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xxvii). Gujarati (2004, p. 562) pointed out that panel data models combine time series and cross-section observation. Being so, a panel data analysis combines the advantages of both cross sectional and time series approaches (Gujarati 2004, p. xxvii). Panel data analysis is highly feasible because panel data in the social sciences are available (Gujarati 2004, p. xxvii). In the time series data, one observes the values of one or several variables across time (Gujarati 2004, p. 636). In contrast, the values of the variables are collected for one or several sample units or entities at the same point in time (Gujarati 2004, p. 636). The other names for panel data are pooled data, combination of time series and cross-section data, micropanel data, longitudinal data, event history analysis, and cohort analysis (Gujarati 2004, p 636). From panel data, panel data regression models are constructed (Gujarati 2004, p. 634-637). Gujarati (2004, p. 636) reported that Baltagi cited the following advantages of panel data over cross section and timer series analysis: 1. Panel data can consider heterogeneity. 2. Panel data cover more variability, less collinearity, more degrees of freedom and more efficiency. 3. By being able to study cross section observations over time, panel data are better suited to study dynamics of change. 4. Panel data can discover and measure effects that are unobservable using either the pure cross-section or pure time-series analysis: the interrelationship or interaction of variables across time. 5. Panel data can be used to develop or analyze models that more complicated using either of the two pure models (cross-section and timer series). 6. Panel data can minimize that bias that can result from aggregating cases into broad aggregates. Gujarati (2004, p. 638) summarized Baltagi’s six point analysis of the advantages of panel data into this: panel data can improve analysis in ways not possible under cross-section and time series analysis. (b) Explain the intuition behind the fixed effect model (FEM) and describe the least square dummy variable (LSDV) and the time demeaned approaches to estimating a FEM. [30 Marks] According to Brooks (2008, p. 490), there are two kinds of estimation approaches to panel data in financial research: the fixed effects models and the random effects models. The simplest fixed effect allow the intercept in a regression to differ at a given point in time but not across time, while all of the slope estimates are fixed both at a given time moment and across time (Brooks 2008, p. 490). The fixed effect model is considered parsimonious compared to an alternative---the Seemingly Unrelated Regression (SUR) technique---in which each cross-section can have not only different intercepts but also different slopes for a regression (Brooks 2008, p. 490). In illustrating the fixed effect model, Brooks (2008, p. 490) used the equation uit = ui + vit where the disturbance term uit is decomposed into specific disturbance, ui, and “remainder disturbance”, vit, that changes over time and entities thereby supposedly capturing everything unexplained about yit. Based on this, Brooks (2008, p. 491) said that the usual regression function yit = ? + ?xit + uit becomes yit = ? + ?xit + ui + vit where ui represent all the variables that affect yit cross-sectionally or at a given time moment and which does not vary over time. As pointed out by Brooks, some examples of cross-sectional variables that can affect yit include a ...Show more
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SECTION A 1. (a) What are the advantages of applying panel data analysis compared to using pure cross sectional or time series approaches to modelling data? [10 Marks] A panel data is a special type of pooled data in which a cross-sectional unit is surveyed over time (Gujarati 2004, p…
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