Jörg Breitung

In this paper a nonparametric approach for estimating mixed‐frequency forecast equations is proposed. In contrast to the popular MIDAS approach that employs an (exponential) Almon or Beta lag distribution, we adopt a penalized least‐squares estimator that imposes some degree of smoothness to the lag distribution. This estimator is related to nonparametric estimation procedures based on cubic splines and resembles the popular Hodrick–Prescott filtering technique for estimating a smooth trend function. Monte Carlo experiments suggest that the nonparametric estimator may provide more reliable and flexible approximations to the actual lag distribution than the conventional parametric MIDAS approach based on exponential lag polynomials. Parametric and nonparametric methods are applied to assess the predictive power of various daily indicators for forecasting monthly inflation rates. It turns out that the commodity price index is a useful predictor for inflations rates 20–30 days ahead with a hump‐shaped lag distribution. Copyright © 2015 John Wiley & Sons, Ltd.

In this article, we propose a selection procedure that allows us to consistently estimate the number of dynamic factors in a dynamic factor model. The procedure is based on a canonical correlation analysis of the static factors which has the advantage of being invariant to a rescaling of the factors. Monte Carlo simulations suggest that the proposed selection rule outperforms existing ones, in particular, if the contribution of the common factors to the overall variance is moderate or low. The new selection procedure is applied to the US macroeconomic data panel used in Stock and Watson [NBER working paper 11467 (2005)].

Summary: Objective: To discuss generalized estimating equations as an extension of generalized linear models by commenting on the paper of Ziegler and Vens “Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matrix”. Methods: Inviting an international group of experts to comment on this paper. Results: Several perspectives have been taken by the discussants. Econometricians have established parallels to the generalized method of moments (GMM). Statisticians discussed model assumptions and the aspect of missing data. Applied statisticians commented on practical aspects in data analysis. Conclusions: In general, careful modeling correlation is encouraged when considering estimation efficiency and other implications, and a comparison of choosing instruments in GMM and generalized estimating equations (GEE) would be worthwhile. Some theoretical drawbacks of GEE need to be further addressed and require careful analysis of data. This particularly applies to the situation when data are missing at random.

Summary: This paper considers popular methods for the quantification of survey expectations. We investigate the asymptotic properties of two variants of the probability approach originally suggested by Carlson and Parkin (1975). It is argued that the traditional method can be interpreted as an instrumental variable estimator that suffers from a severe loss of efficiency if the mean of the target variable is close to zero. The regression approach advocated by Pesaran (1984) can be seen as a special case assuming that expectations of survey participants are uniformly distributed. Applying alternative variants of these approaches to the ZEW survey of expected changes in the inflation rate demonstrates that the regression variant of the Carlson-Parkin methodology yields the best fit to the realized changes of the inflation rate. It turns out, however, that even the best performing quantification method fails to fulfill the requirement of rational expectations, as the variance of the expectation error is significantly reduced by including additional variables such as the lagged inflation rate.

This paper considers various tests of the unit root hypothesis in panels where the cross-section dependence is due to common dynamic factors. Three situations are studied. First, the common factors and idiosyncratic components may both be nonstationary. In this case test statistics based on generalized least squares (GLS) possess a standard normal limiting distribution, whereas test statistics based on ordinary least squares (OLS) are invalid. Second, if the common component is I(1) and the idiosyncratic component is stationary (the case of cross-unit cointegration), then both the OLS and the GLS statistics fail. Finally, if the idiosyncratic components are I(1) but the common factors are stationary, then the OLS-based test statistics are severely biased, whereas the GLS-based test statistics are asymptotically valid in this situation. A Monte Carlo study is conducted to verify the asymptotic results.The research for this paper was carried out within research project “Unit roots and cointegration in panel data” financed by the German Research Association (DFG). We thank Paulo Rodrigues and two anonymous referees for helpful comments and suggestions.

In this paper alternative approaches for testing the unit root hypothesis in panel data are considered. First, a robust version of the Dickey‐Fuller t‐statistic under contemporaneous correlated errors is suggested. Second, the GLS t‐statistic is considered, which is based on the t‐statistic of the transformed model. The asymptotic power of both tests is compared against a sequence of local alternatives. To adjust for short‐run serial correlation of the errors, we propose a pre‐whitening procedure that yields a test statistic with a standard normal limiting distribution as N and T tends to infinity. The test procedure is further generalized to accommodate individual specific intercepts or linear time trends. From our Monte Carlo simulations it turns out that the robust OLS t‐statistic performs well with respect to size and power, whereas the GLS t‐statistic may suffer from severe size distortions in small and moderate sample sizes. The tests are applied to test for a unit root in real exchange rates.