QUANTITATIVE RESEARCH METHODS-SEEN EXAM Coursework

QUANTITATIVE RESEARCH METHODS-SEEN EXAM - Coursework Example2. By using or combining several measure serial publication of cross section observations, board entropy provide much(prenominal) informative selective information, more variability, less collinearity among variables, more degrees of freedom and efficiency as per Baltagis discussion (1995, p. 3-6). 3. jury information are able to count better the dynamics of adjustment because the dining table entropy technique is able to pass across repeated cross section of observation across sequence. Thus, panel data is believed to more appropriate than either of the two methods (cross section and time series analysis) in the study of situations like straight waves of minimum wage increases across localities and local minimum wages and everywhere time 4. Panel data can detect and measure effects better than what purely time series and cross-section data can do. 5. Panel data are able to study complicated behavioural mod els like economies of collection plate and technical change---better than what pure time series and pure cross-section analysis can do. For example, panel data can study the interaction of the variables involved over time, which can non be done by pure time series data. 6. By covering more units over time, panel data can minimize the biases that may result as data are aggregated. In 1995, Baltagi pointed out that time series and cross-section studies were not controlling for heterogeneity and run the risk of obtaining biased results (p. 3). For example, Baltagi (1995, p. 3) cited for example that consumption of cigarettes is often modelled as a function of lagged consumption, price, and income but the specification of the same function can convert across countries, state, and time. Baltagi 1995, p. 4) added that panel data are able to control for location specific and time invariant variables while a time-series study or a cross-section study cannot at that time. As panel data ca n cover heterogeneity, Baltagi (1995, p. 4), not accounting for country heterogeneity can cause serious specification error. In addition, Baltagi (1995, p. 4) tell that panel data are able to study the dynamics of adjustment. Meanwhile, Verbeek (2008, p. 655) say that the main advantage of panel data over either time series or cross-section analysis is that through panel data, economists are able to specify more complicated and realistic models than a single time series or cross-section data can do. (b) Explain the intuition behind the fixed effect model (FEM) and describe the least real dummy variable (LSDV) and the time demeaned approaches to estimating a FEM. 30 Marks Verbeek (2008, p. 359) defined the fixed effects model as simply a regression model in which the intercept terms vary over the individual units. Gujarati (2004, p. 642) pointed out that the main intuition behind the fixed effect model or FEM is that although the intercept may differ across individual elements, ea ch specific intercept does not change over time or is time invariant. The methods for estimating the fixed effects model (FEM) are the least square dummy variable (LSDV) and the time demeaned approaches towards estimating the FEM. In the LSDV method, the main instruments for capturing the fixed effects are dummy variables. The time demeaned variable approach to FEM modelling, reconstruct the basic model yit = ? + ?xit + uit as departures of a variable from its mean over time or yit - i ?(xit - i ) + ( uit - )i) where the values with bars denote the time mean of the said variable (Brooks