Engel's Law Reconsidered

Engel's Law expresses a negative stochastic association of the bivariate distribution of income and food share across a population. Among the many different definitions which can be found in the statistical literature four concepts are discussed and tested: Kendall's τ, quadrant dependence, stochastic decreasing conditional food share distribution function and decreasing regression. Only the last one is used in the economic literature, yet it does not imply useful information of the underlying distribution. For linking Engels's Law to micro-economics, stronger concepts of stochastic association are needed. This motivates the empirical study of the proposed alternative concepts of negative association.     

A spatial dominance approach to evaluate the performance of stocks and bonds: Does the investment horizon matter?

The common advice by practitioners is to allocate a greater proportion of stocks for long-term investors than for short-term investors. However, part of the academic literature disagrees with this advice. We use a spatial dominance test which is suited for comparing alternative investments for a given time horizon. Using daily data for the US from 1962 to 2012, we test for dominance of cumulative returns series for stocks versus bonds at different investment horizons from 1 to 15 years. We find evidence that bonds second order spatially dominate stocks for horizons from 1 to 4 years. In contrast, for horizons of 6 years and longer, we find evidence that stocks dominate bonds. The conclusion that bonds dominate stocks at short horizons, while stocks dominate bonds at long horizons is consistent across different periods and international markets. When different portfolios of stocks and bonds are compared, we find that for long investment horizons only those portfolios with a sufficiently high proportion of stocks are efficient in the sense of spatial dominance.     

Chance Constrained Programming Formulations for Stochastic Characterizations of Efficiency and Dominance in DEA

Pareto-Koopmans efficiency in Data Envelopment Analysis (DEA) is extended to stochastic inputs and outputs via probabilistic input-output vector comparisons in a given empirical production (possibility) set. In contrast to other approaches which have used Chance Constrained Programming formulations in DEA, the emphasis here is on joint chance constraints. An assumption of arbitrary but known probability distributions leads to the P-Model of chance constrained programming. A necessary condition for a DMU to be stochastically efficient and a sufficient condition for a DMU to be non-stochastically efficient are provided. Deterministic equivalents using the zero order decision rules of chance constrained programming and multivariate normal distributions take the form of an extended version of the additive model of DEA. Contacts are also maintained with all of the other presently available deterministic DEA models in the form of easily identified extensions which can be used to formalize the treatment of efficiency when stochastic elements are present.     

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.     

Changes in risk and strategic interaction

In this paper we study the comparative statics of Nth degree stochastic dominance shifts in a large class of non-cooperative games. We consider symmetric equilibria as well as asymmetric equilibria in which the risk changes are idiosyncratic and not necessarily of the same stochastic order. Furthermore, we establish conditions for risk changes to produce multiplier effects on equilibrium strategies. Finally, we evaluate the comparative statics of stochastic dominance shifts in supermodular games, which may feature multiple equilibria and non-convex strategy sets.     

Estimation of the Option Prime: Microsimulation of Backward Stochastic Differential Equations

A mathematical statistical model is needed to obtain an option prime and create a hedging strategy. With formulas derived from stochastic differential equations, the primes for US Dollar/Chilean Pesos currency options using a prime calculator are obtained. Furthermore, a backward simulation of the option prime trajectory is used with a numerical method created for backward stochastic differential equations. The use of statistics in finance is highly important in order to develop complex products.     

Production Risk and Productivity Growth: Some Findings for Norwegian Salmon Aquaculture

This paper deals with estimation of primal panel data models of production risk, focusing on measurement of risk properties of inputs and productivity growth. Under production risk one should estimate technical change separately for the deterministic part and risk part of the technology, since risk averse producers will take into account both the mean and variance of output when they rank alternative technologies. For a panel of Norwegian salmon farms fish feed and fish input are found to increase output risk, while labor has a risk-decreasing effect on output. In the analysis of technical change by the first order stochastic dominance criterion the increase in mean output dominates the increase in output risk.     

