Using penalized spline, generalized additive model and mixed model regression techniques to examine univariate and multivariate time series and in particular business cycles

Teuber T. Using penalized spline, generalized additive model and mixed model regression techniques to examine univariate and multivariate time series and in particular business cycles. Bielefeld: Universität Bielefeld; 2013

Interpreting Business Cycles as Generalized Two-Dimensional Loops Using Penalized Splines Regression Techniques

The two-dimensional circular structure model by Kauermann, Teuber, and Flaschel (2011) will be extended to estimate more than two time series simultaneously. It will be assumed that the multivariate time series follow a cycle over the time. However, the radius and the angle are allowed to smoothly change over the time and will be estimated using a Penalized Spline Regression Technique. The model will be put to life using the Leading, Coincident and Lagging Indicators provided by the Conference Board. The model confirms known observations and sheds new light in the business cycle discussion concerning the long-term trend of economic time series, duration and dating of the business cycles, and the measurement and confirmation of leading and lagging characteristics of time series. JEL Classifikation: C32, C14, E3

Exploring US Business Cycles with Bivariate Loops Using Penalized Spline Regression

Kauermann G, Teuber T, Flaschel P. Exploring US Business Cycles with Bivariate Loops Using Penalized Spline Regression. Computational Economics. 2012;39(4):409-427.The phrase business cycle is usually used for short term fluctuations in macroeconomic time series. In this paper we focus on the estimation of business cycles in a bivariate manner by fitting two series simultaneously. The underlying model is thereby nonparametric in that no functional form is prespecified but smoothness of the functions are assumed. The functions are then estimated using penalized spline estimation. The bivariate approach will allow to compare business cycles, check and compare phase lengths and visualize this in forms of loops in a bivariate way. Moreover, the focus is on separation of long and short phase fluctuation, where only the latter is the classical business cycle while the first is better known as Friedman or Goodwin cycle, respectively. Again, we use nonparametric models and fit the functional shape with penalized splines. For the separation of long and short phase components we employ an Akaike criterion