This paper investigates the Euro-area business cycle using a multivariate autoregressive time series model with cointegration. The cointegration restrictions help to identify permanent and transitory shocks which form the stochastic part of trend and cyclical GDP, respectively. The...

Mixed-data sampling (MIDAS) regressions allow to estimate dynamic equations that explain a low-frequency variable by high-frequency variables and their lags. To account for temporal instabilities in this relationship, this paper discusses an extension to MIDAS with time-varying parameters, which...

This paper compares two single-equation approaches from the recent nowcast literature: Mixed-data sampling (MIDAS) regressions and bridge equations. Both approach are used to nowcast a low-frequency variable such as quarterly GDP growth by higher-frequency business cycle indicators. Three...

This paper considers factor estimation from heterogenous data, where some of the variables are noisy and only weakly informative for the factors. To identify the irrelevant variables, we search for zero rows in the loadings matrix of the factor model. To sharply separate these irrelevant...

We propose a new approach to sample unobserved states conditional on available data in (conditionally) linear unobserved component models when some of the observations are missing. The approach is based on the precision matrix of the states and model variables, which is sparse and banded in many...

This paper compares the mixed-data sampling (MIDAS) and mixed-frequency VAR(MF-VAR) approaches to model speci…cation in the presence of mixed-frequency data,e.g., monthly and quarterly series. MIDAS leads to parsimonious models based onexponential lag polynomials for the coe¢ cients, whereas...

This paper discusses pooling versus model selection for now- and forecasting in the pres-ence of model uncertainty with large, unbalanced datasets. Empirically, unbalanceddata is pervasive in economics and typically due to di¤erent sampling frequencies andpublication delays. Two model classes...

This paper compares different ways to estimate the current state of the economy using factor models that can handle unbalanced datasets. Due to the different release lags of business cycle indicators, data unbalancedness often emerges at the end of multivariate samples, which is sometimes...