A translog cost model of the bulk shipping industry

1987 ◽  
Vol 11 (4) ◽  
pp. 311-321 ◽  
Author(s):  
S. R. Tolofari ◽  
K. J. Button ◽  
D. E. Pitfield
Keyword(s):  
Cost Model ◽  
Shipping Industry ◽  
Bulk Shipping ◽  
Translog Cost

scholarly journals Substitution and price elasticity estimates using inter-country pooled data in a translog cost model

2006 ◽  
Vol 28 (5-6) ◽  
pp. 706-719 ◽  
Author(s):  
Joyashree Roy ◽  
Alan H. Sanstad ◽  
Jayant A. Sathaye ◽  
Raman Khaddaria
Keyword(s):  
Price Elasticity ◽  
Cost Model ◽  
Pooled Data ◽  
Translog Cost

scholarly journals Substitution and price elasticity estimates using inter-countrypooled data in a translog cost model

2006 ◽  
Author(s):  
Joyashree Roy ◽  
Alan H. Sanstad ◽  
Jayant A. Sathaye ◽  
Raman Khaddaria
Keyword(s):  
Price Elasticity ◽  
Cost Model ◽  
Translog Cost

Homogeneity Restrictions on the Translog Cost Model: A Note

1986 ◽  
Vol 41 (5) ◽  
pp. 1153-1155 ◽  
Author(s):  
ASGHAR ZARDKOOHI ◽  
NANDA RANGAN ◽  
JAMES KOLARI
Keyword(s):  
Cost Model ◽  
Translog Cost

scholarly journals Waves in Ship Prices and Investment *

2014 ◽  
Vol 130 (1) ◽  
pp. 55-109 ◽  
Author(s):  
Robin Greenwood ◽  
Samuel G. Hanson
Keyword(s):  
Behavioral Model ◽  
High Current ◽  
Shipping Industry ◽  
Bulk Shipping ◽  
Excess Volatility ◽  
Boom And Bust ◽  
Investment Boom ◽  
Industry Investment ◽  
Investment Response ◽  
Boom And Bust Cycles

Abstract We study the link between investment boom and bust cycles and returns on capital in the dry bulk shipping industry. We show that high current ship earnings are associated with high used ship prices and heightened industry investment in new ships, but forecast low future returns. We propose and estimate a behavioral model of industry cycles that can account for the evidence. In our model, firms overextrapolate exogenous demand shocks and partially neglect the endogenous investment response of their competitors. As a result, firms overpay for ships and overinvest in booms and are disappointed by the subsequent low returns. Formal estimation of the model suggests that modest expectational errors can result in dramatic excess volatility in prices and investment.

Scale Economies in Indian Commercial Banking Sector

2011 ◽  
Vol 2 (4) ◽  
pp. 13-30 ◽  
Author(s):  
Biresh K. Sahoo ◽  
Dieter Gstach
Keyword(s):  
Economies Of Scale ◽  
Banking Sector ◽  
Cost Model ◽  
Scale Economies ◽  
Commercial Banking ◽  
Foreign Banks ◽  
Constant Input ◽  
Diseconomies Of Scale ◽  
Translog Cost ◽  
Estimation Models

Two alternative estimation models, i.e., a translog cost function and data envelopment analysis (DEA) based on a cost model are compared and contrasted in revealing scale economies in the Indian commercial banking sector. The empirical results indicate that while the translog cost model exhibits increasing returns to scale for all the ownership groups, the DEA model reveals economies of scale only for foreign banks, diseconomies of scale for nationalized banks, and both economies and diseconomies of scale for private banks. The divergence of the results obtained from these two estimation models should concern model builders. From an empirical perspective the definition of scale economies through a constant input mix is very restrictive. The DEA cost model is much more flexible in this respect: It neither requires the restrictive assumptions that the unit factor prices are always available with certainty, nor that these prices are exogenous to the firms. However, the very volatile nature of the banking industry might question the validity of the empirical estimates in this deterministic setting. Therefore, further research is required to examine the bank performance behavior using both SFA and chance constrained DEA for the comparison in a stochastic setting.

Scale Economies in Indian Commercial Banking Sector

2012 ◽  
pp. 239-259
Author(s):  
Biresh K. Sahoo ◽  
Dieter Gstach
Keyword(s):  
Economies Of Scale ◽  
Banking Sector ◽  
Cost Model ◽  
Scale Economies ◽  
Commercial Banking ◽  
Empirical Estimates ◽  
Diseconomies Of Scale ◽  
Translog Cost ◽  
Definition Of ◽  
Estimation Models

Two alternative estimation models, i.e., a translog cost function and data envelopment analysis (DEA) based on a cost model are compared and contrasted in revealing scale economies in the Indian commercial banking sector. The empirical results indicate that while the translog cost model exhibits increasing returns to scale for all the ownership groups, the DEA model reveals economies of scale only for foreign banks, diseconomies of scale for nationalized banks, and both economies and diseconomies of scale for private banks. The divergence of the results obtained from these two estimation models should concern model builders. From an empirical perspective the definition of scale economies through a constant input mix is very restrictive. The DEA cost model is much more flexible in this respect: It neither requires the restrictive assumptions that the unit factor prices are always available with certainty, nor that these prices are exogenous to the firms. However, the very volatile nature of the banking industry might question the validity of the empirical estimates in this deterministic setting. Therefore, further research is required to examine the bank performance behavior using both SFA and chance constrained DEA for the comparison in a stochastic setting.

The Dynamics of Dry Bulk Shipping Market Under the Shipping Cycle Perspective: Market Relationships and Volatility

2018 ◽  
Vol 2672 (11) ◽  
pp. 1-9 ◽  
Author(s):  
Yijie Wu ◽  
Jingbo Yin ◽  
Pan Sheng
Keyword(s):  
Financial Crisis ◽  
Recovery Period ◽  
Granger Causality Test ◽  
Freight Rate ◽  
Shipping Industry ◽  
Causality Test ◽  
Bulk Shipping ◽  
Before And After ◽  
Causality Relationship ◽  
Freight Market

The shipping industry plays an essential role in world trade. For shipping companies, having an accurate view of the markets and grasp of the interactions between the freight market, second-hand ship market, and the newbuild ship markets is essential. The shipping market cycles are divided into four periods (trough, recovery, peak, and recession) based upon shipping cycle theory. The current shipping markets have been stuck in the trough period since the financial crisis in 2008. This paper investigates the recovery period and causality relationship between the freight rate, second-hand price, and new-build ship price, in the dry bulk shipping market and applies the Granger causality test at each stage of the cycle based on quantitative analysis. The results show that the recovery period and causality relationship can be identified only during the trough and peak periods. When comparing the results for the trough period before and after the financial crisis, we find similarities between the two periods, leading us to conclude that the shipping cycle rules still apply.

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