nep-for New Economics Papers
on Forecasting
Issue of 2010‒05‒02
fifteen papers chosen by
Rob J Hyndman
Monash University

  1. A comparison of ten principal component methods for forecasting mortality rates By Han Lin Shang; Rob J Hyndman; Heather Booth
  2. Macroeconomic forecasting and structural change By Antonello D’Agostino; Luca Gambetti; Domenico Giannone
  3. Strategic Forecasting on the FOMC By Peter Tillmann
  4. Decision-Based Forecast Evaluation of UK Interest Rate Predictability* By Stephen Hall; Kavita Sirichand
  5. Forecast Densities for Economic Aggregates from Disaggregate Ensembles By Francesco Ravazzolo; Shaun P. Vahey
  6. Term structure forecasting using macro factors and forecast combination By Michiel De Pooter; Francesco Ravazzolo; Dick van Dijk
  7. Are Forecast Updates Progressive? By Chia-Lin Chang; Philip Hans Franses; Michael McAleer
  8. Evaluating Macroeconomic Forecasts: A Review of Some Recent Developments By Philip Hans Franses; Michael McAleer; Rianne Legerstee
  9. How Accurate are Government Forecasts of Economic Fundamentals? The Case of Taiwan By Chia-Lin Chang; Philip Hans Franses; Michael McAleer
  10. Should We Trust in Leading Indicators? Evidence from the Recent Recession By Katja Drechsel; Rolf Scheufele
  11. Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions By George Athanasopoulos; Osmani Teixeira de Carvalho Guillén; João Victor Issler; Farshid Vahid
  12. Simple rules versus optimal policy: what fits? By Ida Wolden Bache; Leif Brubakk; Junior Maih
  13. Smooth Transition Patterns in the Realized Stock Bond Correlation By Nektarios Aslanidis; Charlotte Christiansen
  14. A Simple Expected Volatility (SEV) Index: Application to SET50 Index Options By Michael McAleer; Chatayan Wiphatthanananthakul
  15. General Equilibrium Restrictions for Dynamic Factor Models By David de Antonio Liedo

  1. By: Han Lin Shang; Rob J Hyndman; Heather Booth
    Abstract: Using the age- and sex-specific data of 14 developed countries, we compare the short- to medium-term accuracy of ten principal component methods for forecasting mortality rates and life expectancy. These ten methods include the Lee-Carter method and many of its variants and extensions. For forecasting mortality rates, the weighted Hyndman-Ullah method provides the most accurate point forecasts, while the Lee-Miller method gives the best point forecast accuracy of life expectancy. Furthermore, the weighted Hyndman-Ullah method provides the most accurate interval forecasts of mortality rates, while the robust Hyndman-Ullah method provides the best interval forecast accuracy of life expectancy.
    Keywords: Mortality forecasting, life expectancy forecasting, principal component methods, Lee-Carter method, interval forecasts, forecasting time series
    JEL: C14 C23
    Date: 2010–04–09
  2. By: Antonello D’Agostino (Central Bank and Financial Services Authority of Ireland – Economic Analysis and Research Department, PO Box 559 – Dame Street, Dublin 2, Ireland.); Luca Gambetti (Office B3.174, Departament d’Economia i Historia Economica, Edifici B, Universitat Autonoma de Barcelona, Bellaterra 08193, Barcelona, Spain.); Domenico Giannone (ECARES Université Libre de Bruxelles, 50, Avenue Roosevelt CP 114 Brussels, Belgium.)
    Abstract: The aim of this paper is to assess whether explicitly modeling structural change increases the accuracy of macroeconomic forecasts. We produce real time out-of-sample forecasts for inflation, the unemployment rate and the interest rate using a Time-Varying Coefficients VAR with Stochastic Volatility (TV-VAR) for the US. The model generates accurate predictions for the three variables. In particular for inflation the TV-VAR outperforms, in terms of mean square forecast error, all the competing models: fixed coefficients VARs, Time-Varying ARs and the na¨ıve random walk model. These results are also shown to hold over the most recent period in which it has been hard to forecast inflation. JEL Classification: C32, E37, E47.