Protection in numbers? Self-protection as a local public good

In many contexts with endogenous physical risks – e.g., households, neighbourhood traffic calming, production quality control – risk reduction is a local public good. Risk-reduction incentives then depend on the protected population’s size. Focusing on a household’s physical risks modelled as an i.i.d. Bernoulli trials sequence with endogenous “success” probability, I give sufficient conditions for safety to increase with the number protected via both monotone comparative statics methodology and a “first-order” approach. I utilise a recursive decomposition of a covariance involving a monotonic function of a binomial variable and first-degree stochastic dominance (FSD). Because “protection” problems are generally non-concave, I give a detailed treatment of the second-order condition, again via FSD.     

Poverty Orderings

This paper reviews the literature of partial poverty orderings. Partial poverty orderings require unanimous poverty rankings for a class of poverty measures or a set of poverty lines. The need to consider multiple poverty measures and multiple poverty lines arises inevitably from the arbitrariness inherent in poverty comparisons. In the paper, we first survey the ordering conditions of various individual poverty measures for a range of poverty lines; for some measures necessary and sufficient conditions are identified while for others only some easily verifiable sufficient conditions are established. These ordering conditions are shown to have a close link with the stochastic dominance relations which are based on the comparisons of cumulative distribution functions. We then survey the ordering conditions for various classes of poverty measures with a single or a set of poverty lines; in all cases necessary and sufficient conditions are established. These conditions again rely on the stochastic dominance relations or their transformations. We also extend the relationship between poverty orderings and stochastic dominance to higher orders and explore the possibility and the conditions of increasing the power of poverty orderings beyond the second degree dominance condition.     

(When) Do hub airlines internalize their self-imposed congestion delays?

Specification tests using stochastic bottleneck models of airport congestion investigate whether dominant airlines internalize or ignore self-imposed delays at twenty-seven major US airports. Data on flight times determine the airport's landing and takeoff delays for every minute of operation during peak travel days. Dynamic congestion functions based on stochastic-queuing theory separately identify delays that aircraft experience directly, impose internally on their airline's other aircraft, or impose externally on other airlines. Specification tests largely reject internalization and fail to reject non-internalization by dominant airlines. Optimal pricing should value all time using non-dominant aircraft time values and treat all delays as external.     

Interpreting and Testing the Scaling Property in Models where Inefficiency Depends on Firm Characteristics

Let u ≥ 0 be technical inefficiency, let z be a set of variables that affect u, and let δ be the parameters of this relationship. The model satisfies the scaling property if u(z, δ) can be written as a scaling function h(z, δ) times a random variable u* that does not depend on z. This property implies that changes in z affect the scale but not the shape of u(z,δ). This paper reviews the existing literature and identifies models that do and do not have the scaling property. It also discusses practical advantages of the scaling property. The paper shows how to test the hypothesis of scaling, and other interesting hypotheses, in the context of the model of Wang, Journal of Productivity Analysis, 2002. Finally, two empirical examples are given.     

On fitting the conditional difference asymmetry models to square contingency tables with nominal categories

In this paper, we implement the conditional difference asymmetry model (CDAS) for square tables with nominal categories proposed by Tomizawa et al. (J. Appl. Stat. 31(3): 271–277, 2004) with the use of the non-standard log-linear model formulation approach. The implementation is carried out by refitting the model in the 3 ×  3 table in (Tomizawa et al. J. Appl. Stat. 31(3): 271–277, 2004). We extend this approach to a larger 4 ×  4 table of religious affiliation. We further calculated the measure of asymmetry along with its asymptotic standard error and confidence bounds. The procedure is implemted with SAS PROC GENMOD but can also be implemented in SPSS by following the discussion in (Lawal, J. Appl. Stat. 31(3): 279–303, 2004; Lawal, Qual. Quant. 38(3): 259–289, 2004).