Efstathios Paparoditis

Residual-Based Block Bootstrap for Unit Root Testing

A nonparametric, residual-based block bootstrap procedure is proposed in the context of testing for integrated (unit root) time series. The resampling procedure is based on weak assumptions on the dependence structure of the stationary process driving the random walk and successfully generates unit root integrated pseudo-series retaining the important characteristics of the data. It is more general than previous bootstrap approaches to the unit root problem in that it allows for a very wide class of weakly dependent processes and it is not based on any parametric assumption on the process generating the data. As a consequence the procedure can accurately capture the distribution of many unit root test statistics proposed in the literature. Large sample theory is developed and the asymptotic validity of the block bootstrap-based unit root testing is shown via a bootstrap functional limit theorem. Applications to some particular test statistics of the unit root hypothesis, i.e., least squares and Dickey-Fuller type statistics are given. The power properties of our procedure are investigated and compared to those of alternative bootstrap approaches to carry out the unit root test. Some simulations examine the finite sample performance of our procedure.

Spectral Density Based Goodness-of-Fit Tests for Time Series Models

A new goodness-of-fit test for time series models is proposed. The test statistic is based on the distance between a kernel estimator of the ratio between the true and the hypothesized spectral density and the expected value of the estimator under the null. It provides a quantification of how well a parametric spectral density model fits the sample spectral density (periodogram). The asymptotic distribution of the statistic proposed is derived and its power properties are discussed. To improve upon the large sample (Gaussian) approximation of the distribution of the test statistic under the null, a bootstrap procedure is presented and justified theoretically. The finite sample performance of the test is investigated through a simulation experiment and applications to real data sets are given.

On the range of validity of the autoregressive sieve bootstrap

We explore the limits of the autoregressive (AR) sieve bootstrap, and show that its applicability extends well beyond the realm of linear time series as has been previously thought. In particular, for appropriate statistics, the AR-sieve bootstrap is valid for stationary processes possessing a general Wold-type autoregressive representation with respect to a white noise; in essence, this includes all stationary, purely nondeterministic processes, whose spectral density is everywhere positive. Our main theorem provides a simple and effective tool in assessing whether the AR-sieve bootstrap is asymptotically valid in any given situation. In effect, the large-sample distribution of the statistic in question must only depend on the first and second order moments of the process; prominent examples include the sample mean and the spectral density. As a counterexample, we show how the AR-sieve bootstrap is not always valid for the sample autocovariance even when the underlying process is linear.

The local bootstrap for Markov processes

A nonparametric bootstrap procedure is proposed for stochastic processes which follow a general autoregressive structure. The procedure generates bootstrap replicates by locally resampling the original set of observations reproducing automatically its dependence properties. It avoids an initial nonparametric estimation of process characteristics in order to generate the pseudo-time series and the bootstrap replicates mimic several of the properties of the original process. Applications of the procedure in nonlinear time-series analysis are considered and theoretically justified; some simulated and real data examples are discussed.

The Local Bootstrap for Kernel Estimators under General Dependence Conditions

We consider the problem of estimating the distribution of a nonparametric (kernel) estimator of the conditional expectation g(x; φ) = E(φ(Xt+1) | Yt,m = x) of a strictly stationary stochastic process {Xt, t ≥ 1}. In this notation φ(·) is a real-valued Borel function and Yt,m a segment of lagged values, i.e., Yt,m=(Xt-i1,Xt-i2,...,Xt-im), where the integers ii, satisfy 0 ≤ i1. We show that under a fairly weak set of conditions on {Xt, t ≥ 1}, an appropriately designed and simple bootstrap procedure that correctly imitates the conditional distribution of Xt+1 given the selective past Yt,m, approximates correctly the distribution of the class of nonparametric estimators considered. The procedure proposed is entirely nonparametric, its main dependence assumption refers to a strongly mixing process with a polynomial decrease of the mixing rate and it is not based on any particular assumptions on the model structure generating the observations.

Bootstrapping unit root tests for autoregressive time series

The theory developed for bootstrapping unit root tests in an autoregressive (AR) context has been concerned mainly with the large-sample behavior of the methods proposed under the assumption that the null hypothesis is true. No results exist for the relative performance and the power behavior of the bootstrap methods under the alternative. This article studies the properties of different AR bootstrap schemes of the unit root hypothesis, including a new proposal based on unrestricted residuals. It shows that bootstrap procedures based on differencing the observed series suffer from power problems as compared with bootstrap procedures based on unrestricted residuals. Whereas for finite-order AR processes differencing leads to just a loss of power, for infinite-order autoregressions such a differencing makes the application of sieve AR bootstrap schemes inappropriate if the alternative is true. The superiority of the new bootstrap proposal is shown, and some numerical examples illustrate our theoretical findings.

Testing temporal constancy of the spectral structure of a time series

Statistical inference for stochastic processes with time-varying spectral characteristics has received considerable attention in recent decades. We develop a nonparametric test for stationarity against the alternative of a smoothly time-varying spectral structure. The test is based on a comparison between the sample spectral density calculated locally on a moving window of data and a global spectral density estimator based on the whole stretch of observations. Asymptotic properties of the nonparametric estimators involved and of the test statistic under the null hypothesis of stationarity are derived. Power properties under the alternative of a time-varying spectral structure are discussed and the behavior of the test for fixed alternatives belonging to the locally stationary processes class is investigated.

The Local Bootstrap for Periodogram Statistics

A bootstrap procedure for the periodogram of a weakly dependent stationary sequence is proposed. The method works by locally resampling the periodogram ordinates and does not require estimation of the spectral density and of frequency domain residuals obtained by means of initial smoothing. Asymptotic properties of the proposed bootstrap procedure are studied and consistency is proved for interesting classes of statistics including ratio statistics, kernel estimates of the spectral density and parameter estimates. Some practical aspects concerning the implementation of the method are also discussed.

Validating Stationarity Assumptions in Time Series Analysis by Rolling Local Periodograms

We propose a simple and powerful procedure to validate the assumption of weak stationarity in time series analysis. Our focus is on processes with a slowly varying autocovariance structure. The procedure evaluates the supremum over time of the L2-distance between the local sample spectral density (local periodogram) calculated using a segment of observations falling within a rolling window and an estimator of the spectral density obtained using the entire time series at hand. Large sample properties of a basic deviation process are investigated and critical values of a supremum type test are obtained using an appropriate bootstrap procedure. The finite sample size and power properties of the procedure are investigated by means of simulations. Real data examples demonstrate the ability of the procedure to detect (possible) changes in the autocovariance structure of a time series and to understand their nature.

Short-Term Load Forecasting: The Similar Shape Functional Time-Series Predictor

A novel functional time-series methodology for short-term load forecasting is introduced. The prediction is performed by means of a weighted average of past daily load segments, the shape of which is similar to the expected shape of the load segment to be predicted. The past load segments are identified from the available history of the observed load segments by means of their closeness to a so-called reference load segment. The latter is selected in a manner that captures the expected qualitative and quantitative characteristics of the load segment to be predicted. As an illustration, the suggested functional time-series forecasting methodology is applied to historical daily load data in Cyprus. Its performance is compared with some recently proposed alternative methodologies for short-term load forecasting.