In this paper, an autoregressive moving average (ARMA) model with threshold generalized autoregressive conditional heteroscedasticity (TGARCH) innovations is considered to model Chilean economic uncertainty time series. Uncertainty is measured through the Business Confidence Index (BCI) and...

This paper describes a moments estimator for a standard state-space model with coefficients generated by a random walk. The method calculates the conditional expectations of the coefficients, given the observations. A penalized least squares estimation is linked to the GLS (Aitken) estimates of...

Financial markets witness high levels of activity at certain times but remain calm at others. This makes the flow of physical time discontinuous. Therefore, to use physical time scales for studying financial time series runs the risk of missing important activities. An alternative approach is to...

Market growth is driven by product innovation. Beyond functional satiation the marginal utility of product performance and variety decreases. We argue that social comparisons underlying innovation diffusion results in consumer motivations for upward assimilation toward the behavior of better...

The current economic crisis requires fast information to predict economic behavior early, which is difficult at times of structural changes. This paper suggests an innovative new method of using data on internet activity for that purpose. It demonstrates strong correlations between keyword...

This paper discusses various approaches to decompose economic time series into their trend and cyclical components. For over 30 years now, the Deutsche Bundesbank publishes trend-adjusted indicators in its Statistical Supplement 4 entitled ?Seasonally Adjusted Business Statistics? which are...

Financial markets witness high levels of activity at certain times, but remain calm at others. This makes the flow of physical time discontinuous. Therefore using physical time scales for studying financial time series, runs the risk of missing important activities. An alternative approach is...

In the existing literature, conditional forecasts in the vector autoregressive (VAR) framework have not been commonly presented with probability distributions or error bands. This paper develops Bayesian methods for computing such distributions or bands. It broadens the class of conditional...

Many economic applications call for simultaneous equations VAR modeling. We show that the existing importance sampler can be prohibitively inefficient for this type of models. We develop a Gibbs simulator that works for both simultaneous and recursive VAR models with a much broader range of...

Causal analysis in multiple equation models often revolves around the evaluation of the effects of an exogenous shift in a structural equation. When taking into account the uncertainty implied by the shape of the likelihood, we argue that how normalization is implemented matters for inferential...