistica Neerlandica © 2014 VVS.     

On the information criterion for selecting regressors

Summary It has been asserted in the past that any Bayesian treatment of the model selection problem in regression using some form of continuous loss structure would always lead to using the largest possible model (Leamer 1979; Chow 1981). We show in this paper that, provided the distinction between the choice of a model and the estimation of its parameters is maintained, the Kullback-Leibler information measure can be used in a Bayesian context to derive a criterion which may lead to parsimony of parameters in regression analysis. The regression models are taken as restrictions of a general class of distributions which includes the truen-variate distribution of the variabley. Separate criteria for the cases of known and unknown variance ofy are obtained. In the limiting situation when prior opinions about the parameters are weak, these criteria reduce to special cases of the generalizedC _p and AIC criteria (Atkinson 1981). Relationship with other criteria is discussed.     

On the existence of maximum likelihood estimates in random effects models for clustered multivariate binary data

We consider a class of random effects models for clustered multivariate binary data based on the threshold crossing technique of a latent random vector. Components of this latent vector are assumed to have a Laird–Ware structure. However, in place of their Gaussian assumptions, any specified class of multivariate distribution is allowed for the random effects, and the error vector is allowed to have any strictly positive pdf. A well known member of this class of models is the multivariate probit model with random effects. We investigate sufficient and necessary conditions for the existence of maximum likelihood estimates for the location and the association parameters. Implications of our results are illustrated through some hypothetical examples.     

Application of Multilevel Models to Structured Repeated Measurements

The linear mixed-effects model has been widely used for the analysis of continuous longitudinal data. This paper demonstrates that the linear mixed model can be adapted and used for the analysis of structured repeated measurements. A computational advantage of the proposed methodology is that there is no extra burden on the analyst since any software for linear mixed-effects models can be used to fit the proposed models. Two data sets from clinical psychology are used as motivating examples and to illustrate the methods.     

Classification-based model selection in retail demand forecasting

Retailers supply a wide range of stock keeping units (SKUs), which may differ for example in terms of demand quantity, demand frequency, demand regularity, and demand variation. Given this diversity in demand patterns, it is unlikely that any single model for demand forecasting can yield the highest forecasting accuracy across all SKUs. To save costs through improved forecasting, there is thus a need to match any given demand pattern to its most appropriate prediction model. To this end, we propose an automated model selection framework for retail demand forecasting. Specifically, we consider model selection as a classification problem, where classes correspond to the different models available for forecasting. We first build labeled training data based on the models’ performances in previous demand periods with similar demand characteristics. For future data, we then automatically select the most promising model via classification based on the labeled training data. The performance is measured by economic profitability, taking into account asymmetric shortage and inventory costs. In an exploratory case study using data from an e-grocery retailer, we compare our approach to established benchmarks. We find promising results, but also that no single approach clearly outperforms its competitors, underlying the need for case-specific solutions.     

Estimating the Competitive Storage Model with Trending Commodity Prices

We present a method to estimate jointly the parameters of a standard commodity storage model and the parameters characterizing the trend in commodity prices. This procedure allows the influence of a possible trend to be removed without restricting the model specification, and allows model and trend selection based on statistical criteria. The trend is modeled deterministically using linear or cubic spline functions of time. The results show that storage models with trend are always preferred to models without trend. They yield more plausible estimates of the structural parameters, with storage costs and demand elasticities that are more consistent with the literature. They imply occasional stockouts, whereas without trend the estimated models predict no stockouts over the sample period for most commodities. Moreover, accounting for a trend in the estimation implies price moments closer to those observed in commodity prices. Our results support the empirical relevance of the speculative storage model, and show that storage model estimations should not neglect the possibility of long‐run price trends. Copyright © 2016 John Wiley & Sons, Ltd.