This paper contains a nonlinear, nonstationary autoregressive model whose intercept changes deterministically over time. The intercept is a flexible function of time, and its construction bears some resemblance to neural network models. A modelling technique, modified from one for single...

We consider the problem of testing for a structural break in the spatial lag parameter in a panel model (spatial autoregressive). We propose a likelihood ratio test of the null hypothesis of no break against the alternative hypothesis of a single break. The limiting distribution of the test is...

We consider the problem of testing for a structural break in the spatial lag parameter in a panel model (spatial autoregressive). We propose a likelihood ratio test of the null hypothesis of no break against the alternative hypothesis of a single break. The limiting distribution of the test is...

Even though the impacts of the globalization on economic growth and structural changes are inevitable, many developing countries are slowly transformed in the process. This paper examines the impact of structural transformation of Sri Lanka's economy on sectoral interdependencies to provide...

Performance of unit root tests depends on several specification decisions prior to their application, e.g., whether or not to include a deterministic trend. Since there is no standard procedure for making such decisions; therefore, the practitioners routinely make several arbitrary specification...

Literature shows that the regression of independent and (nearly) nonstationary time series could result in spurious outcomes. In this paper, we conjecture that under some situations, the regression of two independent and nearly non-stationary series does not have any spurious problem at all. To...

Performance of unit root tests depends on several specification decisions prior to their application, e.g., whether or not to include a deterministic trend. Since there is no standard procedure for making such decisions; therefore, the practitioners routinely make several arbitrary specification...

A Hidden Markov Model (HMM) is used to model the VIX (the Cboe Volatility Index). A 4- state Gaussian mixture is fitted to the VIX price history from 1990 to 2022. Using a growing window of training data, the price of the S&P500 is predicted and two trading algorithms are presented, based on the...