These moments of the asymptotic distribution of the least-squares estimator of the local-to-unity autoregressive model are computed using computationally simple integration. These…
Abstract
These moments of the asymptotic distribution of the least-squares estimator of the local-to-unity autoregressive model are computed using computationally simple integration. These calculations show that conventional simulation estimation of moments can be substantially inaccurate unless the simulation sample size is very large. We also explore the minimax efficiency of autoregressive coefficient estimation, and numerically show that a simple Stein shrinkage estimator has minimax risk which is uniformly better than least squares, even though the estimation dimension is just one.
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Venkatesh Kodur, James Stein, Rustin Fike and Mahmood Tabbador
This paper aims to present an evaluation of comparative fire resistance on traditional and engineered wood joists used in the construction of floor systems in residential housing.
Abstract
Purpose
This paper aims to present an evaluation of comparative fire resistance on traditional and engineered wood joists used in the construction of floor systems in residential housing.
Design/methodology/approach
Fire resistance experiments were carried out on four types of wood joists, namely, traditional lumber, engineered I-joist, castellated I-joist and steel/wood hybrid joist, used in traditional and modern residential construction. The test variables included type of wood joist, support conditions and fire protection (insulation).
Findings
Results from these tests indicate that webs of engineered I-joists and castellated I-joists are highly susceptible to fire, and failure generally occurs through the burn-out of the web. In addition, engineered I-joists have much lower fire resistance than traditional solid joist lumber. The application of an intumescent coating on an engineered I-joist significantly enhances its fire resistance and yields a similar level of fire resistance as that of a traditional lumber joist.
Originality/value
The presented fire tests are unique and provide valuable insight (and information) to the behavior and response of four types of wood joists when subjected to gravity loading and fire conditions.
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Abstract
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George G. Judge and Ron C. Mittelhammer
In the context of competing IV econometric models and estimators, we demonstrate a semiparametric Stein-like estimator (SSLE) that, under quadratic loss, has superior risk…
Abstract
In the context of competing IV econometric models and estimators, we demonstrate a semiparametric Stein-like estimator (SSLE) that, under quadratic loss, has superior risk performance. The method eliminates the need for pretesting to decide whether covariate endogeneity is present and makes use of a pretest estimator choice between IV and non-IV methods unnecessary. A sampling study is used to illustrate finite sample performance over a range of sampling designs, including its performance relative to pretest estimators. An important applied problem from the literature is analyzed to indicate possible applied implications and the relation of SSLE to other modern IV estimators.
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Eric Hillebrand and Tae-Hwy Lee
We examine the Stein-rule shrinkage estimator for possible improvements in estimation and forecasting when there are many predictors in a linear time series model. We consider the…
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We examine the Stein-rule shrinkage estimator for possible improvements in estimation and forecasting when there are many predictors in a linear time series model. We consider the Stein-rule estimator of Hill and Judge (1987) that shrinks the unrestricted unbiased ordinary least squares (OLS) estimator toward a restricted biased principal component (PC) estimator. Since the Stein-rule estimator combines the OLS and PC estimators, it is a model-averaging estimator and produces a combined forecast. The conditions under which the improvement can be achieved depend on several unknown parameters that determine the degree of the Stein-rule shrinkage. We conduct Monte Carlo simulations to examine these parameter regions. The overall picture that emerges is that the Stein-rule shrinkage estimator can dominate both OLS and principal components estimators within an intermediate range of the signal-to-noise ratio. If the signal-to-noise ratio is low, the PC estimator is superior. If the signal-to-noise ratio is high, the OLS estimator is superior. In out-of-sample forecasting with AR(1) predictors, the Stein-rule shrinkage estimator can dominate both OLS and PC estimators when the predictors exhibit low persistence.
