We forecast US GDP sampled quarterly over horizons ranging from one quarter to three years. Using AR-MIDAS models we study three lag polynomials: the Almon lag, the exponential Almon lag and the beta lag, and nine macroeconomic variables, sampled weekly or monthly. Our benchmark model is an AR(1) and we compare forecast errors using RMSE. In all instances the AR-MIDAS achieves lower forecast errors compared to the benchmark model. The predictor sampled weekly generally performs better compared to other predictors, which are sampled monthly.

This thesis suggests a Bayesian vector autoregressive (VAR) model which allows for explicit parametrization of the unconditional mean for data measured at different frequencies, without the need to aggregate data to the lowest common frequency. Using a normal prior for the steady-state and a normal-inverse Wishart prior for the dynamics and error covariance, a Gibbs sampler is proposed to sample the posterior distribution. A forecast study is performed using monthly and quarterly data for the US macroeconomy between 1964 and 2008. The proposed model is compared to a steady-state Bayesian VAR model estimated on data aggregated to quarterly frequency and a quarterly least squares VAR with standard parametrization. Forecasts are evaluated using root mean squared errors and the log-determinant of the forecast error covariance matrix. The results indicate that the inclusion of monthly data improves the accuracy of quarterly forecasts of monthly variables for horizons up to a year. For quarterly variables the one and two quarter forecasts are improved when using monthly data.

Most macroeconomic activity series such as Swedish GDP growth are collected quarterly while an important proportion of time series are recorded at a higher frequency. Thus, policy and business decision makers are often confront with the problems of forecasting and assessing current business and economy state via incomplete statistical data due to publication lags. In this paper, we survey a few general methods and examine different models for mixed frequency issues. We mainly compare mixed data sampling regression (MIDAS) and state space dynamic factor model (SS-DFM) by the comparison experiments forecasting Swedish GDP growth with various economic indicators. We find that single-indicator MIDAS is a wise choice when the explanatory variable is coincident with the target series; that an AR term enables MIDAS more promising since it considers autoregressive behaviour of the target series and makes the dynamic construction more flexible; that SS-DFM and M-MIDAS are the most outstanding models and M-MIDAS dominates undoubtedly at short horizons up to 6 months, whereas SS-DFM is more reliable at long predictive horizons. And finally we conclude that there is no perfect winner because each model can dominate in a special situation.

This doctoral dissertation proposes methodologies which, from a linear or a non-linear approach, accommodate to the information flow and can deal with a large amount of data. The empirical application of the proposed methodologies contributes to answer some of the questions that have emerged or that it has potentiated after the 2008 global crisis. Thus, essential aspects of the macroeconomic analysis are studied, like the identification and forecast of business cycles turning points, the business cycles interactions between countries or the development of tools able to forecast the evolution of key economic indicators based on new data sources, like those which emerge from search engines.