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on Forecasting |
| By: | Hassani, Hossein |
| Abstract: | In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, the performance of the SSA technique has been considered by applying it to a well-known time series data set, namely, monthly accidental deaths in the USA. The results are compared with those obtained using Box-Jenkins SARIMA models, the ARAR algorithm and the Holt-Winter algorithm (as described in Brockwell and Davis (2002)). The results show that the SSA technique gives a much more accurate forecast than the other methods indicated above. |
| Keywords: | ARAR algorithm; Box-Jenkins SARIMA models; Holt-Winter algorithm; singular spectrum analysis (SSA); USA monthly accidental deaths series. |
| JEL: | C14 C61 C53 |
| Date: | 2007–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:4991&r=for |
| By: | Edda Claus; Iris Claus |
| Abstract: | This paper constructs six leading indexes of New Zealand employment and compares their short term forecasting performance. Forecasting New Zealand employment is particularly difficult owing to the volatility of the data and the short sample size of available time series. These restrictions make leading indexes especially appealing. The paper has two aims. The first is to construct an effective forecasting tool. The second is to evaluate leading indexes constructed using different methods available in the literature. The results show that an index constructed using the traditional NBER method dominates in terms of forecasting performance. The results also suggest that increasing the dataset does not strengthen the index and that exogenously determining the weights of component series can add to forecasting performance. |
| Date: | 2007–09 |
| URL: | http://d.repec.org/n?u=RePEc:acb:camaaa:2007-17&r=for |
| By: | Pesaran, M.H.; Assenmacher-Wesche, K. |
| Abstract: | We investigate the effect of forecast uncertainty in a cointegrating vector error correction model for Switzerland. Forecast uncertainty is evaluated in three different dimensions. First, we investigate the effect on forecasting performance of averaging over forecasts from different models. Second, we look at different estimation windows. We find that averaging over estimation windows is at least as e¤ective as averaging over different models and both complement each other. Third, we explore whether using weighting schemes from the machine learning literature improves the average forecast. Compared to equal weights the e¤ect of the weighting scheme on forecast accuracy is small in our application. |
| Keywords: | Bayesian model averaging, choice of observation window, longrun structural vector autoregression. |
| JEL: | C53 C32 |
| Date: | 2007–09 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:0746&r=for |
| By: | David, DE LA CROIX (UNIVERSITE CATHOLIQUE DE LOUVAIN, Department of Economics and CORE); FrŽdŽric DOCQUIER (FNRS and UNIVERSITE CATHOLIQUE DE LOUVAIN, Department of Economics); Philippe, LIEGEOIS (CEPS, INSTEAD, Luxemburg) |
| Abstract: | We forecast income growth over the periode 2000-2050 in the US, Canada, and France. To ground the forecasts on relationships that are as robust as possible t changes in the environment, we use a quantitative theoretical approach which consists in calibrating and simulating a general equilibrium model. Compared to existing studies to link taxes and public expenditures to demographic changes, and take into account the interaction between education and work experience. Forecasts show that growth will be weaker over the period 2010-2040. The gap between the US and the two other countries is increasing over time. France will catch-up and overtake Canada in 2020. Investigating alternative policy scenarios, we show that increasing the effective retirement age to 63 would be most profitable for France, reducing its gap with US by one third. A decrease in social security benefits would slightly stimulate growth but would have no real impact on the gap between the countries. |
| Keywords: | Aging, Forecast, Computable General Equilibrium, Education, Experience |
| JEL: | D58 E6 H55 J11 O40 |
| Date: | 2007–09–29 |
| URL: | http://d.repec.org/n?u=RePEc:ctl:louvec:2007029&r=for |
| By: | Anthony Garratt (School of Economics, Mathematics & Statistics, Birkbeck); Gary Koop; Emi Mise; Shaun P Vahey |
| Abstract: | A popular account for the demise of the UK monetary targeting regime in the 1980s blames the weak predictive relationships between broad money and inflation and real output. In this paper, we investigate these relationships using a variety of monetary aggregates which were used as intermediate UK policy targets. We use both real-time and final vintage data and consider a large set of recursively estimated Vector Autoregressive (VAR) and Vector Error Correction models (VECM). These models differ in terms of lag length and the number of cointegrating relationships. Faced with this model uncertainty, we utilize Bayesian model averaging (BMA) and contrast it with a strategy of selecting a single best model. Using the real-time data available to UK policymakers at the time, we demonstrate that the in-sample predictive content of broad money fluctuates throughout the 1980s for both strategies. However, the strategy of choosing a single best model amplifies these fluctuations. Out-of-sample predictive evaluations rarely suggest that money matters for either inflation or real output, regardless of whether we select a single model or do BMA. Overall, we conclude that the money was a weak (and unreliable) predictor for these key macroeconomic variables. But the view that the predictive content of UK broad money diminished during the 1980s receives little support using either the real-time or final vintage data. |
| Keywords: | Money, Vector Error Correction Models, Model Uncertainty, Bayesian Model Averaging, Real Time Data |
| JEL: | C11 C32 C53 E51 E52 |
| Date: | 2007–09 |
| URL: | http://d.repec.org/n?u=RePEc:bbk:bbkefp:0714&r=for |