| Chapter I of this dissertation explores how misspecification of a panel regression with a large cross-section dimension but a small time dimension affects its estimation and forecasts. This paper considers the effects of individual-specific factors in a dynamic panel regression. Theoretical results show that a size-factor with a long-tailed distribution or a time-varying property may cause spurious stochastics in a regression.; Chapter II introduces a new type of nonlinear model, the min-max model, and analyzes the properties for a pair of series. Stability conditions of this system are given for the nonlinearly integrated bivariate series. Under these stability conditions, the difference of the two series has a threshold-type nonlinearity. One can construct a threshold error correction model from min-max processes. Neglected nonlinearity tests are applied to the univariate series and to the system to detect nonlinearity, and it turns out the tests using the system have better power and the neural network test has good power in many cases. We apply the min-max model to U.S. Treasury bill and commercial paper interest rates.; Chapter III investigates systematically the use of mixed-frequency data sets and suggests that the use of high frequency data in forecasting economic aggregates can improve forecast accuracy. The best way of using this information is to build a single model, for example, an ARMA model with missing observations, that relates data of all frequencies. The implementation of such an approach, however, poses serious practical problems in all but the simplest cases. As a feasible and consistent alternative, we propose a two-stage procedure to obtain pseudo high frequency data and to subsequently use these artificial values as proxies for macroeconomic or financial models. This alternative method yields a sub-optimal forecast in general but avoids the computational problems of a full-blown single model. Our approach differs from classical interpolation since we only use past and current information to get the pseudo series.; Chapter IV demonstrates that a series with breaks can mimic some of the properties of I(d) processes, particularly the autocorrelations, where d can be a fraction, its value depending on the number of breaks for a particular sample size. (Abstract shortened by UMI.)... |
