Different studies provide a surprisingly large variety of controversial conclusions about the forecasting power of an indicator, even when it is supposed to forecast the same time series. In this study, we aim to provide a thorough overview of linear forecasting techniques and draw conclusions...

We analyse the accuracy of an econometric model for nowcasting GDP growth in a true real-time setting. The analysis is based on a unique sample of nowcasts that were produced in real time and stored. Our results support the use of econometric models for nowcasting because the accuracy of these...

Beyond their importance from the regulatory policy point of view, Value-at-Risk (VaR) and Expected Shortfall (ES) play an important role in risk management, portfolio allocation, capital level requirements, trading systems, and hedging strategies. Unfortunately, due to the curse of...

We nowcast and forecast Austrian economic activity, namely real gross domestic product (GDP), consumption and investment, which are available at a quarterly frequency. While nowcasting uses data up to (and including) the quarter to be predicted, forecasting uses only data up to the previous...

We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the Efficient Market Hypothesis). We do not...

We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the weak form Efficient Market Hypothesis). We do...

We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the weak form Efficient Market Hypothesis). We do...

We develop a new targeted maximum likelihood estimation method that provides improved forecasting for misspecified linear autoregressive models. The method weighs data points in the observed sample and is useful in the presence of data generating processes featuring structural breaks, complex...

An effective and efficient search algorithm has been developed to select from an I(1) system zero-non-zero patterned cointegrating and loading vectors in a subset VECM, where the long term impact matrix contains zero entries. The Finnish money-output model presented by Johansen and Juselius...

In this paper, the authors empirically assess the extent to which early release inefficiency and definitional change affect prediction precision. In particular, they carry out a series of ex-ante prediction experiments in order to examine: the marginal predictive content of the revision process,...