Fixed-smoothing asymptotics for time series

To “ Fixed - Smoothing Asymptotics for Time Series

We assume the (i, j)th element and the (j, i)th element of Σ are functionally unrelated. The results can be extended to the case where symmetric matrix elements are considered functionally equal (see e.g., McCulloch [5]). In the following, we use ⊗ to denote the Kronecker product in matrix algebra and use vec to denote the operator that transforms a matrix into a column vector by stacking the c...

Fixed - Smoothing Asymptotics for Time Series

In this paper, we derive higher order Edgeworth expansions for the finite sample distributions of the subsampling-based t-statistic and the Wald statistic in the Gaussian location model under the so-called fixed-smoothing paradigm. In particular, we show that the error of asymptotic approximation is at the order of the reciprocal of the sample size and obtain explicit forms for the leading erro...

Fixed-smoothing Asymptotics in a Two-step GMM Framework

The paper develops the …xed-smoothing asymptotics in a two-step GMM framework. Under this type of asymptotics, the weighting matrix in the second-step GMM criterion function converges weakly to a random matrix and the two-step GMM estimator is asymptotically mixed normal. Nevertheless, the Wald statistic, the GMM criterion function statistic and the LM statistic remain asymptotically pivotal. I...

Fixed-smoothing asymptotics in the generalized empirical likelihood estimation framework

A new adaptive exponential smoothing method for non-stationary time series with level shifts

Simple exponential smoothing (SES) methods are the most commonly used methods in forecasting and time series analysis. However, they are generally insensitive to non-stationary structural events such as level shifts, ramp shifts, and spikes or impulses. Similar to that of outliers in stationary time series, these non-stationary events will lead to increased level of errors in the forecasting pr...