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on Forecasting |
| By: | Rob J. Hyndman; Yeasmin Khandakar |
| Abstract: | Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algorithms are applicable to both seasonal and non-seasonal data, and are compared and illustrated using four real time series. We also briefly describe some of the other functionality available in the forecast package. |
| Keywords: | ARIMA models; automatic forecasting; exponential smoothing; prediction intervals; state space models; time series, R. |
| JEL: | C53 C22 C52 |
| Date: | 2007–06 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2007-6&r=for |
| By: | Pim Ouwehand; Rob J. Hyndman; Ton G. de Kok; Karel H. van Donselaar |
| Abstract: | We present an approach to improve forecast accuracy by simultaneously forecasting a group of products that exhibit similar seasonal demand patterns. Better seasonality estimates can be made by using information on all products in a group, and using these improved estimates when forecasting at the individual product level. This approach is called the group seasonal indices (GSI) approach, and is a generalization of the classical Holt-Winters procedure. This article describes an underlying state space model for this method and presents simulation results that show when it yields more accurate forecasts than Holt-Winters. |
| Keywords: | Common seasonality; demand forecasting; exponential smoothing; Holt-Winters; state space model. |
| JEL: | C53 C22 C52 |
| Date: | 2007–06 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2007-7&r=for |
| By: | Michiel De Pooter (Erasmus Universiteit Rotterdam) |
| Abstract: | In this paper I examine various extensions of the Nelson and Siegel (1987) model with the purpose of fitting and forecasting the term structure of interest rates. As expected, I find that using more flexible models leads to a better in-sample fit of the term structure. However, I show that the out-of-sample predictability improves as well. The four-factor model, which adds a second slope factor to the three-factor Nelson-Siegel model, forecasts particularly well. Especially with a one-step state-space estimation approach the four-factor model produces accurate forecasts and outperforms competitor models across maturities and forecast horizons. Subsample analysis shows that this outperformance is also consistent over time. |
| Keywords: | Term structure of interest rates; Nelson-Siegel; Svensson; Forecasting; State-space model |
| JEL: | E4 C5 C32 |
| Date: | 2007–06–11 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20070043&r=for |
| By: | Ralf Becker; Adam Clements |
| Abstract: | Forecasting volatility has received a great deal of research attention. Many articles have considered the relative performance of econometric model based and option implied volatility forecasts. While many studies have found that implied volatility is the preferred approach, a number of issues remain unresolved. One issue being the relative merit of combination forecasts. By utilising recent econometric advances, this paper considers whether combination forecasts of S&P 500 volatility are statistically superior to a wide range of model based forecasts and implied volatility. It is found that combination forecasts are the dominant approach, indicating that the VIX cannot simply be viewed as a combination of various model based forecasts. |
| Keywords: | Implied volatility, volatility forecasts, volatility models, realized volatility, combination forecasts. |
| JEL: | C12 C22 G00 |
| Date: | 2007–06–14 |
| URL: | http://d.repec.org/n?u=RePEc:qut:auncer:2007-92&r=for |
| By: | Ralf Becker; Adam Clements |
| Abstract: | This paper presents a GARCH type volatility model with a time-varying unconditional volatility which is a function of macroeconomic information. It is an extension of the SPLINE GARCH model proposed by Engle and Rangel (2005). The advantage of the model proposed in this paper is that the macroeconomic information available (and/or forecasts)is used in the parameter estimation process. Based on an application of this model to S&P500 share index returns, it is demonstrated that forecasts of macroeconomic variables can be easily incorporated into volatility forecasts for share index returns. It transpires that the model proposed here can lead to significantly improved volatility forecasts compared to traditional GARCH type volatility models. |
| Keywords: | Volatility, macroeconomic data, forecast, spline, GARCH. |
| JEL: | C12 C22 G00 |
| Date: | 2007–06–14 |
| URL: | http://d.repec.org/n?u=RePEc:qut:auncer:2007-93&r=for |
| By: | Vivek B. Arora |
