This paper studies the information content of some Ifo indicators. In particular, we investigate whether two Ifo indicators, one on the current business situation, the other on current production development, provide information on revisions of German industrial production. A new feature of our analysis is the construction and use of a real-time dataset. We conclude that the Ifo indicators play...

We present a novel approach to assessing the attentiveness of professional forecasters to news about the macroeconomy. We …nd evidence that professional forecasters, taken as a group, do not always update their estimates of the current state of the economy to re‡ect the latest releases of revised estimates of key data. Key words: Professional forecasters, data revisions, inattention. JEL classi...

In this paper we propose a new set of multivariate stochastic models that capture time varying seasonality within the vector innovations structural time series (VISTS) framework. These models encapsulate exponential smoothing methods in a multivariate setting. The models considered are the local level, local trend and damped trend VISTS models with an additive multivariate seasonal component. W...

Our proposed local vector autoregressive (LVAR) model has timevarying parameters that allow it to be safely used in both stationary and non-stationary situations. The estimation is conducted over an interval of local homogeneity where the parameters are approximately constant. The local interval is identified in a sequential testing procedure. Numerical analysis and real data application are co...

Analyzing International Monetary Fund (IMF) data, we find that overly optimistic growth expectations for a country induce economic contractions few years later. To isolate the causal effect, take an instrumental variable approach—exploiting randomness in allocation of IMF mission chiefs. We first document chiefs differ their individual degrees forecast optimism, yielding quasi-experimental vari...

this article is a comparative study of estimation power of artificial neural networks and autoregressive time series models in inflation forecasting. using 37 years iran’s inflation data, neural networks performs better on average for short horizons than autoregressive models. this study shows usefulness of early stopping technique in learning stage of neural networks for estimating time series...

This paper derives analytical results for determination of the window size that explores the trade-off between bias and forecast error variance to minimize the mean squared forecast error in the presence of breaks. We show analytically how to determine the estimation window optimally for the case with strictly exogenous regressors. Through Monte Carlo simulations the paper compares the performa...

We expand Nakamura’s (2005) neural network based inflation forecasting experiment to an alternative non-linear model; a Markov switching autoregressive (MS-AR) model. The two non-linear models perform approximately on par and outperform the linear autoregressive model on short forecast horizons of one and two quarters. Furthermore, the MS-AR model is the best performer on longer horizons of thr...