Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non-Gaussian response variables. The posterior marginals are not available in closed form owing to the non-Gaussian response variables. For such models, Markov chain Monte Carlo methods can be implemented, but they are not without problems, in terms of both convergence and computational time. In some practical applications, the extent of these problems is such that Markov chain Monte Carlo sampling is simply not an appropriate tool for routine analysis. We show that, by using an integrated nested Laplace approximation and its simplified version, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.

/pdf/approximate-bayesian-inference-for-latent-gaussian-models-by-2m3x22eic2.pdf

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

Statistical Analysis with Missing Data

Foreword Stephen P. Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng Introduction to MCMC, Charles J. Geyer A short history of Markov chain Monte Carlo: Subjective recollections from in-complete data, Christian Robert and George Casella Reversible jump Markov chain Monte Carlo, Yanan Fan and Scott A. Sisson Optimal proposal distributions and adaptive MCMC, Jeffrey S. Rosenthal MCMC using Hamiltonian dynamics, Radford M. Neal Inference and Monitoring Convergence, Andrew Gelman and Kenneth Shirley Implementing MCMC: Estimating with confidence, James M. Flegal and Galin L. Jones Perfection within reach: Exact MCMC sampling, Radu V. Craiu and Xiao-Li Meng Spatial point processes, Mark Huber The data augmentation algorithm: Theory and methodology, James P. Hobert Importance sampling, simulated tempering and umbrella sampling, Charles J.Geyer Likelihood-free Markov chain Monte Carlo, Scott A. Sisson and Yanan Fan MCMC in the analysis of genetic data on related individuals, Elizabeth Thompson A Markov chain Monte Carlo based analysis of a multilevel model for functional MRI data, Brian Caffo, DuBois Bowman, Lynn Eberly, and Susan Spear Bassett Partially collapsed Gibbs sampling & path-adaptive Metropolis-Hastings in high-energy astrophysics, David van Dyk and Taeyoung Park Posterior exploration for computationally intensive forward models, Dave Higdon, C. Shane Reese, J. David Moulton, Jasper A. Vrugt and Colin Fox Statistical ecology, Ruth King Gaussian random field models for spatial data, Murali Haran Modeling preference changes via a hidden Markov item response theory model, Jong Hee Park Parallel Bayesian MCMC imputation for multiple distributed lag models: A case study in environmental epidemiology, Brian Caffo, Roger Peng, Francesca Dominici, Thomas A. Louis, and Scott Zeger MCMC for state space models, Paul Fearnhead MCMC in educational research, Roy Levy, Robert J. Mislevy, and John T. Behrens Applications of MCMC in fisheries science, Russell B. Millar Model comparison and simulation for hierarchical models: analyzing rural-urban migration in Thailand, Filiz Garip and Bruce Western

Handbook of Markov Chain Monte Carlo

A detailed review of the education sector in Australia as in the data provided by the 2006 edition of the OECD's annual publication, 'Education at a Glance' is presented. While the data has shown that in almost all OECD countries educational attainment levels are on the rise, with countries showing impressive gains in university qualifications, it also reveals that a large of share of young people still do not complete secondary school, which remains a baseline for successful entry into the labour market.

/pdf/education-at-a-glance-m5vl7o68j5.pdf

Education at a Glance

We exend Meng and Wong (1996) identity from a fixed to a varying dimentional setting. The identity is a very powerful tool to estimate ratios of normalizing constants and thus can be used to evaluate Bayes factors. The extention is driven by the reversibler jump algorithm so that the output from the semplar can be directly used to efficiently estimate the required Bayes factor. Two applications, involving linear and logistic regression models, illustrate the advantages of the suggested approach with respect to alternatives previously proposed in the literature.

Efficient estimate of Bayes factors from Reversible Jump output

We study the development of social capital through adult civic engagement, in relation to social capital exposure having occurred during childhood based on experiences outside the family at primary school. We assume that the types of classmates in attendance at a child's school would have influenced her/his social capital. To identify the types of classmates, we take advantage of the heterogeneity in the ability levels of British primary-school classes during the 1960s. At that time, some schools were practicing a method of streaming, whereas others were not. Using British National Child Development data, we construct a single score of civic engagement and evaluate the effect on adult civic engagement of attending homogeneous-ability classes versus nonhomogeneous-ability classes and being in high-, average- or low-ability classes when enrolled in streamed schools. Our results show that children who were grouped in homogeneous-ability classes developed a lower interest in civic engagement than their peers who attended mixed-ability classes (nonstreamed schools). Moreover, among children who attended streamed schools, a lower attitude toward civic engagement was observed among low-ability students. Thus, streaming appears to be detrimental to social capital development, especially for low-ability individuals.

/pdf/primary-school-class-composition-and-the-development-of-2mcue1mfoj.pdf

Primary-school class composition and the development of social capital

The recent Italian anti-corruption law has introduced a new figure, the supervisor for corruption prevention, who has to fill in an annual report about the accomplishment of anti-corruption measures within the institution he/she represents. Using data coming from such annual reports referred to a sample of Italian municipalities, a latent Markov model is fitted to investigate the evolution over time of the degree of accomplishment of anti-corruption measures. First results evidence three latent states of increasing virtuosity. Moreover, at the beginning of the study, the most of the sample belongs to the low and intermediate states of virtuosity, even if there is evidence of high probabilities to move to upper states over time.

https://meetings3.sis-statistica.org/index.php/sis2018/50th/paper/download/1191/10

A longitudinal analysis of the degree of accomplishment of anti-corruption measures by Italian municipalities: a latent Markov approach.

Statistical inference is mainly concerned with providing some conclusions about the parameters which describe the distribution of a variable of interest in a certain population on the basis of a random sample. In this article, we review point estimation methods which consist of assigning a value to each unknown parameter. Basic properties of an estimator are illustrated together with the main methods of finding estimators: method of moments, maximum likelihood, and Bayesian methods. In particular, we discuss maximum likelihood estimation of the most well-known item response theory model, the Rasch model, and illustrate it through a data analysis example.

Point Estimation Methods with Applications to Item Response Theory Models

This article is focused on the analysis of the determinants of job satisfaction of Russian workers through the estimation of ordered logit models with individual fixed effects on a panel data set extracted from the Russian Longitudinal Monitoring Survey The real wage results positively associated with job satisfaction on all samples considered, after controlling for several time-varying controls aimed at picking up time-varying human capital, preferences, and non-instantaneous equilibrium adjustments As long as the included controls capture all the heterogeneous trends in individual wages, we may interpret this result as a possible failure of the theory of compensating wage differentials in the Russian labour market

Francesco Bartolucci

Papers

Efficient estimate of Bayes factors from Reversible Jump output

Primary-school class composition and the development of social capital

A longitudinal analysis of the degree of accomplishment of anti-corruption measures by Italian municipalities: a latent Markov approach.

Point Estimation Methods with Applications to Item Response Theory Models

Job satisfaction and compensating wage differentials: Evidence from Russia