94 research outputs found

    Is Seasonal Adjustment a Linear or Nonlinear Data Filtering Process?

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    In this paper, we investigate whether seasonal adjustment procedures are, at least approximately, linear data transformations. This question is important with respect to many issues including estimation of regression models with seasonally adjusted data. We focus on the X-11 program and first review the features of the program that might be potential sources of nonlinearity. We rely on simulation evidence, involving linear unobserved component ARIMA models, to assess the adequacy of the linear approximation. We define a set of properties for the adequacy of a linear approximation to a seasonal adjustment filter. These properties are examined through statistical tests. Next, we study the effect of X-11 seasonal adjustment on regression statistics assessing the statistical significance of the relationship between economic variables in the same spirit as Sims (1974) and Wallis (1974). These findings are complemented with several empirical examples involving economic data. Nous examinons si la procédure d'ajustement X-11 est approximativement linéaire. Il y a potentiellement plusieurs sources de non-linéarité dans cette procédure. Le but de l'étude est de savoir si ces sources sont effectivement assez importantes pour affecter, par exemple, des résultats d'estimation dans des modèles de régression linéaire. La seule façon de répondre à cette question est par estimation. Nous proposons plusieurs critères qu'on peut utiliser pour juger si une procédure d'ajustement est approximativement linéaire. Nous examinons également par simulation des propriétés de tests dans le modèle de régression dans le même esprit que Sims (1974) et Wallis (1974).X-11 program; Nonlinearity, X-11 program ; Nonlinearity

    Copycats and Common Swings: The Impact of the Use of Forecasts in Information Sets

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    This paper presents evidence, using data from Consensus Forecasts, that there is an "attraction" to conform to the mean forecasts; in other words, views expressed by other forecasters in the previous period influence individuals' current forecast. The paper then discusses--and provides further evidence on--two important implications of this finding. The first is that the forecasting performance of these groups may be severely affected by the detected imitation behavior and lead to convergence to a value that is not the "right" target. Second, since the forecasts are not independent, the common practice of using the standard deviation from the forecasts' distribution, as if they were standard errors of the estimated mean, is not warranted. Copyright 2002, International Monetary Fund

    Seasonal Adjustment and Volatility Dynamics

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    In this paper we try to enhance our understanding of the effect of filtering, particularly seasonal adjustment filtering, on the estimation of volatility models. We focus exclusively on ARCH models as a specific class of models and examine the effect of both linear and nonlinear filters on (seasonal) volatility dynamics. The case of linear filters is treated in a general abstract setting applicable to seasonal adjustment as well as various other linear filters often applied to transform raw data. Next we focus on specific cases like the first and seasonal differencing filters as well as the X-11 filter, both its linear representation and the (nonlinear) procedure implemented in practice. We uncover surprising features regarding the linear X-11 filter, e.g. it introduces a small seasonal pattern in volatility. More interestingly, we show that the linear X-11 and the actual procedure produce serious downward biases in ARCH effects and their persistence. Finally, we uncover important differences between the linear version of X-11 and the actual procedure. Nous étudions l'effet de filtre sur l'estimation de processus de type GARCH. Le cas du filtre linéaire est analysé dans un contexte général pour des processus GARCH faibles. Plusieurs cas spéciaux sont discutés, notamment ce-lui du filtre d'ajustement X-11 pour les effets saisonniers. Nous trouvons que ce filtre produit un effet de persistance saisonnière au niveau de la volatilité. Nous abordons ensuite le filtrage non linéaire dans le cas du filtre X-11. Une étude de Monte Carlo démontre qu'il y a des différences très importantes entre la représentation linéaire du filtre et le programme non linéaire appliqué aux données réelles.GARCH processes, seasonality, X-11, Processus GARCH, Saisonnalité, X-11

    Extracting Information from Mega-Panels and High-Frequency Data

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    Very large data sets in economics are already available and will soon become commonplace. The econometric techniques currently in use may not be relevant and new techniques will have to be devised. It can be argued that most tests of significance, linear models, assumptions of normality, and procedures to reduce bias, for example, will be replaced. The usefulness of asymptotic theory is discussed. It is suggested that methods for extracting conditional distributions will be becomes especially useful and a few particular possible techniques are suggested

    Some thoughts on the development of cointegration

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    This paper describes how the notion of cointegration came about, and discusses some generalizations to indicate where the topic may go next. In particular, some issues in the analysis of possibly cointegrated quantile time series are discussed.

    Outline of forecast theory using generalized cost functions

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    The cost functions used to form forecasts in practice may be quite different than the squared costs that is often assumed in forecast theory. The impact on evaluation procedures is determined and simple properties for the derivate of the cost function of the errors are found to provide simple tests of optimality. For a very limited class of situations are forecasts based on conditional means optimal, generally, the econometricians needs to provide the whole conditional predicted distribution. Implications for multi-step forecasts and the combination of forecasts are briefly considered.Optimum forecasts, cost functions, predictive distribution
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