Upon examining research in premier finance journals, we find that the majority of studies employ OLS regression
as the primary statistical inference technique.
Variable Beta T Pixar Studio 0.307 2.023 ** G Rating 0.095 0.698 Movie Run Time 0.305 1.968 * Comedy Genre -0.314 -1.911 * Family Genre -0.268 -1.639 Dependent variable: Adjusted Gross Earnings N = 50, [R.sup.2] = 0.318, Adjusted [R.sup.2] = 0.240, Model F (ANOVA) = 4.101 *** *** p < 0.01, ** p < 0.05, * p < 0.10 AUTHOR BIOGRAPHY
Furthermore, based upon the OLS regression
results, the provision of access to credit and training were ineffective, or in fact lower the efficiency.
We employ different methods, including pooled OLS regression
, FamaMacBeth regression, fixed effects regression, and random effects regression, to examine the determinants of cash holding.
Above table presents results of OLS regression
relating Foreign Direct Investment (FDI) net inflows as a percentage of Gross Domestic Product (GDP) to regional trade agreements.
Different specification was made in OLS regression
equation in which dependent variable is current GPA and the independent variables were randomly used.
The GWR technique is a direct extension of OLS regression
, and in contrast to traditional global regression, it allows varying coefficients for the environmental predictors at different locations.
For the OLS regression
model, we found that two coefficients changed from insignificant (p > .05) in the unweighted estimates (i) to significant (P <.
The parametric technique most familiar to cost estimators is OLS regression
, which makes many assumptions about the distribution function and normality of error terms.
In the Heckman two-step model, the first step consists of a Probit regression for the selection function as shown in equation (3) below; the second step is an OLS regression
, as shown in equation (4).
Second, a simple OLS regression
is conducted to see whether teams improve their goal difference (goals scored minus goals conceded), as opposed to points obtained, when firing the coach.
Another advantage of quantile regression over OLS regression
is that the quantile regression estimates are more robust against outliers in the response measurements, which gives it an edge when handling heteroskedasticity, skewness, and leptokurtosis in financial data.