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Out-of-sample Forecasts (out-of-sample + forecast)
Selected AbstractsForecasting German GDP using alternative factor models based on large datasetsJOURNAL OF FORECASTING, Issue 4 2007Christian Schumacher Abstract This paper discusses the forecasting performance of alternative factor models based on a large panel of quarterly time series for the German economy. One model extracts factors by static principal components analysis; the second model is based on dynamic principal components obtained using frequency domain methods; the third model is based on subspace algorithms for state-space models. Out-of-sample forecasts show that the forecast errors of the factor models are on average smaller than the errors of a simple autoregressive benchmark model. Among the factor models, the dynamic principal component model and the subspace factor model outperform the static factor model in most cases in terms of mean-squared forecast error. However, the forecast performance depends crucially on the choice of appropriate information criteria for the auxiliary parameters of the models. In the case of misspecification, rankings of forecast performance can change severely.,,Copyright © 2007 John Wiley & Sons, Ltd. [source] Unemployment variation over the business cycles: a comparison of forecasting modelsJOURNAL OF FORECASTING, Issue 7 2004Saeed Moshiri Abstract Asymmetry has been well documented in the business cycle literature. The asymmetric business cycle suggests that major macroeconomic series, such as a country's unemployment rate, are non-linear and, therefore, the use of linear models to explain their behaviour and forecast their future values may not be appropriate. Many researchers have focused on providing evidence for the non-linearity in the unemployment series. Only recently have there been some developments in applying non-linear models to estimate and forecast unemployment rates. A major concern of non-linear modelling is the model specification problem; it is very hard to test all possible non-linear specifications, and to select the most appropriate specification for a particular model. Artificial neural network (ANN) models provide a solution to the difficulty of forecasting unemployment over the asymmetric business cycle. ANN models are non-linear, do not rely upon the classical regression assumptions, are capable of learning the structure of all kinds of patterns in a data set with a specified degree of accuracy, and can then use this structure to forecast future values of the data. In this paper, we apply two ANN models, a back-propagation model and a generalized regression neural network model to estimate and forecast post-war aggregate unemployment rates in the USA, Canada, UK, France and Japan. We compare the out-of-sample forecast results obtained by the ANN models with those obtained by several linear and non-linear times series models currently used in the literature. It is shown that the artificial neural network models are able to forecast the unemployment series as well as, and in some cases better than, the other univariate econometrics time series models in our test. Copyright © 2004 John Wiley & Sons, Ltd. [source] Non-linear interest rate dynamics and forecasting: evidence for US and Australian interest ratesINTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, Issue 2 2009David G. McMillan Abstract Recent empirical finance research has suggested the potential for interest rate series to exhibit non-linear adjustment to equilibrium. This paper examines a variety of models designed to capture these effects and compares both their in-sample and out-of-sample performance with a linear alternative. Using short- and long-term interest rates we report evidence that a logistic smooth-transition error-correction model is able to best characterize the data and provide superior out-of-sample forecasts, especially for the short rate, over both linear and non-linear alternatives. This model suggests that market dynamics differ depending on whether the deviations from long-run equilibrium are above or below the threshold value. Copyright © 2007 John Wiley & Sons, Ltd. [source] Conventional and unconventional approaches to exchange rate modelling and assessmentINTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, Issue 1 2008Ron Alquist Abstract We examine the relative predictive power of the sticky price monetary model, uncovered interest parity, and a transformation of net exports and net foreign assets. In addition to bringing Gourinchas and Rey's new approach and more recent data to bear, we implement the Clark,West procedure for testing the significance of out-of-sample forecasts. The interest rate parity relation holds better at long horizons and the net exports variable does well in predicting exchange rates at short horizons in sample. In out-of-sample forecasts, we find evidence that our proxy for Gourinchas and Rey's measure of external imbalances outperforms a random walk at short horizons as do some of the other models, although no single model uniformly beats the random walk forecast. Copyright © 2007 John Wiley & Sons, Ltd. [source] Bananas and petrol: further evidence on the forecasting accuracy of the ABS ,headline' and ,underlying' rates of inflationJOURNAL OF FORECASTING, Issue 6 2010Liam J. A. Lenten Abstract In the light of the still topical nature of ,bananas and petrol' being blamed for driving much of the inflationary pressures in Australia in recent times, the ,headline' and ,underlying' rates of inflation are scrutinised in terms of