On the distribution of city sizes

The city size distribution in many countries is remarkably well described by a Pareto distribution. We derive conditions that standard urban models must satisfy in order to explain this regularity. We show that under general conditions urban models must have (i) a balanced growth path and (ii) a Pareto distribution for the underlying source of randomness. In particular, one of the following combinations can induce a Pareto distribution of city sizes: (i) preferences for different goods follow reflected random walks, and the elasticity of substitution between goods is 1; or (ii) total factor productivities of different goods follow reflected random walks, and increasing returns are equal across goods.     

An equilibrium model of city size

This paper develops an equilibrium model of city size in which population distribution, real prices, and real wages are determined. Two possible modifications are considered, one involving externalities. The partial equilibrium changes resulting from these modifications are derived and seen to be quite different from the general equilibrium changes. Finally, the paper suggests that site-specific factors of production must be introduced into any consistent model of city size.     

An efficient estimator using auxiliary information

The use of ratio and product methods of estimation using auxiliary information for estimating the mean of a finite population is well known.Srivastava [1967] andReddy [1973] proposed ratio-cum-product type estimators. This paper proposes a transformed estimator which is even more efficient than these estimators for a wide range of the value of the correlation coefficient between the main and auxiliary variables.     

An algorithm for winning coalitions in indirect control of corporations

Decisions in Economics and Finance - We describe in this paper a variance reduction method based on control variates. The technique uses the fact that, if all stochastic assets but one are replaced...     

An efficient GMM estimator of spatial autoregressive models

In this paper, we consider GMM estimation of the regression and MRSAR models with SAR disturbances. We derive the best GMM estimator within the class of GMM estimators based on linear and quadratic moment conditions. The best GMM estimator has the merit of computational simplicity and asymptotic efficiency. It is asymptotically as efficient as the ML estimator under normality and asymptotically more efficient than the Gaussian QML estimator otherwise. Monte Carlo studies show that, with moderate-sized samples, the best GMM estimator has its biggest advantage when the disturbances are asymmetrically distributed. When the diagonal elements of the spatial weights matrix have enough variation, incorporating kurtosis of the disturbances in the moment functions will also be helpful.     

An efficient variant of the product and ratio estimators

Abstract  This article presents a variant of the usual ratio and product methods of estimation, with the intention 10 improve their efficiency. The first order large sample approximations to the bias and the mean square error of the proposed estimator are obtained and compared with those of the well-known methods (simple expansion, ratio, product, difference and linear regression methods). For a special case, the accuracy of the first order approximation (terms up to the order n-¹ ) is examined by including terms upto the order n^-2 . With suitable choice of a design parameter, the proposed estimator turns out to be superior to the three methods mentioned first. The relation to the other two methods is examined; if the design parameter can be chosen near to the optimal value, the proposed method is seen to be approximately as efficient as the linear regression estimator. Finally some extensions are indicated.     

An improved bootstrap test of stochastic dominance

We propose a new method of testing stochastic dominance that improves on existing tests based on the standard bootstrap or subsampling. The method admits prospects involving infinite as well as finite dimensional unknown parameters, so that the variables are allowed to be residuals from nonparametric and semiparametric models. The proposed bootstrap tests have asymptotic sizes that are less than or equal to the nominal level uniformly over probabilities in the null hypothesis under regularity conditions. This paper also characterizes the set of probabilities so that the asymptotic size is exactly equal to the nominal level uniformly. As our simulation results show, these characteristics of our tests lead to an improved power property in general. The improvement stems from the design of the bootstrap test whose limiting behavior mimics the discontinuity of the original test’s limiting distribution.     

