Likelihood-based regression models such as the normal linear regression model and the linear logistic model, assume a linear (or some other parametric) form for the covariates $X_1, X_2, \cdots, X_p$. We introduce the class of generalized additive models which replaces the linear form $\sum \beta_jX_j$ by a sum of smooth functions $\sum s_j(X_j)$. The $s_j(\cdot)$'s are unspecified functions that are estimated using a scatterplot smoother, in an iterative procedure we call the local scoring algorithm. The technique is applicable to any likelihood-based regression model: the class of generalized linear models contains many of these. In this class the linear predictor $\eta = \Sigma \beta_jX_j$ is replaced by the additive predictor $\Sigma s_j(X_j)$; hence, the name generalized additive models. We illustrate the technique with binary response and survival data. In both cases, the method proves to be useful in uncovering nonlinear covariate effects. It has the advantage of being completely automatic, i.e., no "detective work" is needed on the part of the statistician. As a theoretical underpinning, the technique is viewed as an empirical method of maximizing the expected log likelihood, or equivalently, of minimizing the Kullback-Leibler distance to the true model.

Generalized Additive Models

25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Multiple Imputation for Nonresponse in Surveys

We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects

In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

This paper surveys the microfoundations, empirical evidence, and estimation issues underlying the aggregate matching function. There is no consensus yet on microfoundations but one is emerging on estimation. An aggregate, constant returns, Cobb-Douglas matching function with hires as a function of vacancies and unemployment has been successfully estimated for several countries. Recent work has utilized disaggregated data to go beyond aggregate estimates, with many refinements and suggestions for future research. The paper discusses spatial aggregation issues, and implications of on-the-job search and of the timing of stocks and flows for estimated matching functions.

/pdf/looking-into-the-black-box-a-survey-of-the-matching-function-1boerkl7oh.pdf

Looking into the Black Box: A Survey of the Matching Function

In Chapter 8 we discussed additive models (AM) of the form 

$$ E(Y|X) = c + \sum\limits_{\alpha = 1}^d {g_\alpha (x_\alpha )} . $$

(1)

Note that we put EY = c and E(g α (X α ) = 0 for identification.

On the one hand, kernel density estimation has become a common tool for empirical studies in any research area. This goes hand in hand with the fact that this kind of estimator is now provided by many software packages. On the other hand, since about three decades the discussion on bandwidth selection has been going on. Although a good part of the discussion is about nonparametric regression, this parameter choice is by no means less problematic for density estimation. This becomes obvious when reading empirical studies in which practitioners have made use of kernel densities. New contributions typically provide simulations only to show that the own selector outperforms some of the existing methods. We review existing methods and compare them on a set of designs that exhibit few bumps and exponentially falling tails. We concentrate on small and moderate sample sizes because for large ones the differences between consistent methods are often negligible, at least for practitioners. As a byproduct we find that a mixture of simple plug-in and cross-validation methods produces bandwidths with a quite stable performance.

/pdf/bandwidth-selection-for-kernel-density-estimation-a-review-2c2gy704kf.pdf

Bandwidth selection for kernel density estimation: a review of fully automatic selectors

espanolEstudiamos los precios de mercado de las tierras para uso agrario y elaboramos un modelo de datos de panel para identificar las variables que determinan la evolucion de estos precios en Espana. La determinacion del precio de la tierra es compleja, en nuestro modelo identificamos como variables fundamentales la evolucion de los ingresos esperados por los agricultores y la localizacion geografica, y como no fundamentales o especulativos la presion urbanizadora y el incremento del area de regadio, que tienen un impacto significativo. El crecimiento demografico es otras de las variables significativas en el modelo. Ademas tratamos de describir los elementos del ciclo de precios que estan mas relacionados con el proceso de especializacion productiva de las regiones, ya que el periodo estudiado coincide con la aceleracion de la especializacion en el ambito regional y la integracion en la PAC. La novedad de este trabajo consiste en identificar, mediante tecnicas de datos de panel, factores no fundamentales (precios de la vivienda, creacion de regadios, cambio demografico) que, ademas de los fundamentales, (ingresos esperados, costes de produccion) determinan el valor de cada tipo de tierras y su productividad en cada region. En definitiva se trata de cuantificar que parte de la subida de precios observada se justifica por elementos “internos”, relacionados con la renta agraria esperada, y cual proviene de elementos externos o especulativos (cambio de uso del suelo). Palabras clave: Precios del suelo, competencia por la tierra, especulacion urbana, datos de panel, precio de la vivienda, productividad agraria, regadios, especializacion regional. EnglishThe paper describes the agricultural land price to identify the explanatory variables of the recent cycle in Spain. The key variables in our panel data model are location and expected farm rents as fundamentals and the housing prices and increases in the irrigated area as non fundamentals dependant variables. The demographic growth is also a relevant explanatory variable in the regions. The price cycle is also related with the specialization path of the regions and the impact of the integration in the CAP. The novelty of the paper relay on use of panel data models to identify fundamental factors related with agricultural productivity (expected agricultural rents) and location and no fundamental factors or speculative like (housing price, the irrigate area and the population) using regional data by type of land. The fundamental and non fundamental dependant variables are identify and the results are related with the regional and county specialization.

Precio de la tierra con presión urbana: un modelo para España

http://baobab.uc3m.es/monet/monnet/IMG/pdf/_049-064_.agec_176_1_.pdf

Efficiency, subsidies, and environmental adaptation of animal farming under CAP

We consider an additive model with second-order interaction terms Both marginal integration estimators and a combined backfitting-integration estimator are proposed for all components of the model and their derivatives The corresponding asymptotic distributions are derived Moreover, two test statistics for testing the presence of interactions are proposed Asymptotics for the test functions and local power results are obtained Because direct implementation of the test procedure based on the asymptotics would produce inaccurate results unless the number of observations is very large, a bootstrap procedure is provided, which is applicable for small or moderate sample sizes Further, based on these methods a general test for additivity is developed Estimation and testing methods are shown to work well in simulation studies Finally, our methods are illustrated on a five-dimensional production function for a set of Wisconsin farm data In particular, the separability hypothesis for the production function is discussed

/pdf/nonparametric-estimation-and-testing-of-interaction-in-40gvcx0v1k.pdf

Stefan Sperlich

Papers

Generalized Additive Models

Bandwidth selection for kernel density estimation: a review of fully automatic selectors

Precio de la tierra con presión urbana: un modelo para España

Efficiency, subsidies, and environmental adaptation of animal farming under CAP

Nonparametric Estimation and Testing of Interaction in Additive Models