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Bai Huang, Tae-Hwy Lee and Aman Ullah
This chapter examines the asymptotic properties of the Stein-type shrinkage combined (averaging) estimation of panel data models. We introduce a combined estimation when the fixed…
Abstract
This chapter examines the asymptotic properties of the Stein-type shrinkage combined (averaging) estimation of panel data models. We introduce a combined estimation when the fixed effects (FE) estimator is inconsistent due to endogeneity arising from the correlated common effects in the regression error and regressors. In this case, the FE estimator and the CCEP estimator of Pesaran (2006) are combined. This can be viewed as the panel data model version of the shrinkage to combine the OLS and 2SLS estimators as the CCEP estimator is a 2SLS or control function estimator that controls for the endogeneity arising from the correlated common effects. The asymptotic theory, Monte Carlo simulation, and empirical applications are presented. According to our calculation of the asymptotic risk, the Stein-like shrinkage estimator is more efficient estimation than the CCEP estimator.
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THE CLASSLESS STATE, considered by many to be socially ideal, is only achieved in the world of books by fiction. Rivalling the marketing boards in zeal, librarians passionately…
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THE CLASSLESS STATE, considered by many to be socially ideal, is only achieved in the world of books by fiction. Rivalling the marketing boards in zeal, librarians passionately mark down eggs to the fifth place after the decimal (641.665 13) and, deaf to the entreaties of geographers, callously separate Mother Earth from her children. Why, then, should the most individualistic form of writing enjoy a sequestered Stellen‐bosch? Is P. G. Wodehouse neither fish, flesh, nor good red herring that he must be denied a number—a privilege accorded him even in the concentration camp? Must Harriet Beecher Stowe rub shoulders with Gertrude Stein and James Janeway doss down with Jerome K. Jerome? It is hard luck for squares, who have moved in such different circles, to have to toe the party line, when, being immortal, they might reasonably expect to be given the freedom of the Heavenly City.
George G. Judge and Ron C. Mittelhammer
In the context of competing theoretical economic–econometric models and corresponding estimators, we demonstrate a semiparametric combining estimator that, under quadratic loss…
Abstract
In the context of competing theoretical economic–econometric models and corresponding estimators, we demonstrate a semiparametric combining estimator that, under quadratic loss, has superior risk performance. The method eliminates the need for pretesting to decide between members of the relevant family of econometric models and demonstrates, under quadratic loss, the nonoptimality of the conventional pretest estimator. First-order asymptotic properties of the combined estimator are demonstrated. A sampling study is used to illustrate finite sample performance over a range of econometric model sampling designs that includes performance relative to a Hausman-type model selection pretest estimator. An important empirical problem from the causal effects literature is analyzed to indicate the applicability and econometric implications of the methodology. This combining estimation and inference framework can be extended to a range of models and corresponding estimators. The combining estimator is novel in that it provides directly minimum quadratic loss solutions.
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Without short-sales constraints, mean-variance (MV) and power-utility portfolios generated from historical data are often characterized by extreme expected returns, standard…
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Without short-sales constraints, mean-variance (MV) and power-utility portfolios generated from historical data are often characterized by extreme expected returns, standard deviations, and weights. The result is usually attributed to estimation error. I argue that modeling error, that is, modeling the portfolio problem with just a budget constraint, plays a more fundamental role in determining the extreme solutions and that a more complete analysis of MV problems should include realistic constraints, estimates of the means based on predictive variables, and specific values of investors’ risk tolerances. Empirical evidence shows that investors who utilize MV analysis without imposing short-sales constraints, without employing estimates of the means based on predictive variables, and without specifying their risk tolerance miss out on remarkably remunerative investment opportunities.
This chapter explores how neoliberal higher education reforms in the United Republic of Tanzania (URT) during the 1990s and 2000s were shaped by the history of governance…
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This chapter explores how neoliberal higher education reforms in the United Republic of Tanzania (URT) during the 1990s and 2000s were shaped by the history of governance, schooling, and foreign donor involvement in the country following its independence in 1961. Against this backdrop, I examine how concepts of private versus public leadership, individualism, competition, and education’s place in the overall development scheme shifted over time, and the influence these changing conceptualizations had on the role of universities in Tanzania by the end of the first decade of the 21st century. In an international environment in which powerful funding agencies see neoliberal higher education policies and “knowledge societies” as the key to increased national competitiveness and poverty eradication in sub-Saharan Africa, this chapter shows how changes embedded in recent market-centered university reforms – in which the state is said to “steer” rather than “row” – have influenced the quest for equitable development.