    Keywords: Forecasting, Inflation, Stochastic Volatility, Time Varying Vector Autoregression.
    Date: 2010–04
  3. By: Peter Tillmann (Justus Liebig University Gießen)
    Abstract: The Federal Open Market Committee (FOMC) of the Federal Reserve consists of voting- and non-voting members. Apart from deciding about interest rate policy, members individually formulate regular inflation forecasts. This paper uncovers systematic differences in individual inflation forecasts submitted by voting and non-voting members. Based on a data set with individual forecasts recently made available it is shown that non-voters systematically overpredict inflation relative to the consensus forecast if they favor tighter policy and underpredict inflation if the favor looser policy. These findings are consistent with non-voting member following strategic motives in forecasting, i.e. non-voting members use their forecast to influence policy deliberation.
    Keywords: inflation forecast, forecast errors, monetary policy, monetary committee, Federal Reserve
    JEL: E43 E52
    Date: 2010
  4. By: Stephen Hall; Kavita Sirichand
    Abstract: This paper illustrates the importance of density forecasting in portfolio decision making involving bonds of different maturities. The forecast performance of an atheoretic and a theory informed model of bond returns is evaluated. The decision making environment is fully described for an investor seeking to optimally allocate his portfolio between long and short Treasury Bills, over investment horizons of up to two years. Using weekly data over 1997 to 2007 we examine the impact of parameter uncertainty and predictability in returns on the investor's allocation. We describe how the forecasts are computed and used in this context. Both statistical and decision-based criteria are used to assess the out-of-sample forecasting performance of the models. Our results show sensitivity to the evaluation criterion used. In the context of investment decision making under an economic value criterion, we find some potential gain for the investor from assuming predictability.
    Keywords: Density Forecasting; Interest rate Predictability; Parameter Uncertainty and Decision-Based Forecast Evaluation
    JEL: C32 C53 E43 E47 G11
    Date: 2010–03
  5. By: Francesco Ravazzolo; Shaun P. Vahey
    Abstract: We propose a methodology for producing forecast densities for economic aggregates based on disaggregate evidence. Our ensemble predictive methodology utilizes a linear mixture of experts framework to combine the forecast densities from potentially many component models. Each component represents the univariate dynamic process followed by a single disaggregate variable. The ensemble produced from these components approximates the many unknown relationships between the disaggregates and the aggregate by using time-varying weights on the component forecast densities. In our application, we use the disaggregate ensemble approach to forecast US Personal Consumption Expenditure inflation from 1997Q2 to 2008Q1. Our ensemble combining the evidence from 11 disaggregate series outperforms an aggregate autoregressive benchmark, and an aggregate time-varying parameter specification in density forecasting.
    JEL: C11 C32 C53 E37 E52
    Date: 2010–04
  6. By: Michiel De Pooter; Francesco Ravazzolo; Dick van Dijk
    Abstract: We examine the importance of incorporating macroeconomic information and, in particular, accounting for model uncertainty when forecasting the term structure of U.S. interest rates. We start off by analyzing and comparing the forecast performance of several individual term structure models. Our results confirm and extend results found in previous literature that adding macroeconomic information, through factors extracted from a large number of individual series, tends to improve interest rate forecasts. We then show, however, that the predictive power of individual models varies over time significantly. Models with macro factors are the more accurate in and around recession periods. Models without macro factors do particularly well in low-volatility subperiods such as the late 1990s. We demonstrate that this problem of model uncertainty can be mitigated by combining individual model forecasts. Combining forecasts leads to encouraging gains in predictability, especially for longer-dated maturities, and importantly, these gains are consistent over time.
