The Measurement of Consumption Efficiency Considering the Discrete Choice of Consumers

In this study we propose a mathematical definition of the consumption efficiency of multi-attribute products in the price–quality space. A new model, the discrete Range Adjusted Measure (RAM) model, is suggested as an empirical tool to measure the level of consumption efficiency. We further discuss the effect of consumption efficiency on the innovation incentive. Empirical work is made for the mobile phone market. We expect that the consumption efficiency concept will contribute to the extension of the traditional framework of production efficiency analysis on the one hand and to the understanding of the nature of innovation in a technology-intensive market on the other hand.JEL Classification: C67, D11, D12, D21     

A new bounded log-linear regression model

In this paper we introduce a new regression model in which the response variable is bounded by two unknown parameters. A special case is a bounded alternative to the four parameter logistic model. The four parameter model which has unbounded responses is widely used, for instance, in bioassays, nutrition, genetics, calibration and agriculture. In reality, the responses are often bounded although the bounds may be unknown, and in that situation, our model reflects the data-generating mechanism better. Complications arise for the new model, however, because the likelihood function is unbounded, and the global maximizers are not consistent estimators of unknown parameters. Although the two sample extremes, the smallest and the largest observations, are consistent estimators for the two unknown boundaries, they have a slow convergence rate and are asymptotically biased. Improved estimators are developed by correcting for the asymptotic biases of the two sample extremes in the one sample case; but even these consistent estimators do not obtain the optimal convergence rate. To obtain efficient estimation, we suggest using the local maximizers of the likelihood function, i.e., the solution to the likelihood equations. We prove that, with probability approaching one as the sample size goes to infinity, there exists a solution to the likelihood equation that is consistent at the rate of the square root of the sample size and it is asymptotically normally distributed.     

Finite-sample exact tests for linear regressions with bounded dependent variables

We introduce tests for finite-sample linear regressions with heteroskedastic errors. The tests are exact, i.e., they have guaranteed type I error probabilities when bounds are known on the range of the dependent variable, without any assumptions about the noise structure. We provide upper bounds on probability of type II errors, and apply the tests to empirical data.     

Measurement errors and bounded OLS estimates

In a multiple linear regression model with one mismeasured independent variable, all coefficients are asymptotically biased. It is shown how in OLS, an examination of the sign of the cofactors of the variance-covariance matrix of measured values can be used to obtain large sample bounds on the coefficients. The method involves forward regression and regression on the mismeasured variable. Bounds are generally obtained on the coefficient of the mismeasured variable and often obtained on the remaining coefficients with no knowledge of the size of the measurement error.     

Some results on classifier selection with missing covariates

Methods are proposed to choose classifiers from a given collection of classifiers when there are missing covariates in the data. Two situations are considered: (i) the case where the new observation (to be classified) has no missing covariates and (ii) the case where the new observation is also allowed to have missing covariates. Using arguments from the empirical process theory, exponential performance bounds will be derived for the resulting classifiers. Such bounds, together with the Borel-Cantelli lemma, yield various strong consistency results.     

A Lower Bound for the Centered <Emphasis Type="Italic">L</Emphasis><Subscript>2</Subscript>-Discrepancy on Asymmetric Factorials and its Application

The role of uniformity measured by the centered L ₂-discrepancy (Hickernell 1998a) has been studied in fractional factorial designs. The issue of a lower bound for the centered L ₂-discrepancy is crucial in the construction of uniform designs. Fang and Mukerjee (2000) and Fang et al. (2002, 2003b) derived lower bounds for fractions of two- and three-level factorials. In this paper we report some new lower bounds for the centered L ₂-discrepancy for a set of asymmetric fraction factorials. Using these lower bounds helps to measure uniformity of a given design. In addition, as an application of these lower bounds, we propose a method to construct uniform designs or nearly uniform designs with asymmetric factorials.     