forecasting accuracy. A general structural time-series modelling strategy is applied to estimate models for alternative types of Consumer Price Index (CPI) measures. From this, out-of-sample forecasts are generated from the various models. The underlying forecasts are subsequently adjusted to facilitate comparison. The Ashley, Granger and Schmalensee (1980) test is then performed to determine whether there is a statistically significant difference between the root mean square errors of the models. The results lend weight to the recent findings of Song (2005) that forecasting models using underlying rates are not systematically inferior to those based on the headline rate. In fact, strong evidence is found that underlying measures produce superior forecasts. Copyright © 2009 John Wiley & Sons, Ltd. [source] A New-Keynesian DSGE model for forecasting the South African economyJOURNAL OF FORECASTING, Issue 5 2009Dave' Liu, Guangling Abstract This paper develops a New-Keynesian Dynamic Stochastic General Equilibrium (NKDSGE) model for forecasting the growth rate of output, inflation, and the nominal short-term interest rate (91 days Treasury Bill rate) for the South African economy. The model is estimated via maximum likelihood technique for quarterly data over the period of 1970:1,2000:4. Based on a recursive estimation using the Kalman filter algorithm, out-of-sample forecasts from the NKDSGE model are compared with forecasts generated from the classical and Bayesian variants of vector autoregression (VAR) models for the period 2001:1,2006:4. The results indicate that in terms of out-of-sample forecasting, the NKDSGE model outperforms both the classical and Bayesian VARs for inflation, but not for output growth and nominal short-term interest rate. However, differences in RMSEs are not significant across the models. Copyright © 2008 John Wiley & Sons, Ltd. [source] Forecasting with panel data,JOURNAL OF FORECASTING, Issue 2 2008Badi H. Baltagi Abstract This paper gives a brief survey of forecasting with panel data. It begins with a simple error component regression model and surveys the best linear unbiased prediction under various assumptions of the disturbance term. This includes various ARMA models as well as spatial autoregressive models. The paper also surveys how these forecasts have been used in panel data applications, running horse races between heterogeneous and homogeneous panel data models using out-of-sample forecasts. Copyright © 2008 John Wiley & Sons, Ltd. [source] Bias in the estimation of non-linear transformations of the integrated variance of returnsJOURNAL OF FORECASTING, Issue 7 2006Richard D. F. Harris Abstract Volatility models such as GARCH, although misspecified with respect to the data-generating process, may well generate volatility forecasts that are unconditionally unbiased. In other words, they generate variance forecasts that, on average, are equal to the integrated variance. However, many applications in finance require a measure of return volatility that is a non-linear function of the variance of returns, rather than of the variance itself. Even if a volatility model generates forecasts of the integrated variance that are unbiased, non-linear transformations of these forecasts will be biased estimators of the same non-linear transformations of the integrated variance because of Jensen's inequality. In this paper, we derive an analytical approximation for the unconditional bias of estimators of non-linear transformations of the integrated variance. This bias is a function of the volatility of the forecast variance and the volatility of the integrated variance, and depends on the concavity of the non-linear transformation. In order to estimate the volatility of the unobserved integrated variance, we employ recent results from the realized volatility literature. As an illustration, we estimate the unconditional bias for both in-sample and out-of-sample forecasts of three non-linear transformations of the integrated standard deviation of returns for three exchange rate return series, where a GARCH(1, 1) model is used to forecast the integrated variance. Our estimation results suggest that, in practice, the bias can be substantial.,,Copyright © 2006 John Wiley & Sons, Ltd. [source] Forecasting the conditional covariance matrix of a portfolio under long-run temporal dependenceJOURNAL OF FORECASTING, Issue 6 2006Trino-Manuel Ñíguez Abstract Long-range persistence in volatility is widely modelled and forecast in terms of the so-called fractional integrated models. These models are mostly applied in the univariate framework, since the extension to the multivariate context of assets portfolios, while relevant, is not straightforward. We discuss and apply a procedure which is able to forecast the multivariate volatility of a portfolio including assets with long memory. The main advantage of this model is that it is feasible enough to be applied on large-scale portfolios, solving the problem of dealing with extremely complex likelihood functions which typically arises in this context. An application of this procedure to a portfolio of five daily exchange rate series shows that the out-of-sample forecasts for the multivariate volatility are improved under several loss functions when the long-range dependence property of the portfolio assets is explicitly accounted for.,,Copyright © 2006 John Wiley & Sons, Ltd. [source] Long-memory forecasting of US monetary indicesJOURNAL OF FORECASTING, Issue 4 2006John Barkoulas Abstract Several studies have tested for long-range dependence in macroeconomic and financial time series