    Date: 2010
  7. By: Chia-Lin Chang; Philip Hans Franses; Michael McAleer (University of Canterbury)
    Abstract: Macro-economic forecasts typically involve both a model component, which is replicable, as well as intuition, which is non-replicable. Intuition is expert knowledge possessed by a forecaster. If forecast updates are progressive, forecast updates should become more accurate, on average, as the actual value is approached. Otherwise, forecast updates would be neutral. The paper proposes a methodology to test whether forecast updates are progressive and whether econometric models are useful in updating forecasts. The data set for the empirical analysis are for Taiwan, where we have three decades of quarterly data available of forecasts and updates of the inflation rate and real GDP growth rate. The actual series for both the inflation rate and the real GDP growth rate are always released by the government one quarter after the release of the revised forecast, and the actual values are not revised after they have been released. Our empirical results suggest that the forecast updates for Taiwan are progressive, and can be explained predominantly by intuition. Additionally, the one-, two- and three-quarter forecast errors are predictable using publicly available information for both the inflation rate and real GDP growth rate, which suggests that the forecasts can be improved.
    Keywords: Macro-economic forecasts; econometric models; intuition; initial forecast; primary forecast; revised forecast; actual value; progressive forecast updates; forecast errors
    JEL: C53 C22 E27 E37
    Date: 2010–04–01
  8. By: Philip Hans Franses; Michael McAleer (University of Canterbury); Rianne Legerstee
    Abstract: Macroeconomic forecasts are frequently produced, published, discussed and used. The formal evaluation of such forecasts has a long research history. Recently, a new angle to the evaluation of forecasts has been addressed, and in this review we analyse some recent developments from that perspective. The literature on forecast evaluation predominantly assumes that macroeconomic forecasts are generated from econometric models. In practice, however, most macroeconomic forecasts, such as those from the IMF, World Bank, OECD, Federal Reserve Board, Federal Open Market Committee (FOMC) and the ECB, are based on econometric model forecasts as well as on human intuition. This seemingly inevitable combination renders most of these forecasts biased and, as such, their evaluation becomes non-standard. In this review, we consider the evaluation of two forecasts in which: (i) the two forecasts are generated from two distinct econometric models; (ii) one forecast is generated from an econometric model and the other is obtained as a combination of a model, the other forecast, and intuition; and (iii) the two forecasts are generated from two distinct combinations of different models and intuition. It is shown that alternative tools are needed to compare and evaluate the forecasts in each of these three situations. These alternative techniques are illustrated by comparing the forecasts from the Federal Reserve Board and the FOMC on inflation, unemployment and real GDP growth.
    Keywords: Macroeconomic forecasts; econometric models; human intuition; biased forecasts; forecast performance; forecast evaluation; forecast comparison
    JEL: C22 C51 C52 C53 E27 E37
    Date: 2010–03–01
  9. By: Chia-Lin Chang; Philip Hans Franses; Michael McAleer (University of Canterbury)
    Abstract: A government’s ability to forecast key economic fundamentals accurately can affect business confidence, consumer sentiment, and foreign direct investment, among others. A government forecast based on an econometric model is replicable, whereas one that is not fully based on an econometric model is non-replicable. Governments typically provide non-replicable forecasts (or, expert forecasts) of economic fundamentals, such as the inflation rate and real GDP growth rate. In this paper, we develop a methodology to evaluate non-replicable forecasts. We argue that in order to do so, one needs to retrieve from the non-replicable forecast its replicable component, and that it is the difference in accuracy between these two that matters. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the proposed methodological approach. Our main finding is that it is the undocumented knowledge of the Taiwanese government that reduces forecast errors substantially.
    Keywords: Government forecasts; generated regressors; replicable government forecasts; non- replicable government forecasts; initial forecasts; revised forecasts
    JEL: C53 C22 E27 E37
    Date: 2010–04–01
  10. By: Katja Drechsel; Rolf Scheufele
    Abstract: The paper analyzes leading indicators for GDP and industrial production in Germany. We focus on the performance of single and pooled leading indicators during the pre-crisis and crisis period using various weighting schemes. Pairwise and joint significant tests are used to evaluate single indicator as well as forecast combination methods. In addition, we use an end-of-sample instability test to investigate the stability of forecasting models during the recent financial crisis. We find in general that only a small number of single indicator models were performing well before the crisis. Pooling can substantially increase the reliability of leading indicator forecasts. During the crisis the relative performance of many leading indicator models increased. At short horizons, survey indicators perform best, while at longer horizons financial indicators, such as term spreads and risk spreads, improve relative to the benchmark.
