Showing papers on "Multidimensional panel data published in 1995"
TL;DR: In this article, a panel data approach is advocated and implemented for studying growth convergence, and the familiar equation for testing convergence is reformulated as a dynamic panel data model, and different panel data estimators are used to estimate it.
Abstract: A panel data approach is advocated and implemented for studying growth convergence. The familiar equation for testing convergence is reformulated as a dynamic panel data model, and different panel data estimators are used to estimate it. The main usefulness of the panel approach lies in its ability to allow for differences in the aggregate production function across economies. This leads to results that are significantly different from those obtained from single cross-country regressions. In the process of identifying the individual "country effect," we can also see the point where neoclassical growth empirics meets development economics.
4,113 citations
TL;DR: In this article, a mean-and-covariance structure analysis is proposed for the estimation of a production frontier on a short panel data set, which is closely related to the generalized method of moments.
21 citations
1 Jan 1995
TL;DR: In recent years empirical studies that use panel data have become common, partly because the cost of developing panel or longitudinal data sets is no longer prohibitive and the advance of computerized data management systems has made longitudinal data development cost-effective.
Abstract: A cross-sectional data set refers to observations on a number of individuals at a given time A time-series data set refers to observations made over time on a given unit A panel (or longitudinal or temporal cross-sectional) data set follows a number of individuals over time In recent years empirical studies that use panel data have become common This is partly because the cost of developing panel or longitudinal data sets is no longer prohibitive In some cases, computerized matching of existing administrative records can produce inexpensive longitudinal information, such as the Social Security Administration’s Continuous Work History Sample (CWHS) In other cases, valuable longitudinal data bases can be generated by computerized matching of existing administrative and survey data, such as the University of Michigan’s Panel Study of Income Dynamics (PSID) and the US Current Population Survey Even in cases where the desired longitudinal information can be collected only by initiating new surveys, such as the series of negative income tax experiments in the United States and Canada, the advance of computerized data management systems has made longitudinal data development cost-effective in the last 20 years (Ashenfelter and Solon 1982)