| Abstract: | The transparency of monetary policy in South Africa has increased substantially since the end of the 1990s; but little empirical work has been done to examine the economic benefits of the increased transparency. This paper shows that, in recent years, South African private sector forecasters have become better able to forecast interest rates, are less surprised by reserve bank policy announcements, and are less diverse in the cross-sectional variety of their interest rate forecasts. In addition, there is some evidence that the accuracy of inflation forecasts has increased. The improvements in interest rate and inflation forecasts have exceeded those in real output forecasts, suggesting that increases in reserve bank transparency are likely to have played a role. |
| Date: | 2007–05–29 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:07/123&r=for |
| By: | cipollini, andrea; missaglia, giuseppe |
| Abstract: | In this paper we use a reduced form model for the analysis of Portfolio Credit Risk. For this purpose, we fit a Dynamic Factor model, DF, to a large dataset of default rates proxies and macro-variables for Italy. Multi step ahead density and probability forecasts are obtained by employing both the direct and indirect method of prediction together with stochastic simulation of the DF model. We, first, find that the direct method is the best performer regarding the out of sample projection of financial distressful events. In a second stage of the analysis, the direct method of forecasting through principal components is shown to provide the least sensitive measures of Portfolio Credit Risk to various multifactor model specifications. Finally, the simulation results suggest that the benefits in terms of credit risk diversification tend to diminish with an increasing number of factors, especially when using the indirect method of forecasting. |
| Keywords: | Dynamic Factor Model; Forecasting; Stochastic Simulation; Risk Management; Banking |
| JEL: | G33 C53 G21 |
| Date: | 2007–05–30 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:3582&r=for |
| By: | Jeremy J. Nalewaik |
| Abstract: | This paper discusses extensions of standard Markov switching models that allow estimated probabilities to reflect parameter breaks at or close to the end of the sample, too close for standard maximum likelihood techniques to produce precise parameter estimates. The basic technique is a supplementary estimation procedure, bringing additional information to bear to estimate the statistical properties of the end-of-sample observations that behave differently from the rest. Empirical results using real-time data show that these techniques improve the ability of a Markov switching model based on GDP and GDI to recognize the start of the 2001 recession. |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2007-23&r=for |
| By: | Noureddine Krichene |
| Abstract: | This paper formulated a short-run model, with an explicit role for monetary policy, for analyzing world oil and gas markets. The model described carefully the parameters of these markets and their vulnerability to business cycles. Estimates showed that short-run demand for oil and gas was price- inelastic, relatively income-elastic, and was influenced by interest and exchange rates; short-run supply was price-inelastic. Short-run price inelasticity could be a source for high volatility in oil and gas prices, and could confer to producers a temporary market power. Being simultaneous and incorporating interest and exchange rates, the model could be useful in short-term forecasting of oil and gas outputs and prices under policy scenarios. |
| Date: | 2007–06–12 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:07/135&r=for |
| By: | Inmaculada Martinez-Zarzoso; Felicitas Nowak-Lehmann D.; Sebastian Vollmer |
| Abstract: | This paper evaluates the performance of alternative estimation methods for multiplicative and log models with heteroskedasticity. Contrary to Santos Silva and Tenreyro (2006), the results of a simulation study indicate that the Pseudo Poisson Maximum Likelihood estimator (PPML) is not always the best estimator. New estimates of the gravity equation are obtained for three different datasets with traditional methods (OLS and FGLS) and with the PPML. We find that the PPML assumption concerning the pattern of heteroskedasticity is, in most cases, rejected by the data and PPML estimates are outperformed by OLS and FGLS estimates in out-of-sample forecast. |
| Keywords: | Simulations; Poisson regression; constant-elasticity models; heteroskedasticity; Feasible Generalized Least Squares (FGLS); Maximum Likelihood |
| JEL: | C33 Q25 |
| Date: | 2007–06–18 |
| URL: | http://d.repec.org/n?u=RePEc:got:cegedp:64&r=for |