Byeongchan Seong

Additional sources of bias in half-life estimation

Recently, an increasing amount of attention is being paid to biases in the measurement of time series dynamics based on calculations of half-life. In particular, this issue amplifies the controversy surrounding the purchasing power parity doctrine. Cross-sectional and temporal aggregations, along with mis-specified models, were previously identified as sources of this bias. We identified several other sources of bias, namely, sampling error, incorrect approximations, and structural breaks in time series. These sources should also receive sufficient attention for a sound measurement of half-life.

On-Line Prediction of Nonstationary Variable-Bit-Rate Video Traffic

In this paper, we propose a model-based bandwidth prediction scheme for variable-bit-rate (VBR) video traffic with regular group of pictures (GOP) pattern. Multiplicative ARIMA (autoregressive integrated moving-average) process called GOP ARIMA (ARIMA for GOP) is used as a base stochastic model, which consists of two key ingredients: prediction and model validity check. For traffic prediction, we deploy a Kalman filter over GOP ARIMA model, and confidence interval analysis for validity determination. The GOP ARIMA mPodel explicitly models inter and intra-GOP frame size correlations and the Kalman filter-based prediction maintains ?state? across the prediction rounds. Synergy of the two successfully addresses a number of challenging issues, such as a unified framework for frame type dependent prediction, accurate prediction, and robustness against noise. With few exceptions, a single video session consists of several scenes whose bandwidth process may exhibit different stochastic nature, which hinders recursive adjustment of parameters in Kalman filter, because its stochastic model structure is fixed at its deployment. To effectively address this issue, the proposed prediction scheme harbors a statistical hypothesis test in the prediction framework. By formulating the confidence interval of a prediction in terms of Kalman filter components, it not only predicts the frame size but also determines validity of the stochastic model. Based upon the results of the model validity check, the proposed prediction scheme updates the structures of the underlying GOP ARIMA model. We perform a comprehensive performance study using publicly available MPEG-2 and MPEG-4 traces. We compare the prediction accuracy of four different prediction schemes. In all traces, the proposed model yields superior prediction accuracy than the other prediction schemes. We show that confidence interval analysis effectively detects the structural changes in the sample sequence and that properly updating the model results in more accurate prediction. However, model update requires a certain length of observation period, e.g., 60 frames (2 s). Due to this learning overhead, the advantage of model update becomes less significant when scene length is short. Through queueing simulation, we examine the effect of prediction accuracy over user perceivable QoS. The proposed bandwidth prediction scheme allocates less 50% of the queue(buffer) compared to the other bandwidth prediction schemes, but still yields better packet loss behavior.

Estimation of Vector Error Correction Models with Mixed‐Frequency Data

Vector autoregressive (VAR) models with error‐correction structures (VECMs) that account for cointegrated variables have been studied extensively and used for further analyses such as forecasting, but only with single‐frequency data. Both unstructured and structured VAR models have been estimated and used with mixed‐frequency data. However, VECMs have not been studied or used with mixed‐frequency data. The article aims partly to fill this gap by estimating a VECM using the expectation‐maximization (EM) algorithm and US data on four monthly coincident indicators and quarterly real GDP and, then, using the estimated model to compute in‐sample monthly smoothed estimates and out‐of‐sample monthly forecasts of GDP. Because the model is treated as operating at the highest monthly frequency and the monthly‐quarterly data are used as given (neither interpolated to all‐monthly data, nor aggregated to all‐quarterly data), the application is expected to be unbiased and efficient. A Monte Carlo analysis compares the accuracy of VECMs estimated with the given mixed‐frequency data vs. with their single‐frequency temporal aggregate.

Maximum Eigenvalue Test for Seasonal Cointegrating Ranks

The maximum eigenvalue (ME) test for seasonal cointegrating ranks is presented using the approach of Cubadda [Oxford Bulletin of Economics and Statistics (2001), Vol. 63, pp. 497–511], which is computationally more efficient than that of Johansen and Schaumburg [Journal of Econometrics (1999), Vol. 88, pp. 301–339]. The asymptotic distributions of the ME test statistics are obtained for several cases that depend on the nature of deterministic terms. Monte Carlo experiments are conducted to evaluate the relative performances of the proposed ME test and the trace test, and we illustrate these tests using a monthly time series.

A note on spurious regression in seasonal time series

This paper considers spurious regression between two different types of seasonal time series: one with a deterministic seasonal component and the other with a stochastic seasonal component. When one type of seasonal time series is regressed on the other type and they are independent of each other, the phenomenon of spurious regression occurs. Asymptotic properties of the regression coefficient estimator and the associated regression ‘t-ratio’ are studied. A Monte Carlo simulation study is conducted to confirm the phenomenon of spurious regression and spurious rejection of seasonal cointegration for finite samples.

Bootstrap test for seasonal cointegrating ranks

We consider a bootstrap algorithm for the Likelihood Ratio (LR) test of seasonal Cointegrating (CI) ranks as the extension of Swensen (2006). Through a small Monte Carlo simulation experiment, we find that the bootstrap algorithm can effectively improve size distortions of the LR test.

Comparison of Forecasting Performance in Multivariate Nonstationary Seasonal Time Series Models

This paper studies the analysis of multivariate nonstationary time series with seasonality. Three types of multivariate time series models are considered: seasonal cointegration model, nonseasonal cointegration model with seasonal dummies, and vector autoregressive model in seasonal differences that are compared for forecasting performances using Korean macro-economic time series data. The cointegration models produce smaller forecast errors in short horizons; however, when longer forecasting periods are considered the vector autoregressive model appears preferable.

A Feasible Two-Step Estimator for Seasonal Cointegration

This paper considers a feasible two-step estimator for seasonal cointegration as the extension of and (2005). It is shown that the reducedrank maximum likelihood(ML) estimator for seasonal cointegration can still produce occasional outliers as that for non-seasonal cointegration even though the sizes of them are not extreme as those in non-seasonal cointegration. The ML estimator(MLE) is compared with the two-step estimator in a small Monte Carlo simulation study and we find that the two-step estimator can be an attractive alternative to the MLE, especially, in a small sample.

Joint Test for Seasonal Cointegrating Ranks

In this paper we consider a joint test for seasonal cointegrating(CI) ranks that enables us to simultaneously model cointegrated structures across seasonal unit roots in seasonal cointegration. A CI rank test for a single seasonal unit root is constructed and extended to a joint test for multiple seasonal unit roots. Their asymptotic distributions and selected critical values for the joint test are obtained. Through a small Monte Carlo simulation study, we evaluate performances of the tests.

Inference of seasonal cointegration with linear restrictions

In this article, we study the statistical inference of seasonal cointegration with joint linear restrictions among cointegrating vectors associated with possibly different seasonal unit roots. A Wald-type test and a likelihood ratio test are considered. For the development of the test statistics, we use the Gaussian reduced-rank estimation of Ahn et al. [Ahn, S.K., Cho, S. and Seong, B.C., 2004, Inference of seasonal cointegration: Gaussian reduced rank estimation and tests for various types of cointegration. Oxford Bulletin of Economics and Statistics, 66, 261–284], which simultaneously accommodates the cointegration corresponding to all seasonal unit roots. We then obtain the asymptotic distributions of the test statistics. We present methods for accommodating linear restrictions in the Gaussian reduced-rank estimation and obtain the related asymptotic distributions. A Monte Carlo simulation is conducted to investigate small-sample properties of the test statistics for some linear restrictions.