A Note on Modelling Dynamics in Happiness Estimations
AbstractThis short note discusses two alternative ways to model dynamics in happiness regressions. A explained, this may be important when standard fixed effects estimates have serial correlation in the residuals, but is also potentially useful when serial correlation is not a problem for providing new insights in the happiness of economics area. The note discusses modelling dynamics two ways the note discusses are via a lagged dependent variable, and via an AR(1) process. The usefulness and statistical appropriateness of each is discussed with reference to happiness. Finally, a flow chart is provided summarising key decisions regarding the choice regarding, and potential necessity of, modelling dynamics.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 49364.
Date of creation: Aug 2013
Date of revision:
Happiness; Dynamics; Lagged Dependent Variable; AR(1) process; Estimation;
Find related papers by JEL classification:
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
- C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
- I31 - Health, Education, and Welfare - - Welfare and Poverty - - - General Welfare
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-08-31 (All new papers)
- NEP-ECM-2013-08-31 (Econometrics)
- NEP-HAP-2013-08-31 (Economics of Happiness)
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