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

The role of no-arbitrage on forecasting: lessons from a parametric term structure model

Parametric term structure models have been successfully applied to innumerous problems in fixed income markets, including pricing, hedging, managing risk, as well as studying monetary policy implications. On their turn, dynamic term structure models, equipped with stronger economic structure, have been mainly adopted to price derivatives and explain empirical stylized facts. In this paper, we combine flavors of those two classes of models to test if no-arbitrage affects forecasting. We construct cross section (allowing arbitrages) and arbitrage-free versions of a parametric polynomial model to analyze how well they predict out-of-sample interest rates. Based on U.S. Treasury yield data, we find that no-arbitrage restrictions significantly improve forecasts. Arbitrage-free versions achieve overall smaller biases and Root Mean Square Errors for most maturities and forecasting horizons. Furthermore, a decomposition of forecasts into forward-rates and holding return premia indicates that the superior performance of no-arbitrage versions is due to a better identification of bond risk premium.

The evolution of international output differences (1960-2000): From factors to productivity

This article presents a group of exercises of level and growth decomposition of output per worker using cross-country data from 1960 to 2000. It is shown that at least until 1975 factors of production (capital and education) were the main source of output dispersion across economies and that productivity variance was considerably smaller than in late years. Only after this date the prominence of productivity started to show up in the data, as the majority of the literature has found. The growth decomposition exercises showed that the reversal of relative importance of productivity vis-a-vis factors is explained by the very good (bad) performance of productivity of fast (slow) growing economies. Although growth in the period, on average, is mostly due to factors accumulation, its variance is explained by productivity.

The evolution of international output differences (1960-2000) : from factor to productivity

This article presents a group of exercises of leveI and growth decomposition of output per worker using cross-collntry data from 1960 to :2000. It is shown that at least llntil 197.5 factors of production (capital anel education) ",ere the main source of output dispersion across ecoIlomies and that productivity variance was considerably srnaller than in late years. Qnly after this date the prominence of productivity started to sho\\' up in the data. as the majority of the litcrature has found. The gro\\'th decomposition exercises showecl that t he reversal of relative irnportance of proeluctivity vis-a-\'is factors is explainecl by the very good (bad) performance of procluctivity of fast (slow) growing cconomies. Although growth in the pcriod, on avcragc. is mostly clue to factors accumulation. its variance is explained by productivity.