| This thesis examines the predictive ability of models for forecasting inflation and financial market volatility. Emphasis is put on evaluation of forecasts and the usage of large data sets. Variety of models are used to forecast inflation, including diffusion indices, artificial neural networks, and traditional linear regressions. Financial market volatility is forecast using various GARCH-type and high-frequency based models. High-frequency data are also used to obtain ex-post estimates of volatility, which is then used to evaluate forecasts. All forecast are evaluated using recently proposed techniques that can account for data snooping bias, nested, and nonlinear models. |
