Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract It is widely perceived that the supply of mortgages, especially since the extensive liberalization of the mortgage market since the 1980s, has had implications for the housing market in the Netherlands. In this paper we introduce a new method to estimate a credit condition index (CCI). The CCI represents changes in the supply of credit over time, apart from changes in interest rates and income. It has been estimated by an unobserved component in an error-correction model in which the average amount of new provided mortgages per period is explained by the borrowing capacity and additional control variables. The model has been estimated on data representing first time buyers (FTB). For Terms of use: Documents in EconStor may

What is now known in English as the Netherlands Bureau for Economic Policy Analysis (CPB) has been involved in econometric model building since its foundation in 1945. During the 60 years of developing and using the models reviewed in this article, CPB's model building has evolved significantly. Over this period, a shift of emphasis can be observed from econometrics and empiricism to economic theory. New questions from policymakers and new features in the national economy have guided research, while new developments in econometrics and economic theory were taken on board wherever they helped to improve the quality and scope of the analysis. Although considerable progress has been achieved in several spheres, even the most sophisticated and up-to-date models continue to be riddled with some longstanding limitations and weaknesses.

contribution demand categories, GDP growth, imports, C67, O40,

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In the period 1990-2005 Statistics Netherlands adjusted quarterly Dutch GDP growth (q on q −4 ) by 0.55% point on average. Although in most countries upward adjustments of GDP growth rates are more common than downward adjustments, this is not always the case. For instance, in Germany and Canada the mean adjustment is around zero (Öller and Hansson, 2002). For the Netherlands, the upward adjustment on average was first noticed by CPB Netherlands Bureau for Policy Analysis, 17 years ago (Van Vlimmeren, Don and Okker, 1991). From international perspective, the revisions of Dutch GDP growth rates seem to be rather large. Realized GDP growth rates in recent quarters provide useful information in making forecasts for economic growth in the current and following year. If, afterwards, these preliminary GDP growth figures need to be adjusted, the forecasters used wrong information, which unfavourably influences their forecast accuracy. Using a Monte Carlo simulation technique, Lanser and Kranendonk (2008) show that 15% (11%) of the forecasting error of the Dutch official forecasts for GDP growth in the current (following) year, made by CPB, can be attributed to revisions of preliminary data published by Statistics Netherlands (see also Kranendonk and Verbruggen, 2005, p. 20). Therefore, being forecasters ourselves, we are very much interested in knowledge that could help to diminish these adjustments.In his challenging note, Philip Hans Franses (PHF) proposes a simple reason why GDP is usually adjusted upwards (Franses, 2008). His hypothesis, which unfortunately cannot be tested, is based on the process of collecting data and the behaviour of the statisticians who have to fill in the missing data. We are not convinced that PHF's note sheds some light on this topic. In this short reaction, we will make some qualifications. First, we like to notice that PHF seems to mix up the concepts 'forecasting' and 'nowcasting'. When a statistician has to provide values of yet to collect data about a recent quarter, this is usually called nowcasting, where PHF uses the concept of forecasting. His assumption of an asymmetric loss or utility function is regularly mentioned to be one of the reasons for overestimating GDP growth forecasts in countries where the official forecasts are made by a 'non-independent' ministry. See, for instance, Jonung and Larch (2004), who suggest that to remedy the optimism bias in official forecasts, forecasting should be assigned to an