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

Inequality, Transfers and Growth: New Evidence from the Economic Transition in Poland

This paper challenges the conventional wisdom that inequality in Poland increased markedly during the economic transition that began in 1989-90. Using micro data from the Household Budget Surveys, we find that, after a brief spike in 1989, income and consumption inequality actually declined to below pre-transition levels during 1990-92 and then increased gradually, rising only moderately above pre-transition levels by 1997. In sharp contrast, inequality in labor earnings increased markedly and consistently throughout the 1990-97 period. We find that social transfer mechanisms, including pensions, played an important role in mitigating increases in both overall inequality and poverty. We argue that, from a political economy perspective, transfer mechanisms were well-designed to reduce political resistance to market-oriented reforms in the early years of transition, paving the way for rapid growth. Finally, we provide cross-country evidence from the transition economies that is consistent with our interpretation of the Polish experience and is also consistent with recent work in growth theory which suggests that redistribution that reduces inequality can enhance growth.

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

An algorithm to identify automorphisms which arise from self-induced interval exchange transformations

We give an algorithm to determine if the dynamical system generated by a positive automorphism of the free group can also be generated by a self-induced interval exchange transformation. The algorithm effectively yields the interval exchange transformation in case of success.Comment: 26 pages, 8 figures. v2: the article has been reorganized to make for a more linear read. A few paragraphs have been added for clarit

Response to Teladorsagia circumcincta infection in Scottish Blackface lambs with divergent phenotypes for nematode resistance

peer-reviewedThe objective of this study was to identify Scottish Blackface lambs that were at the extremes of the spectrum of resistance to gastrointestinal nematodes and characterise their response to an experimental nematode challenge. Lambs (n = 90) were monitored for faecal egg count (FEC) (2 samples from each of 2 independent natural infections). The most resistant (n = 10) and susceptible (n = 10) individuals were selected and challenged with 30,000 Teladorsagia circumcincta larvae (L3) at 9 months of age. Response to infection was monitored by measuring FEC, plasma pepsinogen, serum antibodies against nematode larval antigens and haematology profile, until necropsy at 71 days post infection. Worm burden, worm fecundity and the level of anti-nematode antibodies in abomasal mucosa were determined at necropsy. FEC was consistently higher in susceptible animals (P < 0.05), validating the selection method. Worm fecundity was significantly reduced in resistant animals (P = 0.03). There was also a significant correlation (r = 0.88; P < 0.001) between the number of adult worms and FEC at slaughter. There was no effect of phenotype (resistance/susceptibility) on plasma pepsinogen or on haematology profile. Phenotype had a significant effect on the level of anti-nematode IgA antibodies in serum (P < 0.01), reflecting a higher peak in resistant animals at day 7 post infection. It is concluded that significant variation in the response to gastrointestinal nematode challenge exists within the Scottish Blackface population with resistant animals displaying significantly lower FEC, lower worm fecundity and higher concentration of anti-nematode IgA antibodies in serum.Kathryn McRae was supported by a Teagasc Walsh fellowship and the Allan and Grace Kay Overseas Scholarship

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

Radio-frequency reflectometry on an undoped AlGaAs/GaAs single electron transistor

Radio frequency reflectometry is demonstrated in a sub-micron undoped AlGaAs/GaAs device. Undoped single electron transistors (SETs) are attractive candidates to study single electron phenomena due to their charge stability and robust electronic properties after thermal cycling. However these devices require a large top-gate which is unsuitable for the fast and sensitive radio frequency reflectometry technique. Here we demonstrate rf reflectometry is possible in an undoped SET.Comment: Four pages, three figures, one supplementary fil

Data science in translational vision science and technology

What is Data Science? Data science involves the use of a variety of quantitative methods (e.g. mathematics, statistics, computer science) to extract useful information from structured and unstructured data.1 Typically, data scientists undertake exploratory data analysis by deploying machine learning principles and algorithms to identify patterns in rawdata with the purpose of understanding processes and predicting outcomes. These analytic approaches include predictive causal analytics, prescriptive analytics, and machine learning for pattern discovery and outcome prediction, and they require a large volume and variety of data (i.e. structured as well as unstructured data)

Mitochondrial Dysfunction in Parkinson's Disease

Parkinson's disease (PD) is a progressive, neurodegenerative condition that has increasingly been linked with mitochondrial dysfunction and inhibition of the electron transport chain. This inhibition leads to the generation of reactive oxygen species and depletion of cellular energy levels, which can consequently cause cellular damage and death mediated by oxidative stress and excitotoxicity. A number of genes that have been shown to have links with inherited forms of PD encode mitochondrial proteins or proteins implicated in mitochondrial dysfunction, supporting the central involvement of mitochondria in PD. This involvement is corroborated by reports that environmental toxins that inhibit the mitochondrial respiratory chain have been shown to be associated with PD. This paper aims to illustrate the considerable body of evidence linking mitochondrial dysfunction with neuronal cell death in the substantia nigra pars compacta (SNpc) of PD patients and to highlight the important need for further research in this area

Enumerating Abelian Returns to Prefixes of Sturmian Words

We follow the works of Puzynina and Zamboni, and Rigo et al. on abelian returns in Sturmian words. We determine the cardinality of the set $\mathcal{APR}_u$ of abelian returns of all prefixes of a Sturmian word $u$ in terms of the coefficients of the continued fraction of the slope, dependingly on the intercept. We provide a simple algorithm for finding the set $\mathcal{APR}_u$ and we determine it for the characteristic Sturmian words.Comment: 19page