An efficient implementation of the Wagner-Whitin algorithm for dynamic lot-sizing

We consider an N-period planning horizon with known demands D_t ordering cost A_t, procurement cost, C_t and holding cost H_t in period t. The dynamic lot-sizing problem is one of scheduling procurement Q_t in each period in order to meet demand and minimize cost.The Wagner-Whitin algorithm for dynamic lot sizing has often been misunderstood as requiring inordinate computational time and storage requirements. We present an efficient computer implementation of the algorithm which requires low core storage, thus enabling it to be potentially useful on microcomputers.The recursive computations can be stated as follows:

$\text{M}{}_{\mathrm{jk}}\text{=A}{}_{\mathrm{j}}\text{+C}{}_{\mathrm{j}}\text{Q}{}_{\mathrm{j}}\text{+}\text{∑}\text{k?1}\text{t=j}\text{H}{}_{\mathrm{t}}\text{∑}\text{k}\text{r=t+1}\text{D}{}_{\mathrm{r}}$

$\text{F}{}_{\mathrm{k}}\text{=}\text{min}\text{1?j?k}\text{[F}{}_{\mathrm{j}}\text{+M}{}_{\mathrm{jk}}\text{];}\text{F}{}_0\text{=0}$

where M_jk is the cost incurred by procuring in period j for all periods j through k, and F_k is the minimal cost for periods 1 through k. Our implementation relies on the following observations regarding these computations:

$\text{M}{}_{\mathrm{j,k}}\text{=A}{}_{\mathrm{j}}\text{+C}{}_{\mathrm{j}}\text{D}{}_{\mathrm{j}}$

$\text{M}{}_{\mathrm{j,k+1}}\text{=M}{}_{\mathrm{jk}}\text{+D}{}_{\mathrm{k+1}}\text{(C}{}_{\mathrm{j}}\text{+}\text{∑}\text{k}\text{t=j}\text{H}{}_{\mathrm{t}}\text{,}\text{k?j}$

Using this recursive relationship, the number of computations can be greatly reduced. Specifically, $\text{3}\text{2}$N² ? $\text{1}\text{2}$N² additions and $\text{1}\text{2}$N² + $\text{1}\text{2}$N multiplications are required. This is insensitive to the data.A FORTRAN implementation on an Amdahl 470 yielded computation times (in 10^?3 seconds) of T = ?.249 + .0239N + .00446N². Problems with N = 500 were solved in under two seconds.     

An outlier test for linear processes

In this paper we introduce an outlier test for linear processes. It is assumed that an upper bound for the number of outliers is known which is not too big in relation to the sample size. The test statistic bases on the comparison of the observations with certain predictors. We discuss the asymptotical behaviour of the test statistic under the null hypothesis ‘no outlier’ and derive the asymptotic distribution for the case that the distribution of the squared white noise process belongs to a certain subset of the domain of attraction of the Gumbel distribution. Especially the most important case in applications, the Gaussian white noise is included.     

An efficient binomial method for pricing¶American options

We present a new method for obtaining fast and accurate estimates of the price of an American put option by binomial trees. The method is based on the interpolation of suitable values obtained by modifying the contractual strike. A time-saving procedure allows us to derive all the interpolating data from a unique standard backward procedure. Received: 16 July 2001 / Accepted: 19 April 2002 {The authors would like to thank an anonymous referee for helpful comments. We also thank Antonino Zanette for his help in the refinements of the numerical procedures.     

An efficient nonparametric estimator for models with nonlinear dependence

We provide a convenient econometric framework for the analysis of nonlinear dependence in financial applications. We introduce models with constrained nonparametric dependence, which specify the conditional distribution or the copula in terms of a one-dimensional functional parameter. Our approach is intermediate between standard parametric specifications (which are in general too restrictive) and the fully unrestricted approach (which suffers from the curse of dimensionality). We introduce a nonparametric estimator defined by minimizing a chi-square distance between the constrained densities in the family and an unconstrained kernel estimator of the density. We derive the nonparametric efficiency bound for linear forms and show that the minimum chi-square estimator is nonparametrically efficient for linear forms.     

Optimal and efficient repeated measurements designs for comparing test treatments with a control

Rearranging the sequence of test treatments and control treatment within each block of an A-optimal balanced treatment block design such that certain conditions are satisfied, the resulting design is an A-optimal repeated measurements designs when blocks are regarded as units or periods. The efficiencies of designs which are obtained from universally optimal repeated measurements designs with test treatments only by changing some treatment labels into control treatment are given.     

