C22 Time-Series Models; Dynamic Quantile Regressions (Updated!)
Capturing the zero: a new class of zero-augmented distributions and multiplicative error processes
- We propose a novel approach to model serially dependent positive-valued variables which realize a non-trivial proportion of zero outcomes. This is a typical phenomenon in financial time series observed at high frequencies, such as cumulated trading volumes. We introduce a flexible point-mass mixture distribution and develop a semiparametric specification test explicitly tailored for such distributions. Moreover, we propose a new type of multiplicative error model (MEM) based on a zero-augmented distribution, which incorporates an autoregressive binary choice component and thus captures the (potentially different) dynamics of both zero occurrences and of strictly positive realizations. Applying the proposed model to high-frequency cumulated trading volumes of both liquid and illiquid NYSE stocks, we show that the model captures the dynamic and distributional properties of the data well and is able to correctly predict future distributions.
Factor substitution and factor augmenting technical progress in the US : a normalized supply-side system approach
- Using a normalized CES function with factor-augmenting technical progress, we estimate a supply-side system of the US economy from 1953 to 1998. Avoiding potential estimation biases that have occurred in earlier studies and putting a high emphasis on the consistency of the data set, required by the estimated system, we obtain robust results not only for the aggregate elasticity of substitution but also for the parameters of labor and capital augmenting technical change. We find that the elasticity of substitution is significantly below unity and that the growth rates of technical progress show an asymmetrical pattern where the growth of laboraugmenting technical progress is exponential, while that of capital is hyperbolic or logarithmic.
Predicting recessions with interest rate spreads : a multicountry regime-switching analysis
- This study uses Markov-switching models to evaluate the informational content of the term structure as a predictor of recessions in eight OECD countries. The empirical results suggest that for all countries the term spread is sensibly modelled as a two-state regime-switching process. Moreover, our simple univariate model turns out to be a filter that transforms accurately term spread changes into turning point predictions. The term structure is confirmed to be a reliable recession indicator. However, the results of probit estimations show that the markov-switching filter does not significantly improve the forecasting ability of the spread.