Two-stage data envelopment analysis (2-DEA) is commonly used in productive efficiency analysis to estimate the effects of operational conditions and practices on performance. In this method the DEA efficiency estimates are regressed on contextual variables representing the operational conditions. We reexamine the statistical properties of the 2-DEA estimator, and find that it is statistically c...

The prediction of a response random interval-valued set from an explanatory one has been examined in previous developments. These developments have considered an interval arithmetic-based linear model between the random interval-valued sets and a least squares regression analysis. The least squares approach involves a generalized L2-metric between interval data; this metric weights squared dist...

We propose, theoretically investigate, and numerically validate an algorithm for the Monte Carlo solution of least-squares polynomial approximation problems in a collocation framework. Our investigation is motivated by applications in the collocation approximation of parametric functions, which frequently entails construction of surrogates via orthogonal polynomials. A standard Monte Carlo appr...

We discuss linear regression approaches to the estimation of law-invariant conditional risk measures. Two estimation procedures are considered and compared; one is based on residual analysis of the standard least-squares method, and the other is in the spirit of the M-estimation approach used in robust statistics. In particular, value-at-risk and average valueat-risk measures are discussed in d...

The iterative least squares method for estimating panel models with unobservable factor structure is extended to cover the case where the number of factors is unknown a priori. The proposed estimation algorithm optimizes a penalized least squares objective function to estimate the factor dimension jointly with the remaining model parameters in an iterative hierarchical order. Monte Carlo experi...

This paper studies the statistical behavior of the Affine Projection (AP) algorithm for μ = 1 for Gaussian Autoregressive inputs. This work extends the theoretical results of Rupp [3] to the numerical evaluation of the MSE learning curves for the adaptive AP weights. The MSE learning behavior of the AP(P+1) algorithm with an AR(Q) input (Q ≤ P) is shown to be the same as the NLMS algorithm (μ =...

This study examines the role of rock art in the construction of Woodland Period (300 BC to AD 1700) hunter-gatherer landscapes in the Lake of the Woods region of northwestern Ontario. The authors examine the distribution of documented pictograph sites relative to the locations of rock formations where the geologic conditions would have favored the placement of pictographic rock art but are abse...

In this paper we examine the asymptotic properties of the estimator of the long-run coefficient (LRC) in a dynamic regression model with integrated regressors and serially correlated errors. We show that the OLS estimators of the regression coefficients are inconsistent but the OLS-based estimator of the LRC is superconsistent. Furthermore, we propose an alternative consistent estimator of the ...

In this paper we extend the Least Squares Monte Carlo approach proposed by Longstaff and Schwartz for the valuation of American-style contingentclaims to the case of life insurance contracts. These contracts, in fact, often embed an American-style option, called surrender option, that entitles its owner to early terminate the contract and receive a cash amount, called surrender value. The addit...

In the presence of heteroscedasticity, OLS estimates are unbiased, but the usual tests of significance are inconsistent. However, tests based on a heteroscedasticity consistent covariance matrix (HCCM) are consistent. While most applications using a HCCM appear to be based on the asymptotic version of the HCCM, there are three additional, relatively unknown, small sample versions of the HCCM th...