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on Forecasting |
| By: | Carlos Capistrán; Christian Constandse; Manuel Ramos Francia |
| Abstract: | Since the adoption of inflation targeting, the seasonal appears to be the component that explains the major part of inflation's total variation in Mexico. In this context, we study the performance of seasonal time series models to forecast short-run inflation. Using multi-horizon evaluation techniques, we examine the real-time forecasting performance of four well-known seasonal models using data on 16 indices of the Mexican Consumer Price Index (CPI), including headline and core inflation. These models consider both, deterministic and stochastic seasonality. After selecting the best forecasting model for each index, we apply and compare two methods that aggregate hierarchical time series, the bottom-up method and an optimal combination approach. The best forecasts are able to compete with those taken from surveys of experts. |
| Keywords: | Aggregated forecasts, bottom-up forecasting, forecast combination, hierarchical time series, inflation targeting, multi-horizon evaluation, seasonal unit roots. |
| JEL: | C22 C52 C53 E37 |
| Date: | 2009–07 |
| URL: | http://d.repec.org/n?u=RePEc:bdm:wpaper:2009-05&r=for |
| By: | Barhoumi, K.; Darné, O.; Ferrara, L. |
| Abstract: | This paper compares the GDP forecasting performance of alternative factor models based on monthly time series for the French economy. These models are based on static and dynamic principal components. The dynamic principal components are obtained using time and frequency domain methods. The forecasting accuracy is evaluated in two ways for GDP growth. First, we question whether it is more appropriate to use aggregate or disaggregate data (with three disaggregating levels) to extract the factors. Second, we focus on the determination of the number of factors obtained either from various criteria or from a fixed choice. |
| Keywords: | GDP forecasting ; Factor models ; Data aggregation. |
| JEL: | C13 C52 C53 F47 |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:232&r=for |
| By: | Anindya Banerjee; Massimiliano Marcellino; Igor Masten |
| Abstract: | As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor- augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over standard ECM and FAVAR models. In particular, it uses a larger dataset compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter's specification in differences. In this paper we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simula- tions and several empirical applications. We show that relative to the FAVAR, FECM generally offers a higher forecasting precision and in general marks a very useful step forward for forecasting with large datasets. |
| Keywords: | Forecasting, Dynamic Factor Models, Error Correction Models, Cointegration, Factor-augmented Error Correction Models, FAVAR |
| JEL: | C32 E17 |
| Date: | 2009–06 |
| URL: | http://d.repec.org/n?u=RePEc:bir:birmec:09-06&r=for |
| By: | Jane M. Binner; Peter Tino; Jonathan Tepper; Richard G. Anderson; Barry Jones; Graham Kendall |
| Abstract: | This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. |
| Keywords: | Forecasting ; Inflation (Finance) ; Monetary theory |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlwp:2009-30&r=for |
| By: | Llano, Carlos (Departamento de Análisis Económico, Facultad de Ciencias Económicas y Empresariales, Universidad Autónoma de Madrid); Polasek, Wolfgang (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria); Sellner, Richard (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria) |
| Abstract: | Completing data sets that are collected in heterogeneous units is a quite frequent problem. Chow and Lin (1971) were the first to develop a united framework for the three problems (interpolation, extrapolation and distribution) of predicting times series by related series (the 'indicators'). This paper develops a spatial Chow-Lin procedure for cross-sectional and panel data and compares the classical and Bayesian estimation methods. We outline the error covariance structure in a spatial context and derive the BLUE for the ML and Bayesian MCMC estimation. Finally, we apply the procedure to Spanish regional GDP data between 2000-2004. We assume that only NUTS-2 GDP is known and predict GDPat NUTS-3 level by using socio-economic and spatial information available at NUTS-3. The spatial neighborhood is defined by either km distance, travel-time, contiguity and trade relationships. After running some sensitivity analysis, we present the forecast accuracy criteria comparing the predicted with the observed values. |
| Keywords: | Interpolation, Spatial panel econometrics, MCMC, Spatial Chow-Lin, Missing regional data, Spanish provinces, 'Polycentric-periphery' relationship |
| JEL: | C11 C15 C52 E17 R12 |
| Date: | 2009–07 |
| URL: | http://d.repec.org/n?u=RePEc:ihs:ihsesp:241&r=for |