Efficient estimation of semiparametric conditional moment models with possibly nonsmooth residuals

This paper considers semiparametric efficient estimation of conditional moment models with possibly nonsmooth residuals in unknown parametric components (θ) and unknown functions (h) of endogenous variables. We show that: (1) the penalized sieve minimum distance (PSMD) estimator can simultaneously achieve root-n asymptotic normality of and nonparametric optimal convergence rate of , allowing for noncompact function parameter spaces; (2) a simple weighted bootstrap procedure consistently estimates the limiting distribution of the PSMD ; (3) the semiparametric efficiency bound formula of [Ai, C., Chen, X., 2003. Efficient estimation of models with conditional moment restrictions containing unknown functions. Econometrica, 71, 1795–1843] remains valid for conditional models with nonsmooth residuals, and the optimally weighted PSMD estimator achieves the bound; (4) the centered, profiled optimally weighted PSMD criterion is asymptotically chi-square distributed. We illustrate our theories using a partially linear quantile instrumental variables (IV) regression, a Monte Carlo study, and an empirical estimation of the shape-invariant quantile IV Engel curves.     

A note on the decomposition (at a point) of aggregate excess demand on the Grassmannian

This paper analyzes the properties of aggregate excess demand functions for economies with an arbitrary finite set of N commodities where agents face trading restrictions of a general, abstract form: their budget set is defined by K-dimensional planes in ^N. It is shown that, if there are at least K agents in the economy, the only general property satisfied by the value of aggregate excess demand and its derivative, at any arbitrary point, is Walras Law. The result is established by considering an economy where agents' preferences are of a ‘generalized Leontief' type.     

Efficiency Measurement With Unbalanced Panel Data: Evidence from Tunisian Textile,Clothing and Leather Industries

This paper is concerned with the estimation of stochastic frontier production functions with unbalanced panel data when unobservable firm efficiency levels are related to explanatory variables. We use the weighted-means Instrumental Variables method acknowledged to R. Gardner (1998) which is a modification of the Hausman-Taylor (1981) procedure adapted for unbalanced panel data. The estimation method is used to examine technical efficiency in Tunisian textile, clothing and leather (TCL) industries during the period 1983–1994. Further, we assume a Translog production frontier where input uses are expressed in efficiency units and adjusted for the age of capital and types of labor. Firm-specific time-invariant technical efficiency is obtained using Schmidt and Sickles (1984) approach. The results suggest that the Instrumental Variables method produces more accurate estimates of the unknown firm level technical efficency. Mean efficency scores resulting from the MHT method is of 66.5%.     

Validity of climate change forecasting for public policy decision making

Policymakers need to know whether prediction is possible and, if so, whether any proposed forecasting method will provide forecasts that are substantially more accurate than those from the relevant benchmark method. An inspection of global temperature data suggests that temperature is subject to irregular variations on all relevant time scales, and that variations during the late 1900s were not unusual. In such a situation, a “no change” extrapolation is an appropriate benchmark forecasting method. We used the UK Met Office Hadley Centre’s annual average thermometer data from 1850 through 2007 to examine the performance of the benchmark method. The accuracy of forecasts from the benchmark is such that even perfect forecasts would be unlikely to help policymakers. For example, mean absolute errors for the 20- and 50-year horizons were 0.18  C and 0.24  C respectively. We nevertheless demonstrate the use of benchmarking with the example of the Intergovernmental Panel on Climate Change’s 1992 linear projection of long-term warming at a rate of 0.03  C per year. The small sample of errors from ex ante projections at 0.03  C per year for 1992 through 2008 was practically indistinguishable from the benchmark errors. Validation for long-term forecasting, however, requires a much longer horizon. Again using the IPCC warming rate for our demonstration, we projected the rate successively over a period analogous to that envisaged in their scenario of exponential CO₂ growth—the years 1851 to 1975. The errors from the projections were more than seven times greater than the errors from the benchmark method. Relative errors were larger for longer forecast horizons. Our validation exercise illustrates the importance of determining whether it is possible to obtain forecasts that are more useful than those from a simple benchmark before making expensive policy decisions.     

Lower Bound Restrictions on Intensities in Data Envelopment Analysis

We propose an extension to the basic DEA models that guarantees that if an intensity is positive then it must be at least as large as a pre-defined lower bound. This requirement adds an integer programming constraint known within Operations Research as a Fixed-Charge (FC) type of constraint. Accordingly, we term the new model DEA_FC. The proposed model lies between the DEA models that allow units to be scaled arbitrarily low, and the Free Disposal Hull model that allows no scaling. We analyze 18 datasets from the literature to demonstrate that sufficiently low intensities—those for which the scaled Decision-Making Unit (DMU) has inputs and outputs that lie below the minimum values observed—are pervasive, and that the new model ensures fairer comparisons without sacrificing the required discriminating power. We explain why the low-intensity phenomenon exists. In sharp contrast to standard DEA models we demonstrate via examples that an inefficient DMU may play a pivotal role in determining the technology. We also propose a goal programming model that determines how deviations from the lower bounds affect efficiency, which we term the trade-off between the deviation gap and the efficiency gap.     