Confidence intervals for the quantile of treatment effects in randomized experiments

In this paper, we explore partial identification and inference for the quantile of treatment effects for randomized experiments. First, we propose nonparametric estimators of sharp bounds on the quantile of treatment effects and establish their asymptotic properties under general conditions. Second, we construct confidence intervals for the bounds and the true quantile by using the approach in Chernozhukov et al. (2009). Third, under additional conditions, we develop a new approach to construct confidence intervals for the bounds and the true quantile and refer to it as the order statistic approach. A simulation study is conducted to investigate the finite sample performance of both approaches.     

The dynamic analysis of continuous-time life-cycle savings growth models

Working with the framework of a continuous-time overlapping-generations model, this paper examines equilibrium growth paths (consistent with perfect foresight). In particular, we develop a methodology for characterizing the adjustment paths converging to new stationary states following exogenous shocks. The solution technique yields sequences of derivatives approximating the true dynamic paths and bounds for the errors of approximation. In practice, aggregating behavior over discrete-time intervals can save a great deal of effort — considerably enhancing the set of models feasible for analysis; our error bounds enable us to aggregate without compromising our results to an unknown degree.     

Multi-level and mixed-level <Emphasis Type="Italic">k</Emphasis>-circulant supersaturated designs

Supersaturated designs (SSDs) constitute an important class of fractional factorial designs that could be extremely useful in factor screening experiments. Most of the existing studies have focused on balanced designs. This paper provides a new lower bound for the \(E(f_{NOD})\)-optimality measure of SSDs with general run sizes. This bound is a generalization of existing bounds since it is applicable to both balanced and unbalanced designs. Optimal multi and mixed-level, balanced and nearly balanced SSDs are constructed by applying a k-circulant type methodology. Necessary and sufficient conditions are introduced for the generator vectors, in order to pre-ensure the optimality of the constructed k-circulant SSDs. The provided lower bounds were used to measure the efficiency of the generated designs. The presented methodology leads to a number of new families of improved SSDs, providing tools for directly constructing optimal or nearly-optimal k-circulant designs by just checking the corresponding generator vector.     

Modeling CRS bounded additive DEA models and characterizing their Pareto-efficient points

Dealing with weighted additive models in Data Envelopment Analysis guarantees that any projection of an inefficient unit belongs to the strong efficient frontier, among other interesting properties. Recently, constant returns to scale (CRS) range-bounded models have been introduced for defining a new additive-type efficiency measure (see Cooper et al. in J Prod Anal 35(2):85–94, 2011). This paper continues such earlier work further, considering a more general setting. In particular, we show that under free disposability of inputs and outputs, CRS bounded additive models require a double set of slacks. The second set of slacks allows us to properly characterize all the Pareto-efficient points associated to the bounded technology. We further introduce the CRS partially-bounded additive models.     

Sharp bounds on moments of generalized order statistics

Sharp lower and upper bounds on expected values of generalized order statistics are proven by the use of rearranged Moriguti's inequality. The method yields improvements of known quantile and moment bounds for expectations of order and record statistics based on independent identically distributed random variables. The bounds are attainable providing new characterizations of two-point distributions. Received: January 1999     

Bounding quantile demand functions using revealed preference inequalities

This paper develops a new approach to the estimation of consumer demand models with unobserved heterogeneity subject to revealed preference inequality restrictions. Particular attention is given to nonseparable heterogeneity. The inequality restrictions are used to identify bounds on counterfactual demand. A nonparametric estimator for these bounds is developed and asymptotic properties are derived. An empirical application using data from the UK Family Expenditure Survey illustrates the usefulness of the methods.     

Effective sub-simulation-free upper bounds for the Monte Carlo pricing of callable derivatives and various improvements to existing methodologies

We present a new non-nested approach for computing additive upper bounds for callable derivatives using Monte Carlo simulation. It relies on the regression of Greeks computed using adjoint methods. We also show that it is possible to early terminate paths once points of optimal exercise have been reached. A natural control variate for the multiplicative upper bound is introduced which renders it competitive to the additive one. In addition, a new bi-iterative family of upper bounds is introduced which takes a stopping time, an upper bound, and a martingale as inputs.     

