Erik Biørn
- Published in print:
- 2016
- Published Online:
- December 2016
- ISBN:
- 9780198753445
- eISBN:
- 9780191815072
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198753445.001.0001
- Subject:
- Economics and Finance, Econometrics
Panel data is a data type increasingly used in research in economics, social sciences, and medicine. Its primary characteristic is that the data variation goes jointly over space (e.g. individuals, ...
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Panel data is a data type increasingly used in research in economics, social sciences, and medicine. Its primary characteristic is that the data variation goes jointly over space (e.g. individuals, firms, countries) and time (e.g. years, months). Panel data allow examination of problems which cannot be handled by cross-section data or time-series data. Panel data analysis is a core field in modern econometrics and multivariate statistics, and studies based on such data occupy a growing part of the field in many other disciplines. The book is intended as a text for master’s/advanced undergraduate courses. It may also be useful for PhD students writing theses in empirical/applied economics and readers doing empirical work on their own. The book attempts to take the reader gradually from simple models and methods in scalar (simple vector) notation to more complex models in matrix notation. Compared to related texts, a distinctive feature is that relatively more attention is given to unbalanced panel data, the measurement error problem, random coefficient approaches, the interface between panel data and aggregation, and the interface between unbalanced panels and truncated and censored data sets. The 12 chapters are intended to be largely self-contained, although there is a natural progression. Most chapters contain commented examples based on genuine data, mainly taken from panel data applications to economics. Although the book, inter alia, through its use of examples, aims primarily at students of economics/econometrics, it may be useful also for readers in social sciences outside economics and in psychology and medicine, provided they have a sufficient background in statistics, notably basic regression analysis and elementary linear algebra.Less
Panel data is a data type increasingly used in research in economics, social sciences, and medicine. Its primary characteristic is that the data variation goes jointly over space (e.g. individuals, firms, countries) and time (e.g. years, months). Panel data allow examination of problems which cannot be handled by cross-section data or time-series data. Panel data analysis is a core field in modern econometrics and multivariate statistics, and studies based on such data occupy a growing part of the field in many other disciplines. The book is intended as a text for master’s/advanced undergraduate courses. It may also be useful for PhD students writing theses in empirical/applied economics and readers doing empirical work on their own. The book attempts to take the reader gradually from simple models and methods in scalar (simple vector) notation to more complex models in matrix notation. Compared to related texts, a distinctive feature is that relatively more attention is given to unbalanced panel data, the measurement error problem, random coefficient approaches, the interface between panel data and aggregation, and the interface between unbalanced panels and truncated and censored data sets. The 12 chapters are intended to be largely self-contained, although there is a natural progression. Most chapters contain commented examples based on genuine data, mainly taken from panel data applications to economics. Although the book, inter alia, through its use of examples, aims primarily at students of economics/econometrics, it may be useful also for readers in social sciences outside economics and in psychology and medicine, provided they have a sufficient background in statistics, notably basic regression analysis and elementary linear algebra.
Michael Beenstock
- Published in print:
- 2012
- Published Online:
- August 2013
- ISBN:
- 9780262016926
- eISBN:
- 9780262301381
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262016926.003.0005
- Subject:
- Economics and Finance, Econometrics
This chapter examines issues relating to empirical methodology and hypothesis testing. Because data on parents, children, and siblings are observational, hypotheses testing has proved difficult. This ...
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This chapter examines issues relating to empirical methodology and hypothesis testing. Because data on parents, children, and siblings are observational, hypotheses testing has proved difficult. This problem stems from the absence of data on genotypes, suggesting the non-observability of ability, personality type, and susceptibility. The chapter reviews a number of methodological solutions designed to solve this identification problem, including the use of longitudinal data (data on successive generations). If there are several phenotypes but only one genotype, the methodology of longitudinal data may be applied to observations on parents and children for a single generation. Other methodologies include quasi-experimentation, generated-regressor methodology, natural experimentation, and instrumental-variables estimation.Less
This chapter examines issues relating to empirical methodology and hypothesis testing. Because data on parents, children, and siblings are observational, hypotheses testing has proved difficult. This problem stems from the absence of data on genotypes, suggesting the non-observability of ability, personality type, and susceptibility. The chapter reviews a number of methodological solutions designed to solve this identification problem, including the use of longitudinal data (data on successive generations). If there are several phenotypes but only one genotype, the methodology of longitudinal data may be applied to observations on parents and children for a single generation. Other methodologies include quasi-experimentation, generated-regressor methodology, natural experimentation, and instrumental-variables estimation.
