This note investigates the spurious regression where each of the regressand and the regressor follows a random walk with zero, nonzero local, or nonzero constant drift. In the existing literature of spurious regression, both the regressand and regressor have zero or constant drifts. We consider...

We propose a new time series model where the threshold is specified as an empirical quantile of recent observations of a threshold variable. The resulting conditional threshold traces the fluctuation of the threshold variable, which can enhance the fit and interpretation of the model. In the...

Traditional tests of the white noise hypothesis on the residuals from estimated models can leadto size distortion if some parameters are weakly identified. This paper develops a bootstrappedwhite noise test for serial correlation that is robust to weak identification in the parameters. Weshow...

We propose moving average threshold heterogeneous autoregressive (MAT-HAR) models as a novel combination of heterogeneous autoregression (HAR) and threshold autoregression (TAR). The MAT-HAR has multiple groups of lags of a target series, and a threshold term can appear in each group. The...

The existing literature of copula-based regression assumes that complete data are available, but this assumption is violated in many real applications. The present paper allows the regressand and regressors to be missing at random (MAR). We formulate a generalized regression model which unifies...

The existing vector heterogeneous autoregression (VHAR) does not allow for threshold effects. The threshold autoregressions are well established in the literature, but the presence of an unknown threshold complicates inference. To resolve this dilemma, we propose the vector moving average...

This paper presents a weighted optimization framework that unifies the binary, multi-valued, continuous, as well as mixture of discrete and continuous treatment, under unconfounded treatment assignment. With a general loss function, the framework includes the average, quantile and asymmetric...

This paper presents a weighted optimization framework that unifies the binary, multivalued, and continuous treatment - as well as mixture of discrete and continuous treatment - under a unconfounded treatment assignment. With a general loss function, the framework includes the average, quantile,...