Núcleos de inflação no Brasil e poder preditivo da inflação total

Este trabalho tem por objetivo avaliar para o caso brasileiro uma das mais importantes propriedades esperadas de um núcleo: ser um bom previsor da inflação plena futura. Para tanto, foram utilizados como referência para comparação dois modelos construídos a partir das informações mensais do IPCA e seis modelos VAR referentes a cada uma das medidas de núcleo calculadas pelo Banco Central do Brasil. O desempenho das previsões foi avaliado pela comparação dos resultados do erro quadrático médio e pela aplicação da metodologia de Diebold-Mariano (1995) de comparação de modelos. Os resultados encontrados indicam que o atual conjunto de medidas de núcleos calculado pelo Banco Central não atende pelos critérios utilizados neste trabalho a essa característica desejada.

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.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise based upon data from a well known survey is also presented. Overall, theoretical and empirical results show promise for the feasible bias-corrected average forecast.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular it delivers a zero-limiting mean-squared error if the number of forecasts and the number of post-sample time periods is sufficiently large. We also develop a zero-mean test for the average bias. Monte-Carlo simulations are conducted to evaluate the performance of this new technique in finite samples. An empirical exercise, based upon data from well known surveys is also presented. Overall, these results show promise for the bias-corrected average forecast.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise, based upon data from a well known survey is also presented. Overall, these results show promise for the feasible bias-corrected average forecast.

Análise de desempenho dos fundos de investimento em ações brasileiros no período de Janeiro de 1997 a outubro de 2006

This research evaluated the quality of the management of Brazilian stock funds on the period from January 1997 to October 2006. The analysis was based on the Modern Portfolio Theory measures of performance. In addition, this research evaluated the relevance of the performance measures The sample with 21 funds was extracted from the 126 largest Brasilian stock options funds because they were the only with quotas on the whole period. The monthly mean rate of return and the following indexes were calculated: total return, mean monthly return, Jensen Index, Treynor Index, Sharpe Index, Sortino Index, Market Timing and the Mean Quadratic Error. The initial analysis showed that the funds in the sample had different objectives and limitations. To make valuable comparisons, the ANBID (National Association of Investment Banks) categories were used to classify the funds. The measured results were ranked. The positions of the funds on the rankings based on the mean monthly return and the indexes of Jensen, Treynor, Sortino and Sharpe were similar. All of the ten ACTIVE funds of this research were above the benchmark (IBOVESPA index) in the measures above. Based on the CAPM, the managers of these funds got superior performance because they might have compiled the available information in a superior way. The six funds belonging to the ANBID classification of INDEXED got the first six positions in the ranking based on the Mean Quadratic Error. None of the researched funds have shown market timing skills to move the beta of their portfolios in the right direction to take the benefit of the market movements, at the significance level of 5%.

Convex combinations of long memory estimates from different sampling rates

Convex combinations of long memory estimates using the same data observed at different sampling rates can decrease the standard deviation of the estimates, at the cost of inducing a slight bias. The convex combination of such estimates requires a preliminary correction for the bias observed at lower sampling rates, reported by Souza and Smith (2002). Through Monte Carlo simulations, we investigate the bias and the standard deviation of the combined estimates, as well as the root mean squared error (RMSE), which takes both into account. While comparing the results of standard methods and their combined versions, the latter achieve lower RMSE, for the two semi-parametric estimators under study (by about 30% on average for ARFIMA(0,d,0) series).

Microfounded forecasting

Our focus is on information in expectation surveys that can now be built on thousands (or millions) of respondents on an almost continuous-time basis (big data) and in continuous macroeconomic surveys with a limited number of respondents. We show that, under standard microeconomic and econometric techniques, survey forecasts are an affine function of the conditional expectation of the target variable. This is true whether or not the survey respondent knows the data-generating process (DGP) of the target variable or the econometrician knows the respondents individual loss function. If the econometrician has a mean-squared-error risk function, we show that asymptotically efficient forecasts of the target variable can be built using Hansens (Econometrica, 1982) generalized method of moments in a panel-data context, when N and T diverge or when T diverges with N xed. Sequential asymptotic results are obtained using Phillips and Moon s (Econometrica, 1999) framework. Possible extensions are also discussed.