Productivity of nations: a stochastic frontier approach to TFP decomposition

This Paper Tackles the Problem of Aggregate Tfp Measurement Using Stochastic Frontier Analysis (Sfa). Data From Penn World Table 6.1 are Used to Estimate a World Production Frontier For a Sample of 75 Countries Over a Long Period (1950-2000) Taking Advantage of the Model Offered By Battese and Coelli (1992). We Also Apply the Decomposition of Tfp Suggested By Bauer (1990) and Kumbhakar (2000) to a Smaller Sample of 36 Countries Over the Period 1970-2000 in Order to Evaluate the Effects of Changes in Efficiency (Technical and Allocative), Scale Effects and Technical Change. This Allows Us to Analyze the Role of Productivity and Its Components in Economic Growth of Developed and Developing Nations in Addition to the Importance of Factor Accumulation. Although not Much Explored in the Study of Economic Growth, Frontier Techniques Seem to Be of Particular Interest For That Purpose Since the Separation of Efficiency Effects and Technical Change Has a Direct Interpretation in Terms of the Catch-Up Debate. The Estimated Technical Efficiency Scores Reveal the Efficiency of Nations in the Production of Non Tradable Goods Since the Gdp Series Used is Ppp-Adjusted. We Also Provide a Second Set of Efficiency Scores Corrected in Order to Reveal Efficiency in the Production of Tradable Goods and Rank Them. When Compared to the Rankings of Productivity Indexes Offered By Non-Frontier Studies of Hall and Jones (1996) and Islam (1995) Our Ranking Shows a Somewhat More Intuitive Order of Countries. Rankings of the Technical Change and Scale Effects Components of Tfp Change are Also Very Intuitive. We Also Show That Productivity is Responsible For Virtually All the Differences of Performance Between Developed and Developing Countries in Terms of Rates of Growth of Income Per Worker. More Important, We Find That Changes in Allocative Efficiency Play a Crucial Role in Explaining Differences in the Productivity of Developed and Developing Nations, Even Larger Than the One Played By the Technology Gap

Cash flow and discount rate risk decomposition and ICAPM for the US and brazilian stock markets

Esse trabalho é uma aplicação do modelo intertemporal de apreçamento de ativos desenvolvido por Campbell (1993) e Campbell e Vuolteenaho (2004) para as carteiras de Fama-French 2x3 brasileiras no period de janeiro de 2003 a abril de 2012 e para as carteiras de Fama-French 5x5 americanas em diferentes períodos. As varíaveis sugeridas por Campbell e Vuolteenaho (2004) para prever os excessos de retorno do mercado acionário americano no period de 1929 a 2001 mostraram-se também bons preditores de excesso de retorno para o mercado brasileiro no período recente, com exceção da inclinação da estrutura a termo das taxas de juros. Entretanto, mostramos que um aumento no small stock value spread indica maior excesso de retorno no futuro, comportamento que não é coerente com a explicação para o prêmio de valor sugerida pelo modelo intertemporal. Ainda, utilizando os resíduos do VAR preditivo para definir o risco de choques de fluxo de caixa e de choques nas taxas de desconto das carteiras de teste, verificamos que o modelo intertemporal resultante não explica adequadamente os retornos observados. Para o mercado norte-americano, concluímos que a abilidade das variáveis propostas para explicar os excessos de retorno do mercado varia no tempo. O sucesso de Campbell e Vuolteenaho (2004) em explicar o prêmio de valor para o mercado norte-americano na amostra de 1963 a 2001 é resultado da especificação do VAR na amostra completa, pois mostramos que nenhuma das varíaveis é um preditor de retorno estatisticamente significante nessa sub-amostra.

Convergence analysis of sampling-based decomposition methods for risk-averse multistage stochastic convex programs

We consider a class of sampling-based decomposition methods to solve risk-averse multistage stochastic convex programs. We prove a formula for the computation of the cuts necessary to build the outer linearizations of the recourse functions. This formula can be used to obtain an efficient implementation of Stochastic Dual Dynamic Programming applied to convex nonlinear problems. We prove the almost sure convergence of these decomposition methods when the relatively complete recourse assumption holds. We also prove the almost sure convergence of these algorithms when applied to risk-averse multistage stochastic linear programs that do not satisfy the relatively complete recourse assumption. The analysis is first done assuming the underlying stochastic process is interstage independent and discrete, with a finite set of possible realizations at each stage. We then indicate two ways of extending the methods and convergence analysis to the case when the process is interstage dependent.