The Adverse Impact of Temperature on Income

Crop Production/Industries, Risk and Uncertainty,

XLR: A free Excel add-in for introductory business statistics

XLR is an Excel add-in that unifies the user friendly, widely popular interface of Excel with the powerful and robust computational capability of the GNU statistical and graphical language R. The add-in attempts to address the American Statistical Association’s comment that “Generic packages such as Excel are not sufficient even for the teaching of sta- tistics, let alone for research and consulting.” R is the program of choice for researchers in statistical methodology that is freely available under the Free Software Foundation’s GNU General Public License (GPL) Agreement. By wedding the interactive mode of Excel with the power of statistical computing of R, XLR provides a solution to the problem of numerical inaccuracy of using Excel and its various internal statistical functions and procedures by harnessing the computational power of R. XLR will be distributed under the GNU GPL Agreement. The GPL puts students, instructors and researchers in control of their usage of the software by providing them with the freedom to run, copy, distribute, study, change and improve the software, thus, freeing them from the bondage of proprietary software. The creation of XLR will not only have a significant impact on the teaching of an Introductory Business Statistics course by providing a free alternative to the commercial proprietary software but also provide researchers in all disciplines who require so- phisticated and cutting edge statistical and graphical procedures with a user-friendly interactive data analysis tool when the current set of available commands is expanded to include more advance procedures

Computing Cox's smoothing spline score estimator: Working paper series--03-07

We provide an efficient algorithm for computing the smoothing spline score estimator of Cox (1985). The algorithm exploits the banded structure of the linear algebra involved. Calls are made to the LAPACK, Level 1, 2, and 3 BLAS subroutine libraries designed to be efficient on a wide range of modern high-performance computers

Using quantile regression to evaluate human thermal climates in China: Working paper series--08-09

Studies have been conducted to examine the spatial variations of human thermal comfort in various countries using different comfort indices. These human thermal climate studies have important implications on human health, migration patterns, retirement decisions, tourism development and energy requirements. Yan (2005) used a clothing insulation (CLO) index to construct average clothing needs in various regions of China. These average CLO maps, however, only provided information on the center of the distribution of climate variation. Using quantile regressions, we estimated index and constructed contour maps for the whole spectrum of the CLO distribution to provide additional information on the spread and variation of the clothing requirements and, hence, a more complete picture of the human comfort of the various regions in China

Quantile regression analysis of visitor spending: An example of mainland Chinese tourists in Hong Kong: Working paper series--09-06

A common approach to market segmentation based on visitor expenditures is to use the least-squares regression analysis to determine statistically significant variables upon which key market segments are identified for marketing purposes. This was done by Wang (2004) for survey data based on expenditures by Mainland Chinese visitors to Hong Kong. We use this same dataset to demonstrate the benefits of using the quantile regression analysis approach to better identify tourist spending patterns and market segments. The quantile regression measures tourist spending in different categories against a fixed range of dependent variable, which distinguished between lower, medium, and higher spenders. The results show that quantile regression is less susceptible to influence by outlier values and is better able to target finer tourist spending market segments

Robust tests for heteroskedasticity and autocorrelation in the multiple regression model: Working paper series--02-05

The standard Rao's (1948) score or Lagrange multiplier test for heteroskedasticity was originally developed assuming normality of the disturbance term [see Godfrey (1978b), and Bruesch and Pagan (1979)]. Therefore, the resulting test depends heavily on the normality assumption. Koenker (1981) suggests a studentized for which is robust to nonnormality. This approach seems to be limited because of the unavailability of a general procedure that transforms a test to a robust one. Following Bickel (1978), we use a different approach to take account of nonnormality. Our tests will be based on the score function which is defined as the negative derivitive of the log-density function with respect to the underlying random variable. To implement the test we use a nonparametric estimate of the score function. Our robust test for heteroskedasticity is obtained by running a regression of the product of the score function and ordinary least squares residuals on some exogenous variables which are thought to be causing the heteroskedasticity. We also use our procedure to develop a robust test for autocorrelation which can be computed by regressing the score function on the lagged ordinary least squares residuals and the independent variables. Finally, we carry out an extensive Monte Carlo study which demonstrates that our proposed tests have superior finite sample properties compared to the standard tests

The elasticity of demand for gasoline: A semi-parametric analysis: Working paper series--02-33

We use a semi-parametric conditional median as a robust alternative to the parametric conditional mean to estimate the gasoline demand function. Our approach protects against data and specification errors and may yield a more reliable basis for public policy decisions that depend on accurate estimates of gasoline demand. As a comparison, we also estimated the parametric translog conditional mean model. Our semi-parametric estimates imply that gasoline demand becomes more price elastic, but also less income elastic, as incomes rise. In addition, we find that demand appears to become more price elastic as prices increase in real terms

Mincer-zarnovitz quantile and expectile regressions for forecast evaluations under asymmetric loss functions: Working paper series--14-01

Forecast is pervasive in all areas of applications in business and daily life and, hence, evaluating the accuracy of a forecast is important for both the generators and consumers of forecasts. There are two aspects in forecast evaluation: (1) measuring the accuracy of past forecasts using some summary statistics and (2) testing the optimality properties of the forecasts through some diagnostic tests. On measuring the accuracy of a past forecast, we illustrate that the summary statistics used should match the loss function that was used to generate the forecasts. If there is strong evidence that an asymmetric loss function has been used in the generation of a forecast, then a summary statistic that corresponds to that asymmetric loss function should be used in assessing the accuracy of the forecast instead of the popular RMSE or MAE. On testing the optimality of the forecasts, we demonstrate how the quantile regressions and expectile regressions set in the prediction-realization framework of Mincer and Zarnowitz (1969) can be used to recover the unknown parameter that controls the potentially asymmetric loss function used in generating the past forecasts. Finally, we apply the prediction-realization framework to the Federal Reserve's economic growth forecast and forecast sharing in a PC manufacturing supply chain. We find that the Federal Reserves values over prediction approximately 1.5 times more costly than under prediction. We also find that the PC manufacturer weighs positive forecast errors (under forecasts) about four times as costly as negative forecast errors (over forecasts)

Developing professionalism in a college of business: The implementation of a professionalism recognition program: Working paper series--11-06

This paper explores the importance of developing professionalism attributes among business students and the implementation of a program designed to incentivize professionalism behaviors during undergraduate study at a college of business. The "Professionalism Recognition Program" (PRP) was established as a co-curricular activity to promote, evaluate, recognize and reward professionalism behaviors of students. We also describe the key aspects of the program's development and implementation, noting the key resources and constituencies involved as well as considerations for the adoption of similar programs elsewhere. It is hoped that the lessons learned during our implementation and communicated in this manuscript will help others to successfully develop and implement their own programs to improve the professionalism behaviors of students

An Adaptive Observer-based Robust Estimator of Multi-sinusoidal Signals

This paper presents an adaptive observer-based robust estimation methodology of the amplitudes, frequencies and phases of biased multi-sinusoidal signals in presence of bounded perturbations on the measurement. The parameters of the sinusoidal components are estimated on-line and the update laws are individually controlled by an excitation-based switching logic enabling the update of a parameter only when the measured signal is sufficiently informative. This way doing, the algorithm is able to tackle the problem of over-parametrization (i.e., when the internal model accounts for a number of sinusoids that is larger than the true spectral content) or temporarily fading sinusoidal components. The stability analysis proves the existence of a tuning parameter set for which the estimator\u2019s dynamics are input-to-state stable with respect to bounded measurement disturbances. The performance of the proposed estimation approach is evaluated and compared with other existing tools by extensive simulation trials and real-time experiments