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on Forecasting |
| By: | Rangan Gupta (Department of Economics, University of Pretoria); Alain Kabundi (Department of Economics and Econometrics, University of Johannesburg) |
| Abstract: | This paper uses the Dynamic Factor Model (DFM) framework, which accommodates a large cross-section of macroeconomic time series for forecasting the per capita growth rate, inflation, and the nominal shortterm interest rate for the South African economy. The DFM used in this study contains 267 quarterly series observed over the period 1980Q1-2006Q4. The results, based on the RMSEs of one- to four-quartersahead out of sample forecasts over 2001Q1 to 2006Q4, indicate the DFM outperforms the NKDSGE in forecasting per capita growth, inflation and the nominal short-term interest rate. Moreover, the DFM performs no worse compared to the VARs, both classical and Bayesian, indicating the blessing of dimensionality. |
| Keywords: | Dynamic Factor Model, VAR, BVAR, NKDSGE Model, Forecast Accuracy |
| JEL: | C11 C13 C33 C53 |
| Date: | 2008–06 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:200815&r=for |
| By: | Rangan Gupta (Department of Economics, University of Pretoria); Alain Kabundi (Department of Economics and Econometrics, University of Johannesburg) |
| Abstract: | This paper uses two-types of large-scale models, namely the Dynamic Factor Model (DFM) and Bayesian Vector Autoregressive (BVAR) Models based on alternative hyperparameters specifying the prior, which accommodates 267 macroeconomic time series, to forecast key macroeconomic variables of a small open economy. Using South Africa as a case study and per capita growth rate, inflation rate, and the short-term nominal interest rate as our variables of interest, we estimate the two-types of models over the period 1980Q1 to 2006Q4, and forecast one- to four-quarters-ahead over the 24-quarters out-of-sample horizon of 2001Q1 to 2006Q4. The forecast performances of the two large-scale models are compared with each other, and also with an unrestricted three-variable Vector Autoregressive (VAR) and BVAR models, with identical hyperparameter values as the large-scale BVARs. The results, based on the average Root Mean Squared Errors (RMSEs), indicate that the large-scale models are better-suited for forecasting the three macroeconomic variables of our choice, and amongst the two types of large-scale models, the DFM holds the edge. |
| Keywords: | Dynamic Factor Model, BVAR, Forecast Accuracy |
| JEL: | C11 C13 C33 C53 |
| Date: | 2008–06 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:200816&r=for |
| By: | Sonali Das (LQM, CSIR, Pretoria); Rangan Gupta (Department of Economics, University of Pretoria); Alain Kabundi (Department of Economics and Econometrics, University of Johannesburg) |
| Abstract: | This paper uses the Dynamic Factor Model (DFM) framework, which accommodates a large cross-section of macroeconomic time series for forecasting regional house price inflation. As a case study, we use data on house price inflation for five metropolitan areas of South Africa. The DFM used in this study contains 282 quarterly series observed over the period 1980Q1-2006Q4. The results, based on the Mean Absolute Errors of one- to four-quarters-ahead out of sample forecasts over the period of 2001Q1 to 2006Q4, indicate that, in majority of the cases, the DFM outperforms the VARs, both classical and Bayesian, with the latter incorporating both spatial and non-spatial models. Our results, thus, indicate the blessing of dimensionality. |
| Keywords: | Dynamic Factor Model, VAR, BVAR, Forecast Accuracy |
| JEL: | C11 C13 C33 C53 |
| Date: | 2008–06 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:200814&r=for |
| By: | K. Barhoumi; S. Benk; R. Cristadoro; A. Den Reijer; A. Jakaitiene; P. Jelonek; A. Rua; K. Ruth; C. Van Nieuwenhuyze; G. Rünstler (ECB, DG Research) |
| Abstract: | This paper evaluates different models for the short-term forecasting of real GDP growth in ten selected European countries and the euro area as a whole. Purely quarterly models are compared with models designed to exploit early releases of monthly indicators for the nowcast and forecast of quarterly GDP growth. Amongst the latter, we consider small bridge equations and forecast equations in which the bridging between monthly and quarterly data is achieved through a regression on factors extracted from large monthly datasets. The forecasting exercise is performed in a simulated real-time context, which takes account of publication lags in the individual series. In general, we find that models that exploit monthly information outperform models that use purely quarterly data and, amongst the former, factor models perform best. |
| Keywords: | Bridge models, Dynamic factor models, real-time data flow |
| JEL: | E37 C53 |
| Date: | 2008–06 |
| URL: | http://d.repec.org/n?u=RePEc:nbb:reswpp:200806-17&r=for |
| By: | Kenneth S. Rogoff; Vania Stavrakeva |
| Abstract: | Are structural models getting closer to being able to forecast exchange rates at short horizons? Here we argue that over-reliance on asymptotic test statistics in out-of-sample comparisons, misinterpretation of some tests, and failure to sufficiently check robustness to alternative time windows has led many studies to overstate even the relatively thin positive results that have been found. We find that by allowing for common cross-country shocks in our panel forecasting specification, we are able to generate some improvement, but even that improvement is not entirely robust to the forecast window, and much of the gain appears to come from non-structural rather than structural factors. |
| JEL: | C52 C53 F31 F47 |
| Date: | 2008–06 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:14071&r=for |
| By: | Rangan Gupta (Department of Economics, University of Pretoria); Sonali Das (LQM, CSIR, Pretoria) |
| Abstract: | This paper estimates Bayesian Vector Autoregressive (BVAR) models, both spatial and non-spatial (univariate and multivariate), for the twenty largest states of the US economy, using quarterly data over the period 1976:Q1 to 1994:Q4; and then forecasts one-to-four quarters ahead real house price growth over the out-of-sample horizon of 1995:Q1 to 2006:Q4. The forecasts are then evaluated by comparing them with the ones generated from an unrestricted classical Vector Autoregressive (VAR) model and the corresponding univariate variant the same. Finally, the models that produce the minimum average Root Mean Square Errors (RMSEs), are used to predict the downturns in the real house price growth over the recent period of 2007:Q1 to 2008:Q1. The results show that the BVARs, in whatever form they might be, are the best performing models in 19 of the 20 states. Moreover, these models do a fair job in predicting the downturn in 18 of the 19 states, however, they always under-predict the size of the decline in the real house price growth rate – an indication of the need to incorporate the role of fundamentals in the models. |
| Keywords: | BVAR Model; BVAR Forecasts; Forecast Accuracy; SBVAR Model; SBVAR Forecasts; VAR Model; VAR Forecasts |
| JEL: | E17 E27 E37 E47 |
| Date: | 2008–06 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:200821&r=for |
| By: | Ard den Reijer |
| Abstract: | This study analyses the dynamic characteristics of staffing employment across di¤erent business sectors and across different geographical regions in the Netherlands. We analyse a micro data set of the market leader of the Dutch staffing employment market, Randstad. We apply the dynamic factor model to extract common information out of a large data set and to isolate business cycle frequencies with the aim of forecasting economic activity. We identify regions and sectors whose cyclical developments lead the staffing labour cycle at the country level. The second question is then which model specification can best exploit the identified leading indicators at the disaggregate level to forecast the country aggregate? The dynamic factor model turns out to outperform univariate benchmark forecasting models by exploiting the substantial temporal variation of the staffing labour market at the disaggregate level. |
| Keywords: | staffing labour; dynamic factor model; disaggregate forecasting |
| JEL: | C31 C53 J44 J63 |
| Date: | 2007–11 |
| URL: | http://d.repec.org/n?u=RePEc:dnb:dnbwpp:153&r=for |