Do the black market and the official exchange rates converge in the long run?

Establishing cointegration and long-run convergence between the official and the black market exchange rates is a way of testing foreign exchange market efficiency. Earlier research employed the Engle-Granger or Johansen method to test for cointegration between the two rates. Since either method requires each rate to be non-stationary, exchange rates that did not possess this property had to be excluded from analysis. However, with the introduction of the bounds testing approach to cointegration, no exchange rate needs to be excluded since this relatively new technique does not require pre-unit root testing. This paper employs monthly data from 27 developing countries and the bounds testing approach to cointegration by Pesaran et al. (2001) to show that in 22 out of 27 countries, not only are the two rates cointegrated but in all countries the black market exchange rate causes the official rate to adjust and converge toward the black market rate in the long run.     

从资本现金流量法看企业价值评估

针对目前自由现金流量法(FCF)和调整现值法(APV)在企业价值评估中所产生的问题,本文介绍了简单而直现的资 本现金流量法(CCF),并通过比较分析阐述了CCF在实际运用过程中的优越性。     

Treatment effect bounds: An application to Swan–Ganz catheterization

We reanalyze data from the observational study by Connors et al. (1996) on the impact of Swan–Ganz catheterization on mortality outcomes. The study by Connors et al. (1996) assumes that there are no unobserved differences between patients who are catheterized and patients who are not catheterized and finds that catheterization increases patient mortality. We instead allow for such differences between patients by implementing both the instrumental variable bounds of Manski (1990), which only exploits an instrumental variable, and the bounds of Shaikh and Vytlacil (2011), which exploit mild nonparametric, structural assumptions in addition to an instrumental variable. We propose and justify the use of indicators of weekday admission as an instrument for catheterization in this context. We find that in our application, the Manski (1990) bounds do not indicate whether catheterization increases or decreases mortality, where as the Shaikh and Vytlacil (2011) bounds reveal that at least for some diagnoses, Swan–Ganz catheterization reduces mortality at 7 days after catheterization. We show that the bounds of Shaikh and Vytlacil (2011) remain valid under even weaker assumptions than those described in Shaikh and Vytlacil (2011). We also extend the analysis to exploit a further nonparametric, structural assumption–that doctors catheterize individuals with systematically worse latent health–and find that this assumption further narrows these bounds and strengthens our conclusions. In our analysis, we construct confidence regions using the methodology developed in Romano and Shaikh (2008). We show in particular that the confidence regions are uniformly consistent in level over a large class of possible distributions for the observed data that include distributions where the instrument is arbitrarily “weak”.     

Sharp exponential and entropy bounds on expectations of generalized order statistics

Sharp lower and upper bounds on expected values of generalized order statistics are proven by the use of Moriguti's inequality combined with the Young inequality. The bounds are expressed in terms of exponential moments or entropy. They are attainable providing new characterizations of some nontrivial distributions. Received October 2001/Revised May 2002     

Bailing on the Car That Was Not Bailed Out: Bounding Consumer Reactions to Financial Distress

We examine how consumers react to the financial distress of durable goods manufacturers by studying the Swedish new car market. We employ a difference‐in‐differences matching methodology whereby we compare sales of carmaker Saab with those of a control group of substitute products. To account for possible substitution between products in the treatment and control groups, we propose and apply bounds to our difference‐in‐differences matching estimator. We then refine the bounds and provide conditions under which they depend only on product elasticities. We find that there was a significant decrease in the sales of Saab following its filing for administration.     

Testing for autoregressive against moving average errors in the linear regression model

This paper presents a new approach to hypotheses testing problems which are non-nested in the classical sense and which concern the covariance matrix of the disturbance vector of the linear regression model. In particular, the application of the approach to testing for AR(1) disturbances against MA(1) disturbances is explored in some detail. Practical difficulties are discussed and selected upper bounds for the test's five percent significance points are tabulated. The small sample power of four versions of the new test are compared empirically and a clear conclusion is made in regard to the best overall test.