Estimating economies of scale and scope with flexible technology

Vertical disintegration in the European electricity sector: Empirical evidence on lost synergies

“…Nevertheless, we do not have such information for our sample. Therefore, we approximate this variable by the interest 21 In the fashion of Triebs et al (2012), we chose a single output measure (because of multi-collinearity issues) for the downstream transmission stage among several possibilities, such as transmission grid length, transmitted volumes, or peak grid-load. 22 In general, loop flows play a minor role in the distribution network.…”

“…Supposedly, firm fixed-effects impose too many parameters and, hence, many parameter estimates turn out statistically insignificant. Therefore, we include country fixed-effects to take up unobserved heterogeneity across the 16 countries in 23 In a similar vein, Kaserman and Mayo (1991) employ the yield to maturity on long-term bonds as their price of capital, and Triebs et al (2012) also concentrate on long-term interest rates for calculating capital expenditures. 24 Unfortunately, the gas price explicitly for electricity companies in the OECD database exhibited too many missing values and could therefore not be employed in this analysis.…”

“…Hayashi et al, 1997) try to overcome this dilemma by replacing zero values by an arbitrarily small value, Triebs et al (2012) argue that such estimates of scope economies may suffer from significant bias. Other functional forms, for example the composite cost function, allow for zero values in outputs (Fraquelli et al, 2005) but bear disadvantages, such as highly non-linear parameters and no economic meaning of coefficients (Triebs et al, 2012). 14 We will come back to this issue in Section 6.2, when we interpret the magnitude of EVI at large output combinations (e.g.…”

Transmission of Monetary Policy and Bank Heterogeneity in Colombia

“…Moreover, there have been concerns on the effects of heterogeneities across firms on efficiency estimates. Triebs, Saal, Arocena & Kumbhakar (2016) find evidence of the need to consider different technologies across firms when estimating economies of scale and scope. They use a model that allows for flexible technology across firm types and test their theoretical approach with an empirical application to US local government-owned electric utility firms.…”

Semi-Parametric Measures of Scale Characteristics of German Natural Gas-Fired Electricity Generation

“…The parameter estimates, however, can vary with the functional relationship and with the distributional assumptions regarding the residuals (Kumbhakar et al, 2015). Thus, estimates of scale characteristics derived with DEA and SFA may depend on the assumptions by the researcher, which ultimately may influence policy implications (Bogetoft and Wang, 2005;Triebs et al, 2016). To overcome the limitations of DEA and SFA, a third approach known as Stochastic Non-Smooth Envelopment of Data (StoNED, Kuosmanen and Kortelainen, 2012) allows flexible estimation of production functions without an underlying functional form (similar to DEA) and stochastic treatment of inefficiency and noise (similar to SFA).…”