This article considers the cointegrating regression with errors whose variances change smoothly over time. The model can be used to describe a long-run cointegrating relationship, the tightness of which varies along with time. Heteroskedasticity in the errors is modeled nonparametrically and is...

It is widely accepted that long-run elasticities of demand for electricity are not stable over time. We model long-run sectoral electricity demand using a time-varying cointegrating vector. Specifically, the coefficient on income (residential sector) or output (commercial and industrial sectors)...

It is widely accepted that long-run elasticities of demand for electricity are not stable over time. We model long-run sectoral electricity demand using a time-varying cointegrating vector. Specifically, the coefficient on income (residential sector) or output (commercial and industrial sectors)...

We introduce a panel model with a nonparametric functional coefficient of multiple arguments. The coefficient is a function both of time, allowing temporal changes in an otherwise linear model, and of the regressor itself, allowing nonlinearity. In contrast to a time series model, the effects of...

Based on previous studies related to the yeast cell cycle, it is well known that the underlying cellular network in yeast consists of many interactions between genes that have periodic expression patterns during the cell division cycle. In this study, it is proposed that cell cycle-specific gene...

This paper shows that the nonlinear least squares estimator for unit root models has the limiting distribution free of nuisance parameters and is more efficient than the augmented Dickey-Fuller estimator when the sum of coefficients for lagged variables is negative.

This paper shows that the excessive volatility results in spurious regressions. The spuriousness can be driven by persistency in the error variances unlike the conventional spurious regressions that are generated by the persistency in the level of regression errors.

This paper proposes an estimation method for a partial parametric model with multiple integrated time series. Our estimation procedure is based on the decomposition of the nonparametric part of the regression function into homogeneous and integrable components. It consists of two steps: In the...