Purpose With a view to the unconventional monetary policy measures implemented in the euro area in recent years, this study aims to investigate whether the recent house price increases in Germany are signals of an incipient overheating of the German housing market. Design/methodology/approach This paper presents a valuation measure for residential property based on a large and exhaustive regional panel data set for Germany. The fitted house prices from a panel regression at the district level, taking into account spatial spillovers, are taken as a measure of the fundamental equilibrium house prices, which can be aggregated for various regional subsets. Findings The estimation results suggest that apartment prices over the past years substantially exceeded the fundamental price suggested by the model, in particular in the big cities. Single-family houses appear to be markedly overvalued mainly in the cities. The low level of interest rates in recent years appears to have contributed to the emergence of misalignments. Originality/value Exploiting the variation across local housing markets, the estimation approach provides value-add for the estimation of house price valuation results in various regional subsets, as conventional time-series approaches to valuing property are subject to severe data limitations in the case of Germany.

The paper develops an oil price forecasting technique which is based on the present value model of rational commodity pricing. The approach suggests shifting the forecasting problem to the marginal convenience yield which can be derived from the cost-of-carry relationship. In a recursive out-of-sample analysis, forecast accuracy at horizons within one year is checked by the root mean squared error as well as the mean error and the frequency of a correct direction-of-change prediction. For all criteria employed, the proposed forecasting tool outperforms the approach of using futures prices as direct predictors of future spot prices. Vis-à-vis the random-walk model, it does not significantly improve forecast accuracy but provides valuable statements on the direction of change.Keywords: oil price forecasts, rational commodity pricing, convenience yield, singleequation models.JEL classification: E37; G12, G13, Q40; C22. Non-Technical SummaryThe paper develops an oil price forecasting technique on the basis of the present value model of rational commodity pricing. The central equation of the theoretical model describes the current spot price of crude oil as the sum of all discounted expected future payoffs received by the owner of one unit of this commodity ("convenience yields"). The discount factor is the sum of the risk-free interest rate and the oil-specific risk premium. The latter compensates for the holder's nondiversifiable risk. Convenience yields are defined as differences between the cost of carry and the futures prices of the commodity. The forecasting problem is moved to the marginal convenience yield because this entity is clearly more predictable than, say, the oil price percentage change directly. The indirect method, however, requires the oil-specific risk premium to be estimated. This is done by a cointegration approach. Market expectations of the marginal convenience yield can be derived from the term structure of the oil market. Alternatively, the marginal convenience yield can be forecast on the basis of autoregressive (AR) models. Combinations between the two approaches are possible, too. Multi-step AR forecasts can be performed by either the plug-in technique or the direction estimation method. Moreover, several information criteria can be applied for model selection issues.The forecast accuracy of the proposed technique is evaluated by out-of-sample projection exercises at horizons up to eleven months. The random-walk model and the approach of using futures prices as direct predictors of future spot prices serve as benchmarks. The sample of Brent oil prices used (i.e. starting in April 1991) is split into an estimation and an evaluation period. The latter comprises the post-January 1997 data in the first and the post-July 2000 data in the second experiment. The root mean squared error is the central evaluation criterion. The mean error and the relative frequency of a correct direction-of-change prediction are also considered. Moreover, statistical hypothesis testsà la Diebold and Maria...

There has been increased interest in the use of "big data" when it comes to forecasting macroeconomic time series such as private consumption or unemployment. However, applications on forecasting GDP are rather rare. In this paper we incorporate Google search data into a Bridge Equation Model, a version of which usually belongs to the suite of forecasting models at central banks. We show how to integrate these big data information, emphasizing the appeal of the underlying model in this respect. As the choice of which Google search terms to add to which equation is crucial-for the forecasting performance itself as well as for the economic consistency of the implied relationships-we compare different (ad-hoc, factor and shrinkage) approaches in terms of their pseudo-real time out-of-sample forecast performance for GDP, various GDP components and monthly activity indicators. We find that there are indeed sizeable gains possible from using Google search data, whereby partial least squares and LASSO appear most promising. Also, the forecast potential of Google search terms vis-à-vis survey indicators seems to have increased in recent years, suggesting that their scope in this field of application could increase in the future.

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. Inventory fluctuations are an important phenomenon in business cycles. However, the preliminary data on inventory investment as published in the German national accounts are tremendously prone to revision and therefore ill-equipped to diagnose the current stance of the inventory cycle. The Ifo business survey contains information on the assessments of inventory stocks in manufacturing as well as in retail and wholesale trade. Static factor analysis and a method building on canonical correlations are applied to construct a composite index of inventory fluctuations. Based on recursive estimates, the different variants are assessed as regards the stability of the weighting schemes and the ability to forecast the "true" inventory fluctuations better than the preliminary official releases. Terms of use: Documents inJEL classification: E22, C32, C52.

Research questionConsumer price indices usually measure the change in prices for a representative basket of goods and services over time (inflation). Measurement errors can occur in a variety of ways. If the expenditure weights of individual basket items change, for example as a result of changes in consumption patterns, these adjustments are usually incorporated into the consumer price index only after a certain time lag. As a consequence, price changes are no longer depicted in a representative manner. This paper examines two sources of mismeasurement linked to the use of expenditure weights when compiling the Harmonised Index of Consumer Prices (HICP), which serves as a key metric for gauging price stability, and thus for deciding the monetary policy stance adopted in the euro area. ContributionMeasurement bias and uncertainty are quantified for the German HICP between 1997 and 2019 on the basis of publicly available price indices and consumer expenditure weights. A superlative price index capturing substitution effects more accurately than the HICP is used as a benchmark for "true" inflation. In addition to this, when computing the expenditure weights of the benchmark index, all the relevant data available at the end of the period concerned are utilised. By contrast, when calculating the HICP, it is necessary over time to rely on whatever data has most recently become available, meaning that subsequent adjustments to these often provisional data are not taken into account. Any differences between the HICP and the benchmark index are attributed to measurement errors which can be divided up into a substitution component and a data vintage component. The substitution-related divergences for the HICP of the euro area are also analysed. ResultsThe substitution component and the data vintage component generate on average an upward bias in the German HICP inflation rate of about one-ninth of a percentage point, with around 80% of all deviations falling within a range of 0 to 0.25 percentage points. The extent of mismeasurement engendered by each of these two components is broadly the same. Since 2012, when a methodological change was made to the way in which the HICP is calculated, the level of substitution-induced bias decreased slightly. However, this has been accompanied by a similarly moderate increase in data vintage-induced bias. The decline in substitution-related bias witnessed since 2012 is also evidenced by the HICP recorded for the euro area. No findings were made with respect to the impact of data vintage due to a lack of data on the euro area HICP.