Showing papers by "Francis X. Diebold published in 1995"
TL;DR: In this article, explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts are proposed and evaluated, and asymptotic and exact finite-sample tests are proposed, evaluated and illustrated.
Abstract: We propose and evaluate explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts. In contrast to previously developed tests, a wide variety of accuracy measures can be used (in particular, the loss function need not be quadratic and need not even be symmetric), and forecast errors can be non-Gaussian, nonzero mean, serially correlated, and contemporaneously correlated. Asymptotic and exact finite-sample tests are proposed, evaluated, and illustrated.
5,628 citations
01 Jan 1995
TL;DR: A short list of active subfields includes vector autoregressions, index and dynamic factor models, causality, integration and persistence, cointegration, seasonality, unobserved-components models, state-space representations and the Kalman filter, regime switching models, nonlinear dynamics, and optimal nonlinear filtering as discussed by the authors.
Abstract: Good macroeconomic and financial theorists, like all good theorists, want to get the facts straight before theorizing; hence, the explosive growth in the methodology and application of time-series econometrics in the last twenty-five years. Many factors fueled that growth, ranging from important developments in related fields (see Box and Jenkins, 1970) to dissatisfaction with the “incredible identifying restrictions” associated with traditional macroeconometric models (Sims, 1980) and the associated recognition that many tasks of interest, such as forecasting, simply do not require a structural model (see Granger and Newbold, 1979). A short list of active subfields includes vector autoregressions, index and dynamic factor models, causality, integration and persistence, cointegration, seasonality, unobserved-components models, state-space representations and the Kalman filter, regime-switching models, nonlinear dynamics, and optimal nonlinear filtering. Any such list must also include models of volatility dynamics. Models of autoregressive conditional heteroskedasticity (ARCH), in particular, provide parsimonious approximations to volatility dynamics and have found wide use in macroeconomics and finance1. The family of ARCH models is the subject of this chapter.
160 citations
Posted Content•
01 Jan 1995
TL;DR: The importance of forecast evaluation and combination techniques follows immediatelyforecast users naturally have a keen interest in monitoring and improving forecast performance as mentioned in this paper, and therefore, good forecasts lead to good decisions.
Abstract: Forecasts are of great importance and widely used in economics and finance. Quite simply, good forecasts lead to good decisions. The importance of forecast evaluation and combination techniques follows immediatelyforecast users naturally have a keen interest in monitoring and improving forecast performance. Here we provide a five-part selective account of forecast evaluation and combination methods. In the first, we discuss evaluation of a single forecast, and in particular, evaluation of whether and how it may be improved. In the second, we discuss the evaluation and comparison of the accuracy of competing forecasts. In the third, we discuss whether and how a set of forecasts may be combined to produce a superior composite forecast. In the fourth, we describe a number of forecast evaluation topics of particular relevance in economics and finance, including methods for evaluating direction-of-change forecasts, probability forecasts and volatility forecasts. In the fifth, we conclude.
15 citations
TL;DR: In this paper, the authors sketch the rudiments of a rather general univariate time-series model, allowing for dynamics in both the conditional mean and variance, and discuss both the economic and statistical motivation for the models, their properties, and issues related to estimation and testing.
Abstract: Recently there has been a great deal of interest in modeling volatility fluctuations ARCH models, for example, provide parsimonious approximations to volatility dynamics Here we provide a selective amount of certain aspects of conditional volatility modeling that are of particular relevance in macroeconomics and finance First, we sketch the rudiments of a rather general univariate time- series model, allowing for dynamics in both the conditional mean and variance Second, we discuss both the economic and statistical motivation for the models, we characterize their properties, and we discuss issues related to estimation and testing Finally, we discuss a variety of applications and extensions of the basic models
9 citations
Posted Content•
TL;DR: In this article, the authors sketch the rudiments of a rather general univariate time series model, allowing for dynamics in both the conditional mean and variance, and characterize their properties, and discuss issues related to estimation and testing.
Abstract: Recently there has been a great deal of interest in modeling volatility fluctuations. ARCH models, for example, provide parsimonious approximations to volatility dynamics. Here we provide a selective amount of certain aspects of conditional volatility modeling that are of particular relevance in macroeconomics and finance. First, we sketch the rudiments of a rather general univariate time- series model, allowing for dynamics in both the conditional mean and variance. Second, we discuss both the economic and statistical motivation for the models, we characterize their properties, and we discuss issues related to estimation and testing. Finally, we discuss a variety of applications and extensions of the basic models.
1 citations