387:) example above, two datasets with a panel structure are shown and the objective is to test whether there's a significant difference between people in the sample data. Individual characteristics (income, age, sex) are collected for different persons and different years. In the first dataset, two persons (1, 2) are observed every year for three years (2016, 2017, 2018). In the second dataset, three persons (1, 2, 3) are observed two times (person 1), three times (person 2), and one time (person 3), respectively, over three years (2016, 2017, 2018); in particular, person 1 is not observed in year 2018 and person 3 is not observed in 2016 or 2018.
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source of information in the mathematical and statistical sciences. CPD stands out from other research methods because it vividly illustrates how independent and dependent variables may shift between countries. This panel data collection allows researchers to examine the connection between variables across several cross-sections and time periods and analyze the results of policy actions in other nations.
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can be thought of as special cases of panel data that are in one dimension only (one panel member or individual for the former, one time point for the latter). A literature search often involves time series, cross-sectional, or panel data. Cross-panel data (CPD) is an innovative yet underappreciated
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is fixed over time, it will induce serial correlation in the error term of the regression. This means that more efficient estimation techniques are available. Random effects is one such method: it is a special case of feasible
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is believed to be correlated with one of the independent variables, an alternative estimation technique must be used. Instrumental variables or GMM techniques are commonly used in this situation, such as the
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is not correlated with any of the independent variables, ordinary least squares linear regression methods can be used to yield unbiased and consistent estimates of the regression parameters. However, because
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points in time (for the example, the wide format would have only two (first example) or three (second example) rows of data with additional columns for each time-varying variable (income, age).
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are individual-specific, time-invariant effects (e.g., in a panel of countries this could include geography, climate, etc.) which are fixed over time, whereas
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may occur. The fixed effect estimator and the first differences estimator both rely on the assumption of strict exogeneity. Hence, if
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involving measurements over time. Panel data is a subset of longitudinal data where observations are for the same subjects each time.
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is unobserved, and correlated with at least one of the independent variables, then it will cause omitted variable bias in a standard
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Davies, A.; Lahiri, K. (2000). "Re-examining the
Rational Expectations Hypothesis Using Panel Data on Multi-Period Forecasts".
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Davies, A.; Lahiri, K. (1995). "A New
Framework for Testing Rationality and Measuring Aggregate Shocks Using Panel Data".
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831:. Different assumptions can be made on the precise structure of this general model. Two important models are the
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regression. However, panel data methods, such as the fixed effects estimator or alternatively, the
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1338:. While estimating this we should have the proper information about the instrumental variables.
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panel member is not observed every period. Therefore, if an unbalanced panel contains
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periods, then the following strict inequality holds for the number of observations (
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Diggle, Peter J.; Heagerty, Patrick; Liang, Kung-Yee; Zeger, Scott L. (2002).
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Longitudinal and Panel Data: Analysis and
Applications in the Social Sciences
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is the time dimension. A general panel data regression model is written as
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which controls for the structure of the serial correlation induced by
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1288:{\displaystyle y_{it}=\alpha +\beta 'X_{it}+\gamma y_{it-1}+u_{it}.}
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Fitzmaurice, Garrett M.; Laird, Nan M.; Ware, James H. (2004).
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The presence of the lagged dependent variable violates strict
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705:{\displaystyle X_{it},\quad i=1,\dots ,N,\quad t=1,\dots ,T,}
1569:. Cambridge: Cambridge University Press. pp. 226–254.
1610:(Second ed.). New York: Cambridge University Press.
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Household, Income and Labour
Dynamics in Australia Survey
1567:
Analysis of Panels and
Limited Dependent Variable Models
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501:(e.g., the second dataset above) is a dataset in which
1527:(Fourth ed.). Chichester: John Wiley & Sons.
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394:(e.g., the first dataset above) is a dataset in which
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Data sets which have a multi-dimensional panel design
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917:{\displaystyle y_{it}=\alpha +\beta 'X_{it}+u_{it},}
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where one row represents one observational unit for
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1677:Mathematical and quantitative methods (economics)
1438:(2nd ed.). Oxford University Press. p.
402:year. Consequently, if a balanced panel contains
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1188:of the dependent variable is used as regressor:
1184:Dynamic panel data describes the case where a
1467:. Hoboken: John Wiley & Sons. p. 2.
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1369:Survey of Income and Program Participation
600:Both datasets above are structured in the
115:A study that uses panel data is called a
69:Learn how and when to remove this message
1588:. New York: Cambridge University Press.
398:panel member (i.e., person) is observed
32:This article includes a list of general
1522:
1490:"A Note on Cross-Panel Data Techniques"
982:{\displaystyle u_{it}=\mu _{i}+v_{it}.}
381:multiple response permutation procedure
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842:Consider a generic panel data model:
442:periods, the number of observations (
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1494:Latest Developments in Econometrics
1342:Data sets which have a panel design
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1525:Econometric Analysis of Panel Data
38:it lacks sufficient corresponding
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1086:can be used to control for it.
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1381:Panel Study of Income Dynamics
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16:Longitudinal statistical study
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1465:Applied Longitudinal Analysis
1434:Analysis of Longitudinal Data
1393:National Longitudinal Surveys
590:{\displaystyle n<N\cdot T}
1558:10.1016/0304-4076(94)01649-K
1488:Zaman, Khalid (2023-01-24).
7:
1412:Multidimensional panel data
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97:are both multi-dimensional
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1387:China Family Panel Studies
1084:first-difference estimator
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1523:Baltagi, Badi H. (2008).
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1071:{\displaystyle \mu _{i}}
1011:{\displaystyle \mu _{i}}
1647:Korea Employment Survey
1545:Journal of Econometrics
1336:Arellano–Bond estimator
53:more precise citations.
1672:Statistical data types
1608:Analysis of Panel Data
1506:10.5281/zenodo.7565625
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117:longitudinal study
1534:978-0-470-51886-1
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1304:endogeneity
1302:, that is,
606:wide format
602:long format
105:Time series
51:introducing
1662:Panel data
1656:Categories
1517:References
1500:(1): 1–7.
1300:exogeneity
91:panel data
83:statistics
34:references
1259:−
1245:γ
1222:β
1215:α
1158:μ
1126:μ
1098:μ
1060:μ
1000:μ
952:μ
876:β
869:α
786:β
779:α
691:…
666:…
582:⋅
479:⋅
59:June 2020
1225:′
879:′
835:and the
789:′
616:Analysis
1642:pairfam
1377:(LLMDB)
1359:(HILDA)
379:In the
265:income
259:person
139:income
133:person
123:Example
47:improve
1614:
1592:
1573:
1531:
1471:
1446:
1395:(NLSY)
1389:(CFPS)
1383:(PSID)
1371:(SIPP)
1365:(BHPS)
1353:(SOEP)
1348:German
715:where
36:, but
1637:KLIPS
1604:Hsiao
1417:Notes
1401:(LFS)
400:every
262:year
136:year
1632:PSID
1612:ISBN
1590:ISBN
1571:ISBN
1529:ISBN
1469:ISBN
1444:ISBN
576:<
396:each
385:MRPP
367:3300
364:2017
350:2100
347:2018
333:2000
330:2017
316:1900
313:2016
299:1500
296:2017
282:1600
279:2016
271:sex
268:age
241:2400
238:2018
224:2300
221:2017
207:2000
204:2016
190:2000
187:2018
173:1600
170:2017
156:1300
153:2016
145:sex
142:age
107:and
99:data
93:and
85:and
1554:doi
1502:doi
1186:lag
1089:If
1080:OLS
1051:If
610:all
497:An
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