Estimation of a Transformation Model with Truncation, Interval Observation and Time-Varying Covariates
نویسندگان
چکیده
Abrevaya (1999b) considered estimation of a transformation model in the presence of left–truncation. This paper observes that a cross–sectional version of the statistical model considered in Frederiksen, Honoré, and Hu (2007) is a generalization of the model considered by Abrevaya (1999b) and the generalized model can be estimated by a pairwise comparison version of one of the estimators in Frederiksen, Honoré, and Hu (2007). Specifically, our generalization will allow for discretized observations of the dependent variable and for piecewise constant time–varying explanatory variables. ∗This research was supported by the National Science Foundation and the Gregory C. Chow Econometric Research Program at Princeton University. We thank seminar participants at Rice University, Université Paris 1 Panthéon–Sorbonne and the Federal Reserve Bank of Chicago as well as members of Princeton’s Microeconometric Reading Group for comments. The opinions expressed here are those of the authors and not necessarily those of the Federal Reserve Bank of Chicago or the Federal Reserve System. †Mailing Address: Department of Economics, Princeton University, Princeton, NJ 08544-1021. Email: [email protected]. ‡Mailing Address: Economic Research Department, Federal Reserve Bank of Chicago, 230 S. La Salle Street, Chicago, IL 60604. Email: [email protected].
منابع مشابه
Sub-optimal Estimation of HIV Time-delay Model using State-Dependent Impulsive Observer with Time-varying Impulse Interval: Application to Continuous-time and Impulsive Inputs
Human Immunodeficiency Virus (HIV) weakens the immune system in confronting various diseases by attacking to CD4+T cells. In modeling HIV behavior, the number of CD4+T cells is considered as the output. But, continuous-time measurement of these cells is not possible in practice, and the measurement is only available at variable intervals that are several times bigger than sampling time. In this...
متن کاملGeoadditive hazard regression for interval censored survival times
The Cox proportional hazards model is the most commonly used method when analyzing the impact of covariates on continuous survival times. In its classical form, the Cox model was introduced in the setting of right-censored observations. However, in practice other sampling schemes are frequently encountered and therefore extensions allowing for interval and left censoring or left truncation are ...
متن کاملPositive-Shrinkage and Pretest Estimation in Multiple Regression: A Monte Carlo Study with Applications
Consider a problem of predicting a response variable using a set of covariates in a linear regression model. If it is a priori known or suspected that a subset of the covariates do not significantly contribute to the overall fit of the model, a restricted model that excludes these covariates, may be sufficient. If, on the other hand, the subset provides useful information, shrinkage meth...
متن کاملپیشبینی خشکسالی هیدرولوژیک با استفاده از سریهای زمانی
INTRODUCTION Hydrologic drought in the sense of deficient river flow is defined as the periods that river flow does not meet the needs of planned programs for system management. Drought is generally considered as periods with insignificant precipitation, soil moisture and water resources for sustaining and supplying the socioeconomic activities of a region. Thus, it is difficult to give a univ...
متن کاملOnline Monitoring for Industrial Processes Quality Control Using Time Varying Parameter Model
A novel data-driven soft sensor is designed for online product quality prediction and control performance modification in industrial units. A combined approach of time variable parameter (TVP) model, dynamic auto regressive exogenous variable (DARX) algorithm, nonlinear correlation analysis and criterion-based elimination method is introduced in this work. The soft sensor performance validation...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2007