but very few have assessed the usefulness of long-memory models as forecast-generating mechanisms. This study tests for fractional differencing in the US monetary indices (simple sum and divisia) and compares the out-of-sample fractional forecasts to benchmark forecasts. The long-memory parameter is estimated using Robinson's Gaussian semi-parametric and multivariate log-periodogram methods. The evidence amply suggests that the monetary series possess a fractional order between one and two. Fractional out-of-sample forecasts are consistently more accurate (with the exception of the M3 series) than benchmark autoregressive forecasts but the forecasting gains are not generally statistically significant. In terms of forecast encompassing, the fractional model encompasses the autoregressive model for the divisia series but neither model encompasses the other for the simple sum series.,,Copyright © 2006 John Wiley & Sons, Ltd. [source] Daily volatility forecasts: reassessing the performance of GARCH modelsJOURNAL OF FORECASTING, Issue 6 2004David G. McMillan Abstract Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, accurate measures and good forecasts of volatility are crucial for the implementation and evaluation of asset and derivative pricing models in addition to trading and hedging strategies. However, whilst GARCH models are able to capture the observed clustering effect in asset price volatility in-sample, they appear to provide relatively poor out-of-sample forecasts. Recent research has suggested that this relative failure of GARCH models arises not from a failure of the model but a failure to specify correctly the ,true volatility' measure against which forecasting performance is measured. It is argued that the standard approach of using ex post daily squared returns as the measure of ,true volatility' includes a large noisy component. An alternative measure for ,true volatility' has therefore been suggested, based upon the cumulative squared returns from intra-day data. This paper implements that technique and reports that, in a dataset of 17 daily exchange rate series, the GARCH model outperforms smoothing and moving average techniques which have been previously identified as providing superior volatility forecasts. Copyright © 2004 John Wiley & Sons, Ltd. [source] Forecasting with leading indicators revisitedJOURNAL OF FORECASTING, Issue 8 2003Ruey S. Tsay Abstract Transfer function or distributed lag models are commonly used in forecasting. The stability of a constant-coefficient transfer function model, however, may become an issue for many economic variables due in part to the recent advance in technology and improvement in efficiency in data collection and processing. In this paper, we propose a simple functional-coefficient transfer function model that can accommodate the changing environment. A likelihood ratio statistic is used to test the stability of a traditional transfer function model. We investigate the performance of the test statistic in the finite sample case via simulation. Using some well-known examples, we demonstrate clearly that the proposed functional-coefficient model can substantially improve the accuracy of out-of-sample forecasts. In particular, our simple modification results in a 25% reduction in the mean squared errors of out-of-sample one-step-ahead forecasts for the gas-furnace data of Box and Jenkins. Copyright © 2003 John Wiley & Sons, Ltd. [source] Forecasting high-frequency financial data with the ARFIMA,ARCH modelJOURNAL OF FORECASTING, Issue 7 2001Michael A. Hauser Abstract Financial data series are often described as exhibiting two non-standard time series features. First, variance often changes over time, with alternating phases of high and low volatility. Such behaviour is well captured by ARCH models. Second, long memory may cause a slower decay of the autocorrelation function than would be implied by ARMA models. Fractionally integrated models have been offered as explanations. Recently, the ARFIMA,ARCH model class has been suggested as a way of coping with both phenomena simultaneously. For estimation we implement the bias correction of Cox and Reid (1987). For daily data on the Swiss 1-month Euromarket interest rate during the period 1986,1989, the ARFIMA,ARCH (5,d,2/4) model with non-integer d is selected by AIC. Model-based out-of-sample forecasts for the mean are better than predictions based on conditionally homoscedastic white noise only for longer horizons (, > 40). Regarding volatility forecasts, however, the selected ARFIMA,ARCH models dominate. Copyright © 2001 John Wiley & Sons, Ltd. [source] Term premia and the maturity composition of the Federal debt: new evidence from the term structure of interest ratesJOURNAL OF FORECASTING, Issue 7 2001Basma Bekdache Abstract This paper models bond term premia empirically in terms of the maturity composition of the federal debt and other observable economic variables in a time-varying framework with potential regime shifts. We present regression and out-of sample forecasting results demonstrating that information on the age composition of the Federal debt is useful for forecasting term premia. We show that the multiprocess mixture model, a multi-state time-varying parameter model, outperforms the commonly used GARCH model in out-of-sample forecasts of term premia. The results underscore the importance of modelling term premia, as a function of economic variables rather than just as a function of asset covariances as in the conditional heteroscedasticity models. Copyright © 2001 John Wiley & Sons, Ltd. [source] | ||||||||||