Michigan State University

About: Michigan State University is a education organization based out in East Lansing, Michigan, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 60109 authors who have published 137074 publications receiving 5633022 citations. The organization is also known as: MSU & Michigan State.

What Are We Weighting For

Abstract: The purpose of this paper is to help empirical economists think through when and how to weight the data used in estimation. We start by distinguishing two purposes of estimation: to estimate population descriptive statistics and to estimate causal effects. In the former type of research, weighting is called for when it is needed to make the analysis sample representative of the target population. In the latter type, the weighting issue is more nuanced. We discuss three distinct potential motives for weighting when estimating causal effects: (1) to achieve precise estimates by correcting for heteroskedasticity, (2) to achieve consistent estimates by correcting for endogenous sampling, and (3) to identify average partial effects in the presence of unmodeled heterogeneity of effects. In each case, we find that the motive sometimes does not apply in situations where practitioners often assume it does. We recommend diagnostics for assessing the advisability of weighting, and we suggest methods for appropriate inference.

Fire science for rainforests

Abstract: Forest fires are growing in size and frequency across the tropics. Continually eroding fragmented forest edges, they are unintended ecological disturbances that transcend deforestation to degrade vast regions of standing forest, diminishing ecosystem services and the economic potential of these natural resources. Affecting the health of millions, net forest fire emissions may have released carbon equivalent to 41% of worldwide fossil fuel use in 1997-98. Episodically more severe during El Nino events, pan-tropical forest fires will increase as more damaged, less fire-resistant, forests cover the landscape. Here I discuss the current state of tropical fire science and make recommendations for advancement.

The Message in Daily Exchange Rates: A Conditional-Variance Tale

Abstract: Formal testing procedures confirm the presence of a unit root in the autoregressive polynomial of the univariate time series representation of daily exchange-rate data. The first differences of the logarithms of daily spot rates are approximately uncorrelated through time, and a generalized autoregressive conditional heteroscedasticity model with daily dummy variables and conditionally t-distributed errors is found to provide a good representation to the leptokurtosis and time-dependent conditional heteroscedasticity. The parameter estimates and characteristics of the models are found to be very similar for six different currencies. These apparent stylized facts carry over to weekly, fortnightly, and monthly data in which the degree of leptokurtosis and time-dependent heteroscedasticity is reduced as the length of the sampling interval increases.

Restitution of mass–size residuals: validating body condition indices

Abstract: Body condition can have important fitness consequences, but measuring body condition of live animals from wild populations has been the subject of much recent debate. Using the residuals from a regression of body mass on a linear measure of body size is one of the most common methods of measuring condition and has been used in many vertebrate taxa. Recently, the use of this method has been criticized because assumptions are likely violated. We tested several assumptions regarding the use of this method with body composition and morphometric data from five species of small mammals and with statistical simulations. We tested the assumptions that the relationship between body mass and body size is linear, and that the proportion of mass associated with energy reserves is independent of body size. In addition, we tested whether the residuals from reduced major axis (RMA) regression or major axis (MA) regression performed better than the residuals from ordinary least squares (OLS) regression as indices of body condition. We found no evidence of nonlinear relationships between body mass and body size. Relative energy reserves (fat and lean dry mass) were generally independent or weakly dependent on body size. Residuals from MA and RMA regression consistently explained less variation in body composition than OLS regression. Using statistical simulations, we compared the effects of violations of the assumption that true condition and residual indices are independent of body size on the OLS, MA, and RMA procedures and found that OLS performed better than the RMA and MA procedures. Despite recent criticisms of residuals from mass-size OLS regressions, these indices of body condition appear to satisfy critical assumptions. Although some caution is warranted when using residuals, especially when both inter- individual variation in body size and measurement error are high, we found no reason to reject OLS residuals as legitimate indices of body condition.