A computationally practical simulation estimation algorithm for dynamic panel data models with unobserved endogenous state variables

This paper develops a new simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. The new approach can deal with the commonly encountered and widely discussed ``initial conditions problem,'' as well as the more general problem of missing state variables at any point during the sample period. Repeated sampling experiments on a dynamic panel data probit model with serially correlated errors indicate that the estimator has good small sample properties and is computationally practical for use with panels of the size that are likely to be encountered in practice. <br><br> Keywords; initial conditions, missing data, discrete choice, simulation estimation

A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables

This paper develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. The new approach can easily deal with the commonly encountered and widely discussed “initial conditions problem,” as well as the more general problem of missing state variables during the sample period. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate that the estimator has good small sample properties. We apply the estimator to a model of married women’s labor force participation decisions. The results show that the rarely used Polya model, which is very difficult to estimate given missing data problems, fits the data substantially better than the popular Markov model. The Polya model implies far less state dependence in employment status than the Markov model. It also implies that observed heterogeneity in education, young children and husband income are much more important determinants of participation, while race is much less important.Initial Conditions, Missing Data, Simulation, Female Labor Force Participation Decisions

A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables

This paper develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. The new approach can easily deal with the commonly encountered and widely discussed "initial conditions problem," as well as the more general problem of missing state variables during the sample period. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate that the estimator has good small sample properties. We apply the estimator to a model of married women's labor force participation decisions. The results show that the rarely used Polya model, which is very difficult to estimate given missing data problems, fits the data substantially better than the popular Markov model. The Polya model implies far less state dependence in employment status than the Markov model. It also implies that observed heterogeneity in education, young children and husband income are much more important determinants of participation, while race is much less important.simulation, missing data, initial conditions, female labor force participation

Doctor Anderson!

Fiction by John Keane and Robert Krus

Student Evaluations of Social Media in a University Course

Although university students use social media such as blogs, Twitter, instant messaging, text messaging, and Facebook to communicate with each other, the number of these tools used in the context of their coursework is more limited. In this qualitative study we investigate student evaluations of the use of social media in a university course. Students in an upper level university class that employed social media were asked to reflect on their use of social media in the class. Most report that they found the tools useful, either for their personal productivity or as training tools. We draw implications for the use of social media in university instruction from these finding

An Integrated Study Investigating Masticated Fuel Treatments in the Rocky Mountains

Many coniferous forests in the western US once supported frequent, low intensity fires, but due to a century of fire exclusion and other factors, severe wildfires have now become common. With the goal of lowering fire intensities and severities, one possible fuel treatment that is currently gaining favor in with many land managers is mastication which breaks, shreds, or grinds canopy (seedlings, saplings and pole trees) and surface fuel (fine and coarse woody material) into smaller sizes and deposits the treated fuel on the ground. However, very little is known concerning the effects of this treatment on the resulting fire behavior, vegetation community, and ecosystem responses. Managers need to be aware of the beneficial and adverse effects of mastication to more effectively manage ecosystems. The goal of this study is to investigate the effects of masticated fuels on various ecosystem processes and characteristics with the following objectives • Describe the characteristics and properties of masticated fuelbeds • Develop a sampling protocol to estimate the loading of masticated fuelbed • Describe fire behavior in burning masticated fuelbeds • Evaluate the effects of masticated fuelbed on the ecosystem We have established study sites on the Valles Caldera National Preserve, New Mexico; San Juan National Forest, Colorado; and Kootenai National Forest, Montana. Each site contains control, masticate, masticate and burn, and burn only units. As of fall 2008, all sites had received the mastication treatment but none had been prescribed burned. We found that a cover-depth sampling protocol was the best option for quantifying masticated fuel loadings and mastication reduced canopy fuels by approximately 30-50 percent

Classification error in dynamic discrete choice models: implications for female labor supply behavior

Two key issues in the literature on female labor supply are: (1) if persistence in employment status is due to unobserved heterogeneity or state dependence, and (2) if fertility is exogenous to labor supply. Until recently, the consensus was that unobserved heterogeneity is very important, and fertility is endogenous. But Hyslop (1999) challenged this. Using a dynamic panel probit model of female labor supply including heterogeneity and state dependence, he found that adding autoregressive errors led to a substantial diminution in the importance of heterogeneity. This, in turn, meant he could not reject that fertility is exogenous. Here, we extend Hyslop (1999) to allow classification error in employment status, using an estimation procedure developed by Keane and Wolpin (2001) and Keane and Sauer (2005). We find that a fairly small amount of classification error is enough to overturn Hyslop's conclusions, leading to overwhelming rejection of the hypothesis of exogenous fertility

The importance of balanced pro-inflammatory and anti-inflammatory mechanisms in diffuse lung disease

The lung responds to a variety of insults in a remarkably consistent fashion but with inconsistent outcomes that vary from complete resolution and return to normal to the destruction of normal architecture and progressive fibrosis. Increasing evidence indicates that diffuse lung disease results from an imbalance between the pro-inflammatory and anti-inflammatory mechanisms, with a persistent imbalance that favors pro-inflammatory mediators dictating the development of chronic diffuse lung disease. This review focuses on the mediators that influence this imbalance