A graphical model is a graph that represents a set of conditional independence relations among the vertices (random variables). The graph is often given a causal interpretation as well. I describe how graphical causal models can be used in an algorithm for constructing partial information about causal graphs from observational data that is reliable in the large sample limit, even when some of t...

In this introductory chapter we seek to cover su¢cient di¤erential geometry in order to understand its application to Econometrics. It is not intended to be a comprehensive review of either di¤erential geometric theory, nor of all the applications which geometry has found in statistics. Rather it is aimed as a rapid tutorial covering the material needed in the rest of this volume and the genera...

We introduce the process of psychological burnout and recovery as an explanation for the phenomenon known as unretirement. We illustrate theoretically how predictable time variation in burnout could generate retirement and subsequent re-entry in a standard retirement model. We apply this model to the longitudinal Health and Retirement Study, presenting a novel measure of burnout, the Burnout EX...

Fair sharing and efficient allocation of risks, returns and resources are considered cornerstones of Public Private Partnerships. However, allocation of certain risks, like demand risks, have emerged as pain points. This paper examines the impact of demand uncertainty on a firm’s bidding behavior. Applying econometric models, to check for inconsistencies in firm behavior, on bid data of toll ro...

When the object of the statistical analysis is the estimation of an economic model, the choice of a likelihood function should be coherent with the economic model under investigation. Many econometric models provides the researcher with weak “structural prediction” about the parameter of interest and the data. Econometric models specified through moment conditions and usually estimated by Gener...

Likelihood based inference for multi-state latent factor intensity models is hindered by the fact that exact closed-form expressions for the implied data density are not available. This is a common and well-known problem for most parameter driven dynamic econometric models. This paper reviews, adapts and compares three different approaches for solving this problem. For evaluating the likelihood...

This article is about estimation and inference methods for high dimensional sparse (HDS) regression models in econometrics. High dimensional sparse models arise in situations where many regressors (or series terms) are available and the regression function is wellapproximated by a parsimonious, yet unknown set of regressors. The latter condition makes it possible to estimate the entire regressi...

The main objective of this work is to apply the hedonic pricing method using the methodology of spatial econometrics in order to assess the economic value of irrigation water, as one of the individual attributes of the value of agricultural land parcels. Most of the agricultural land’s value attributes, like neighbor characteristics as well as the availability of irrigation water, exhibit a spa...

This paper considers a variety of econometric models for the joint distribution of stock and bond returns in the presence of regime switching dynamics. While simple twoor three-state models capture the univariate dynamics in bond and stock returns, a more complicated four state model with regimes characterized as crash, slow growth, bull and bull burst states is required to capture the joint di...

Using German regional data for 1998, 2002 and 2006, this study reexamines the Oswald hypothesis, the conjecture that high levels of home ownership are linked to inferior outcomes in regional labor markets. Applying a set of controls for regional unemployment rates, three different econometric models are specified and estimated: a cross-sectional model, a pooled data model, and a model taking in...