While the usefulness of factor models has been acknowledged over recent years, little attention has been devoted to the forecasting power of these models for the Japanese economy. In this paper, we aim at assessing the relative performance of factor models over different samples, including the recent financial crisis. To do so, we construct factor models to forecast Japanese GDP and its subcomponents, using 38 data series (including daily, monthly and quarterly variables) over the period 1991 to 2010. Overall, we find that factor models perform well at tracking GDP movements and anticipating turning points. For most of the components, we report that factor models yield lower forecasting errors than a simple AR process or an indicator model based on Purchasing Managers' Indicators (PMIs). In line with previous studies, we conclude that the largest improvements in terms of forecasting accuracy are found for more volatile periods, such as the recent financial crisis. However, unlike previous studies, we do not find evident links between the volatility of the components and the relative advantage of using factor models. Finally, we show that adding the PMI index as an independent explanatory variable improves the forecasting properties of the factor models.Keywords: Japan; Forecasting; Nowcasting; Factor models; Mixed frequency Bank topics: Econometric and statistical methods; International topics JEL codes: C50, C53, E37, E47 1 Non-technical summaryOver recent years, factor models have proven useful forecasting tools for dealing with large datasets. Forecasting Japanese GDP and its components is challenging, as it also involves dealing with very volatile data. Resorting to large databases could in principle help to single out common patterns (the 'factors') from multiple data series. To this extent, it is surprising to notice that while the usefulness of factor models in obtaining short-term forecasts for many advanced and developing economies has been documented extensively, little attention has been devoted to the Japanese economy. To the best of our knowledge, no mixedfrequency factor model to forecast the Japanese GDP and its components has appeared in the literature. We see this as a shortcoming, given that policymakers have a clear interest in providing forecasts not only for monthly series (e.g. inflation or industrial production), but also for (quarterly) GDP and its subcomponents. Factor models provide a simple and convenient way to accomplish this task.In this paper we explore the usefulness of factor models for forecasting real activity in Japan by resorting to different specifications. More specifically, we construct forecasts of past-, current-and next-quarter GDP, as well as its subcomponents, by using information available on the first, the second and the third month of the quarter. We also assess the performance of factor models over a simple AR specification, as well as a tougher benchmark, an indicator model based on Purchasing Managers index (PMIs).Overall, we find that factor models perf...

No abstract

While the usefulness of factor models has been acknowledged over recent years, little attention has been devoted to the forecasting power of these models for the Japanese economy. In this paper, we aim at assessing the relative performance of factor models over different samples, including the recent financial crisis. To do so, we construct factor models to forecast Japanese GDP and its subcomponents, using 38 data series (including daily, monthly and quarterly variables) over the period 1991 to 2010. Overall, we find that factor models perform well at tracking GDP movements and anticipating turning points. For most of the components, we report that factor models yield lower forecasting errors than a simple AR process or an indicator model based on Purchasing Managers' Indicators (PMIs). In line with previous studies, we conclude that the largest improvements in terms of forecasting accuracy are found for more volatile periods, such as the recent financial crisis. However, unlike previous studies, we do not find evident links between the volatility of the components and the relative advantage of using factor models. Finally, we show that adding the PMI index as an independent explanatory variable improves the forecasting properties of the factor models.