An experimental test of behavior under team production

This study reports experiments that examine behavior under team production and a piece rate. In the experiments, participants complete a forecasting task and are rewarded based on the accuracy of their forecasts. In the piece‐rate condition, participants are paid based on their own performance, whereas the team‐production condition rewards participants based on the average performance of the team. Overall, there is no statistically significant difference in performance between the conditions. However, this result masks important differences in the behavior of men and women across the conditions. Men in the team‐production condition increase their performance relative to men in the piece‐rate condition. However, this gap in male performances across conditions diminishes over the course of the experiment. In contrast, women in the team‐production condition show significantly lower performance than the women in the piece rate. As a consequence of these differences, men in the team‐production condition show significantly better performance than women in the team‐production condition. We also find evidence that men show stronger performance when they are in teams with a larger variation in skill level. Copyright © 2010 John Wiley & Sons, Ltd.     

Conceptualizing academic-entrepreneurial intentions: An empirical test

Policy makers are increasingly recognizing the catalytic role of academics’ spin-off companies in a national economy, which derives from their innovativeness that result in new value generation, and job creation. Although research on academics’ spin-off companies has been increasing, knowledge gaps exist as to the specific determinants and processes that characterize the emergence of academics’ entrepreneurial intentions that lead them to spin off companies. This research aims to fill this gap. Drawing from psychological and entrepreneurship research on intentionality, the authors propose a conceptual model of academics’ entrepreneurial intentions. They empirically test the model using structural equation modeling and a robust data set collected in two European academic settings to guide future research on this important topic.     

An automatic Portmanteau test for serial correlation

This article introduces a data-driven Box–Pierce test for serial correlation. The proposed test is very attractive compared to the existing ones. In particular, implementation of this test is extremely simple for two reasons: first, the researcher does not need to specify the order of the autocorrelation tested, since the test automatically chooses this number; second, its asymptotic null distribution is chi-square with one degree of freedom, so there is no need of using a bootstrap procedure to estimate the critical values. In addition, the test is robust to the presence of conditional heteroskedasticity of unknown form. Finally, the proposed test presents higher power in simulations than the existing ones for models commonly employed in empirical finance.     

An extremal quotient test for exponential distributions

Summary A procedure based on the extremal quotient is proposed for testing the null hypothesis H ₀: the population of the sample has an exponential distribution against H _a : it does not have an exponential distribution.The proposed procedure is a nonparametric test which could lead to an early decision for the rejection ofH ₀     

An unbiased model comparison test using cross-validation

Social scientists often consider multiple empirical models of the same process. When these models are parametric and non-nested, the null hypothesis that two models fit the data equally well is commonly tested using methods introduced by Vuong (Econometrica 57(2):307–333, 1989) and Clarke (Am J Political Sci 45(3):724–744, 2001; J Confl Resolut 47(1):72–93, 2003; Political Anal 15(3):347–363, 2007). The objective of each is to compare the Kullback–Leibler Divergence (KLD) of the two models from the true model that generated the data. Here we show that both of these tests are based upon a biased estimator of the KLD, the individual log-likelihood contributions, and that the Clarke test is not proven to be consistent for the difference in KLDs. As a solution, we derive a test based upon cross-validated log-likelihood contributions, which represent an unbiased KLD estimate. We demonstrate the CVDM test’s superior performance via simulation, then apply it to two empirical examples from political science. We find that the test’s selection can diverge from those of the Vuong and Clarke tests and that this can ultimately lead to differences in substantive conclusions.     

Assessing French nursing home efficiency: An indirect approach via budget-constrained DEA models

In this paper, data envelopment analysis (DEA) techniques are applied to the French nursing home industry in order to address two policy issues. The first involves nursing home size and returns to scale, while the second deals with the potential effects of a change in nursing home reimbursement from a flat rate to one based on the severity of case-mix. To accomplish this, our analysis expands on the existing nursing home literature to analyze technical and allocative efficiency along with budget-constrained models rather than the more common direct input-based distance function. Technical efficiency is evaluated via an indirect output distance function while allocative output efficiency is computed with a cost indirect revenue function. The findings suggest that system-wide efficiency and equity may result from coming reforms since payments would more accurately reflect resource use.