) for some function v(·) and up to the intercept and scaling. For a log- linear approximation, the derivatives of logv … here. This approach has the advantage of being well-understood and imposing a tight discipline on the exercise. To a log-linear … approximation, epsilon1λ,t+1 = −˜ηepsilon1c,t+1 + ˜νepsilon1l,t+1(29) Equation (28) therefore implies a quadratic equation in ˜η and …

) for some function v(·) and up to the intercept and scaling. For a log- linear approximation, the derivatives of logv … here. This approach has the advantage of being well-understood and imposing a tight discipline on the exercise. To a log-linear … approximation, epsilon1λ,t+1 = −˜ηepsilon1c,t+1 + ˜νepsilon1l,t+1(29) Equation (28) therefore implies a quadratic equation in ˜η and …

computationally expensive. Linear approximation by the first-order Taylor expansion is a fast alternative. However, the approximation … transformation and contrast it with the method of linear approximation. Particularly, we derive the convergence rate of the unscented …

linear approximation. We assume no functional form of a given model but estimate it from experimental data by approximating …

incomplete, imprecise data. We propose a fuzzy set-based approach that utilises successive linear approximation. The fuzzy sets …

difficult to solve optimally. Applying a linear approximation technique with some assumptions on the demand distribution …