Applications of Time Series in Finance and Macroeconomics

This dissertation contains three applications of time series in finance and macroeconomics. The first essay compares the cumulative returns for stocks and bonds atinvestment horizons from one to ten years by using a test for spatial dominance.Spatial dominance is a variation of stochastic dominance for nonstationary variables.The results suggest that for investment horizons of one year, bonds spatially dominatestocks. In contrast, for investment horizons longer than five years, stocks spatiallydominate bonds. This result is consistent with the advice given by practitionersto long term investors of allocating a higher proportion of stocks in their portfoliodecisions.The second essay presents a method that allows testing of whether or not anasset stochastically dominates the other when the time horizon is uncertain. In thissetup, the expected utility depends on the distribution of the value of the asset aswell as the distribution of the time horizon, which together form the weighted spatialdistribution. The testing procedure is based on the Kolmogorov Smirnov distancebetween the empirical weighted spatial distributions. An empirical application ispresented assuming that the event of exit time follows an independent Poisson processwith constant intensity.The last essay applies a dynamic factor model to generate out-of-sample forecasts for the inflation rate in Mexico. Factor models are useful to summarize theinformation contained in large datasets. We evaluate the role of using a wide range ofmacroeconomic variables to forecast inflation, with particular interest on the importance of using the consumer price index disaggregated data. The data set contains 54macroeconomic series and 243 consumer price subcomponents from 1988 to 2008. Theresults indicate that factor models outperform the benchmark autoregressive model athorizons of one, two, four and six quarters. It is also found that using disaggregatedprice data improves forecasting performance.