Effect of outliers on the estimation of parameters

Summary The estimation of parameter in the type of distributionf(x)=b x ^{–1 /b} exp (–x ^b (/b),x>0, is considered, when several outliers of the type , ,r=1,2, ...,k, are present in the data. The estimates of as well as of 's are put in the closed form. Special cases, Weibull, Gamma and Exponential are considered for the case of single outlier. Actual estimates are calculated from the generated samples of size 2 and 3 for the Weibull and Exponential.     

Is PPP sensitive to time-varying trade weights in constructing real effective exchange rates?

While the use of real effective exchange rates in stationarity tests of purchasing power parity (PPP) avoids the problems created using bilateral rates, these rates are often constructed using trade shares that are fixed at a single base year. This method fails to take into account the fact that trade shares can change drastically in parts of the world that are undergoing dramatic transformations. In this study, we apply linear as well as nonlinear stationarity tests to 52 currencies’ real effective rates, which were constructed using time-varying weights. Incorporating a time trend, we are also able to assess whether breakdowns in PPP are due to productivity differentials. We find that while nonlinear tests provide more evidence of “productivity bias” than do linear tests, they do not provide much more evidence of PPP. A comparison to a previous study that used fixed-weight data shows that there is somewhat more evidence of productivity bias using the new dataset, especially in Eastern Europe and Asia. We can conclude that PPP and a key cause of its breakdown are somewhat sensitive to the use of time-varying weights in these stationarity tests.     

Linear performance pricing: A collaborative tool for focused supply cost reduction

Supplier networks and the buyer/supplier relationships that comprise them are becoming increasingly important to effective supply chain management. Trust, collaboration, and efficient sharing of information are critical for true win/win relationships to surface in an environment where there is constant pressure to reduce costs and still maintain reasonable profitability. The use of across-the-board cost reduction demands and simple market clout may not always be the most effective approach in the long run. This paper describes linear performance pricing (LPP), a tool developed for a major automobile OEM in an attempt to effectively and efficiently provide more focused supply cost reductions. LPP is a data-driven methodology relying on a series of regression analyses that McKinsey and Company [2006. Automotive and assembly glossary. http://autoassembly.mckinsey.com/html/resources/glossary/1.asp (accessed 15.01.06)] describes as a “measurement tool that establishes a relationship between the value provided by a given part (performance) and its price.” We maintain that LPP facilitates a collaborative effort on the part of both the buyer and supplier and has the potential for leveraging the increased visibility of the buyer within the supplier network with respect to tier one or tier two suppliers. It helps by focusing cost reduction efforts of the tier one suppliers and provides lateral market visibility they may not have otherwise. Although widely used throughout the automotive industry in the US and Europe, little discussion of LPP is found in the literature. To promote a better understanding of LPP, we present a detailed example and discuss the managerial implications of the approach.     

Application of one‐step method to parameter estimation in ODE models

In this paper, we study application of Le Cam's one‐step method to parameter estimation in ordinary differential equation models. This computationally simple technique can serve as an alternative to numerical evaluation of the popular non‐linear least squares estimator, which typically requires the use of a multistep iterative algorithm and repetitive numerical integration of the ordinary differential equation system. The one‐step method starts from a preliminary ‐consistent estimator of the parameter of interest and next turns it into an asymptotic (as the sample size n →∞ ) equivalent of the least squares estimator through a numerically straightforward procedure. We demonstrate performance of the one‐step estimator via extensive simulations and real data examples. The method enables the researcher to obtain both point and interval estimates. The preliminary ‐consistent estimator that we use depends on non‐parametric smoothing, and we provide a data‐driven methodology for choosing its tuning parameter and support it by theory. An easy implementation scheme of the one‐step method for practical use is pointed out.     

Consistent estimation of a general nonparametric regression function in time series

We propose an estimator of the conditional distribution of X_t|X_t−1,X_t−2,…, and the corresponding regression function , where the conditioning set is of infinite order. We establish consistency of our estimator under stationarity and ergodicity conditions plus a mild smoothness condition.     

Feasible estimation of firm‐specific allocative inefficiency through Bayesian numerical methods

Both the theoretical and empirical literature on the estimation of allocative and technical inefficiency has grown enormously. To minimize aggregation bias, ideally one should estimate firm and input‐specific parameters describing allocative inefficiency. However, identifying these parameters has often proven difficult. For a panel of Chilean hydroelectric power plants, we obtain a full set of such parameters using Gibbs sampling, which draws sequentially from conditional generalized method of moments (GMM) estimates obtained via instrumental variables estimation. We find an economically significant range of firm‐specific efficiency estimates with differing degrees of precision. The standard GMM approach estimates virtually no allocative inefficiency for industry‐wide parameters. Copyright © 2009 John Wiley & Sons, Ltd.     

Estimating survivor function using optimally selected order statistics

This paper deals with the estimation of survivor function using optimally selected order statistics when the sample sizen is large. We use the estimates (μ*,σ*) based on the optimum set of order statistics for largen and fixedk (≤n) such that the estimate has optimum variance property. The asymptotic relative efficiency of such an estimator is compared with the one based on the complete sample. The general theory of the problem and specific details with respect to a two-parameter Normal, Logistic, Exponential and Pareto distributions is considered as an example.