We present a Bayesian sampling algorithm for parameter estimation in a discrete-response model, where the dependent variables contain two layers of binary choices and one ordered response. Our investigation is motivated by an empirical study using such a double-selection rule for three...

We present a Bayesian sampling approach to parameter estimation in a discrete-response model with double rules of selectivity, where the dependent variables contain two layers of binary choices and one ordered response. Our investigation is motivated by an empirical study using such a...

The global linear trend with autocorrelated disturbances is a surprising omission from the M1 competition. This approach to forecasting is therefore evaluated using the 51 non-seasonal series from the competition. It is contrasted with a fully optimized version of Holts trend corrected...

In this paper we present a test statistic, which will be used to test for significant differences between generating processes of two time series that may be logically connected. The test statistic is based on the differences between estimated parameters of the autoregressive models which are...

This study proposes a new approach to estimation of the time series properties of daily volatility in financial markets.

In this paper we propose a new test procedure with more general steady state information to test the convergence hypothesis for a specific economy. We consider a model where demeaned per capita output of an economy is a function of time trend and then set the convergence hypothesis as negative...

Estimation of the reduced rank regression model requires restrictions be imposed upon the model. Two forms of restrictions are commonly used. Earlier Bayesian work relied on the triangular method of identification which imposes an a priori ordering on the variables in the system, however,...

Forecasting large numbers of time series is a costly and time-consuming exercise. Before forecasting a large number of series that are logically connected in some way, the authors can first cluster them into groups of similar series. In this paper they investigate forecasting the series in each...