Econometrics: Single Equation Models eJournal
This eJournal distributes working and accepted paper abstracts used in econometrics to estimate models in which a single variable of interest is determined by one or more exogenous explanatory variables. The topics in this eJournal include the subjects in Section C2 of the JEL Classification System. Cross-Sectional Models, Spatial Models, Treatment Effect Models - cross-sectional models form a class of research methods that involve observation of some subset of a population of items all at the same time; spatial models include any of the formal techniques which study entities using their topological, geometric, or geographic properties; treatment effect models are the class of models that attempt to measure the effect of a previous action (treatment) on a particular outcome.Time-Series Models - models where the values of an economic variable are related to past values (either directly or indirectly), the emphasis being on making use of past values of a variable to forecast its future. Panel Data Models - models based on data sets that contain repeated observations over the same units (individuals, households, firms), collected over a number of periods. Truncated and Censored Models - models based on data where observations might be either completely missing (or truncated) or the range of the dependent variable is constrained (censored or also known as a tobit model). Discrete Regression and Qualitative Choice Models - an econometric model in which the actors are presumed to have made a choice from a discrete set. Other - any other topic related to single equation models and not falling under any of the categories listed above.
Click here to Browse our Electronic Library to view our archives of abstracts and associated full text papers published in this journal.
Econometrics: Single Equation Models eJournal Advisory Board
Click on the individual's name below to view the advisory board member's author home page.
Angus S. Deaton
Robert F. Engle
Jerry A. Hausman
James J. Heckman
Daniel L. McFadden
Christopher A. Sims
James H. Stock
Mark W. Watson