This study introduces a monthly coincident indicator for consumption in Germany based on Google Trends data on web search activity. In real-time nowcasting experiments the indicator outperforms common survey-based indicators in predicting consumption. Unlike those indicators, it provides...

This study introduces a monthly coincident indicator for consumption in Germany based on Google Trends data on web search activity. In real-time nowcasting experiments the indicator outperforms common survey-based indicators in predicting consumption. Unlike those indicators, it provides...

Factor models can cope with many variables without running into scarce degrees of freedom problems often faced in a regression-based analysis. In this article we review recent work on dynamic factor models that have become popular in macroeconomic policy analysis and forecasting. By means of an...

This paper discusses a factor model for estimating monthly GDP using a large number of monthly and quarterly time series in real-time. To take into account the different periodicities of the data and missing observations at the end of the sample, the factors are estimated by applying an EM...

This paper indicates special aspects of using vector auto-regression models to forecast rates of basic macroeconomic indicators in short term. In particular, traditional vector auto-regression model, Bayesian vector auto-regression model and factor augmented vector auto-regression model are...

The purpose of this paper is to show that an affine model which incorporates the condition of no arbitrage enables improvements in forecasting the term structure of interest rates in Mexico. The three factors of the yield curve (level, slope and curvature) used in the model are estimated by the...

We study the dynamics of the term-structure of interest rates in Mexico. Specifically, we investigate time variation in bond risk premia and the common factors that have influenced the behavior of the yield curve. We find that term-premia in government bonds appear to be time-varying. We then...