    Keywords: Leading Indicators, Forecast Evaluation, Forecast Pooling, Structural Breaks
    JEL: E37 C22 C53
    Date: 2010–04
  11. By: George Athanasopoulos; Osmani Teixeira de Carvalho Guillén; João Victor Issler; Farshid Vahid
    Abstract: We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian inflation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in different measures of forecasting accuracy are substantial, especially for short horizons.
    Date: 2010–04
  12. By: Ida Wolden Bache (Norges Bank (Central Bank of Norway)); Leif Brubakk (Norges Bank (Central Bank of Norway)); Junior Maih (Norges Bank (Central Bank of Norway))
    Abstract: We estimate a small open-economy DSGE model for Norway with two specifications of monetary policy: a simple instrument rule and optimal policy based on an intertemporal loss function. The empirical fit of the model with optimal policy is as good as the model with a simple rule. This result is robust to allowing for misspecification following the DSGE-VAR approach proposed by Del Negro and Schorfheide (2004). The interest rate forecasts from the DSGE-VARs are close to Norges Bank's official forecasts since 2005. One interpretation is that the DSGE-VAR approximates the judgment imposed by the policymakers in the forecasting process.
    Keywords: DSGE models, forecasting, optimal monetary policy
    JEL: C53 E52
    Date: 2010–04–07
  13. By: Nektarios Aslanidis (Department of Economics, FCEE, University Rovira Virgili); Charlotte Christiansen (School of Economics and Management, Aarhus University and CREATES)
    Abstract: This paper re-examines the joint distribution of equity and bond returns using high frequency data. In particular, we analyze the weekly realized stock bond correlation calculated from 5-minute returns of the futures prices of the S&P 500 and the 10-year Treasury Note. A potentially gradual transition in the realized correlation is accommodated by regime switching smooth transition regressions. The regimes are defined by the VIX/VXO volatility index and the model includes additional economic and financial explanatory variables. The empirical results show that the smooth transition model has a better fit than a linear model at forecasting in sample, whereas the linear model is more accurate for out-of-sample forecasting. It is also shown that it is important to account for differences between positive and negative realized stock bond correlations.
    Keywords: realized correlation, smooth transition regressions, stock bond correlation, VIX index
    JEL: C22 G11
    Date: 2010–04–26
  14. By: Michael McAleer (University of Canterbury); Chatayan Wiphatthanananthakul
    Abstract: In 2003, the Chicago Board Options Exchange (CBOE) made two key enhancements to the volatility index (VIX) methodology based on S&P options. The new VIX methodology seems to be based on a complicated formula to calculate expected volatility. In this paper, with the use of Thailand’s SET50 Index Options data, we modify the VIX formula to a very simple relationship, which has a higher negative correlation between the VIX for Thailand (TVIX) and SET50 Index Options. We show that TVIX provides more accurate forecasts of option prices than the simple expected volatility (SEV) index, but the SEV index outperforms TVIX in forecasting expected volatility. Therefore, the SEV index would seem to be a superior tool as a hedging diversification tool because of the high negative correlation with the volatility index.
    Keywords: Financial markets; model selection; new products; price forecasting; time series; volatility forecasting
    Date: 2010–03–01
  15. By: David de Antonio Liedo (Banco de España)
    Abstract: This paper proposes the use of dynamic factor models as an alternative to the VAR-based tools for the empirical validation of dynamic stochastic general equilibrium (DSGE) theories. Along the lines of Giannone et al. (2006), we use the state-space parameterisation of the factor models proposed by Forni et al. (2007) as a competitive benchmark that is able to capture weak statistical restrictions that DSGE models impose on the data. Beyond the weak restrictions, which are given by the number of shocks and the number of state variables, the behavioural restrictions embedded in the utility and production functions of the model economy contribute to achieve further parsimony. Such parsimony reduces the number of parameters to be estimated, potentially helping the general equilibrium environment improve forecast accuracy. In turn, the DSGE model is considered to be misspecified when it is outperformed by the state-space representation that only incorporates the weak restrictions.
    Keywords: dynamic and static rank, factor models, DSGE models, forecasting
    JEL: E32 E37 C52
    Date: 2010–04

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