Peter Burridge

Relationships between economic growth, foreign direct investment and trade: evidence from China

This study investigates the causal links between trade, economic growth and inward foreign direct investment (FDI) in China at the aggregate level. The integration and cointegration properties of quarterly data are analysed. Long-run relationships between growth, exports, imports and FDI are identified in a cointegration framework, in which this paper finds bi-directional causality between economic growth, FDI and exports. Economic development, exports and FDI appear to be mutually reinforcing under the open-door policy.

Value-at-risk: Applying the extreme value approach to Asian markets in the recent financial turmoil

Abstract Value-at-risk (VaR) measures are generated using extreme value theory by modelling the tails of the return distributions of six Asian financial markets during the recent volatile market conditions. The maxima and minima of these return series were found to be satisfactorily modelled within an extreme value framework and the value at risk measures generated within this structure were found to be different to those generated by variance–covariance and historical methods, particularly for markets characterised by high degrees of leptokurtosis such as Malaysia and Indonesia.

Unit root tests in the presence of uncertainty about the non-stochastic trend

A sequential procedure for determination of trend degree and testing for unit root is introduced; its properties are investigated by Monte Carlo experiments. We compare the performance of Augmented Dickey-Fuller tests and the GLS tests of Elliott, Rothenberg and Srock (1996), in both cases with lag length selected by the BIC criterion.

CALCULATING THE VARIANCE OF SEASONALLY ADJUSTED SERIES

Abstract This article considers the use of the Kalman filter to perform the seasonal adjustment and to calculate the variance of the signal extraction error in model-based seasonal adjustment procedures. The steady-state filter covariance is seen to provide a convenient basis for obtaining the variances not only of the current adjustment but also of subsequent revisions. The method is applied to the unobserved-components model we have recently proposed as a justification of the X-11 method and to a real economic time series.

On the Properties of Regression-Based Tests for Seasonal Unit Roots in the Presence of Higher-Order Serial Correlation

We analyze the behavior of widely used regression-based tests for seasonal unit roots when the shocks are serially correlated. We show, in the quarterly case, that the common assumption that serial correlation may be accommodated by augmenting the test regression with appropriate lagged seasonal differences is only partially correct. The limiting null distributions of t statistics for unit roots at the zero and Nyquist frequencies are corrected by the lag augmentation, but those of t statistics at the harmonic seasonal frequency are not. Fortunately, the joint F-type tests at the harmonic frequency, which are in widespread use, do remain pivotal and should therefore supplant the individual t statistics in applied work. That the latter are indeed badly behaved in finite samples, while the F-type tests are correctly sized, is demonstrated by a Monte Carlo experiment.

On regression-based tests for seasonal unit roots in the presence of periodic heteroscedasticity

In this paper, we analyse the behaviour of regression-based tests for seasonal unit roots when the error is periodically heteroscedastic. We show, using the case of quaterly data to illustrate, that the limiting null distribution of tests for unit roots at the zero and Nyquist frequencies are unaffected by the presence od periodic heteroscedastic behaviour in the error process.

The Limit Distribution of level Crossings of a Random Walk, and a Simple Unit Root Test

We derive the limit distribution of the number of crossings of a level by a random walk with continuously distributed increments, using a Brownian motion local time approximation. This complements the well-known result for the random walk on the integers. Use of the frequency of level crossings to test for a unit root is examined.

Prediction theory for autoregressivemoving average processes

This paper reviews statistical prediction theory for autoregressive-moving average processes wing techniques developed in control theory. It demonstrates explicitly the connectioluns between the statistical and control theory literatures. Both the forecasting problem and the Single extraction problem am considered, udng linear least squares methods. Whereas the classical Statistical theory developed by Wiener and Kolmogomv is restricted to stationary stochaotic processes, the recursive techniques known as the Kalman filter are shown to provide a satisfactory treatment of the difference-stationary care and other more general cases. Complete results for non-invertible moving averages are also obtained.

On the Power of GLS-Type Unit Root Tests

Elliott, Rothenberg and Stock (1996), (ERS), present a ‘GLS’ variant of the Dickey‐Fuller (DF) unit root test. Their statistic is approximately point‐optimal invariant at a chosen local alternative, and usually displays better finite sample power than the DF test. Following the usual efficiency motive for GLS estimation, the higher finite sample power of the ERS test has often been attributed to the greater accuracy of the estimate of the series’ non‐stochastic component under stationary alternatives close to the null. This paper shows that the GLS estimates of the non‐stochastic component are not, in general, more accurate. The power gain arises from the fact that the GLS statistics null distribution has a greater positive shift relative to the DF test, than its distribution under relevant alternatives, and this persists even when the GLS estimates of the non stochastics have higher variance than the OLS estimates.

Consistent estimation and order selection for nonstationary autoregressive processes with stable innovations

A possibly non-stationary autoregressive process, of unknown finite order, with possibly infinite-variance innovations is studied. The Ordinary Least Squares autoregressive parameter estimates are shown to be consistent, and their rate of convergence, which depends on the index of stability, α, is established. We also establish consistency of lag-order selection criteria in the non-stationary case. A small experiment illustrates the relative performance of different lag-length selection criteria in finite samples.