Two-step estimation of simultaneous equation panel data models with censored endogenous variables

This paper presents some two-step estimators for a wide range of parametric panel data models with censored endogenous variables and sample selection bias. Our approach is to derive estimates of the unobserved heterogeneity responsible for the endogeneity/selection bias to include as additional explanatory variables in the primary equation. These are obtained through a decomposition of the reduced form residuals. The panel nature of the data allows adjustment, and testing, for two forms of endogeneity and/or sample selection bias. Furthermore, it incorporates roles for dynamics and state dependence in the reduced form. Finally, we provide an empirical illustration which features our procedure and highlights the ability to test several of the underlying assumptions.Estimation;Panel Data;statistics

Estimating and interpreting models with endogenous treatment effects: The relationship between competing estimators of the union impact on wages

Wages;Trade Unions;Estimation;Models

Estimating and testing simultaneous equation panel data models with censored endogenous variables

Wages;Estimation;Testing;Models

Predictive gains from forecast combinations using time-varying model weights

Several frequentist and Bayesian model averaging schemes, including a new one that simultaneously allows for parameter uncertainty, model uncertainty and time varying model weights, are compared in terms of forecast accuracy over a set of simulation experiments. Artificial data are generated, characterized by low predictability, structural instability, and fat tails, which is typical for many financial-economic time series. Sensitivity of results with respect to misspecification of the number of included predictors and the number of included models is explored. Given the set up of our experiments, time varying model weight schemes outperform other averaging schemes in terms of predictive gains both when the correlation among individual forecasts is low and the underlying data generating process is subject to structural locations shifts. In an empirical application using returns on the S&P 500 index, time varying model weights provide improved forecasts with substantial economic gains in an investment strategy including transaction costs.Bayesian model averaging;forecast combination;stock return predictability;time-varying weight combination

Nucleotide sequence and genomic organization of an ophiovirus associated with lettuce big-vein disease

The complete nucleotide sequence of an ophiovirus associated with lettuce big-vein disease has been elucidated. The genome consisted of four RNA molecules of approximately 7ò8, 1ò7, 1ò5 and 1ò4 kb. Virus particles were shown to contain nearly equimolar amounts of RNA molecules of both polarities. The 5'- and 3'-terminal ends of the RNA molecules are largely, but not perfectly, complementary to each other. The virus genome contains seven open reading frames. Database searches with the putative viral products revealed homologies with the RNA-dependent RNA polymerases of rhabdoviruses and Ranunculus white mottle virus, and the capsid protein of Citrus psorosis virus. The gene encoding the viral polymerase appears to be located on the RNA segment 1, while the nucleocapsid protein is encoded by the RNA3. No significant sequence similarities were observed with other viral proteins. In spite of the morphological resemblance with species in the genus Tenuivirus, the ophioviruses appear not to be evolutionary closely related to this genus nor any other viral genus

Estimating Dynamic Models from Repeated Cross-Sections

A major attraction of panel data is the ability to estimate dynamic models on an individual level. Moffitt (1993) and Collado (1998) have argued that such models can also be identified from repeated cross-section data. In this paper we reconsider this issue. We review the identification conditions underlying alternative estimators and present an instrumental variables type estimator that is consistent under a relatively weak set of conditions. These conditions, however, are not trivially satisfied in applied work.repeated cross-sections;pseudo panel data;chohorts;instrumental variables;individual dynamics