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Multinational enterprises (MNEs) play a dominant role in the international business (IB) literature. Traditionally, by far the majority of IB studies deal with issues at the micro level of the individual MNE, or at the meso level of a sample of individual MNEs in industries. This paper focuses on the impact of MNE behavior through foreign direct investment (FDI) on a country’s international trade, and vice versa. In so doing, this study responds to a recent plea for more macro‐level studies in IB into the effect of MNE behavior on the macroeconomic performance of countries as a whole, particularly developing and emerging economies. In the current study, we focus on the largest developing or emerging economy of all: China. Applying sophisticated econometric techniques, we unravel the causality and direction of FDI‐trade linkages for the Chinese economy in the 1980‐2003 period.

Maizels hypothesizes that export provides more boosts for saving than the non‐export part of GDP. We use recent time series techniques of unit root and cointegration to test the Maizels' hypothesis for nine Latin American countries. Cointegrated vectors and OLS estimates in first differences of the variables provide support for the Maizels' hypothesis.

It is found that one unit root, common trend is shared by the quarterly auction price series of five frequently auctioned types of stamps. The common trends analysis provides specific, stationary linear combinations, or cointegrating portfolios, of the auction price levels. The quarterly returns for the system of cointegrated auction prices can be represented by an error correction model using past returns and cointegrating vectors. There is evidence of a positive relationship between changes in the common trend and leading changes in industrial production

Several recent studies have indicated the existence of a predictable component in stock prices. This study examines the sources of this serial correlation using error‐correction models. The results show that autocorrelated economic variables can generate serial correlation in stock returns. After these effects are accounted for, however, significant serial correlation in stock prices remains. The activities of noise traders and inefficiencies in the pricing of securities, within the context of limitations to the arbitrage process, are suggested as additional sources of serial correlation in stock prices.

The editors are pleased to offer the following papers to the reader in recognition and appreciation of the contributions to our literature made by Robert Engle and Sir Clive Granger, winners of the 2003 Nobel Prize in Economics. Please see the previous dedication page of this volume. The basic themes of this part of Volume 20 of Advances in Econometrics are time-varying betas of the capital asset pricing model, analysis of predictive densities of nonlinear models of stock returns, modeling multivariate dynamic correlations, flexible seasonal time series models, estimation of long-memory time series models, the application of the technique of boosting in volatility forecasting, the use of different time scales in Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) modeling, out-of-sample evaluation of the ‘Fed Model’ in stock price valuation, structural change as an alternative to long memory, the use of smooth transition autoregressions in stochastic volatility modeling, the analysis of the “balancedness” of regressions analyzing Taylor-type rules of the Fed Funds rate, a mixture-of-experts approach for the estimation of stochastic volatility, a modern assessment of Clive's first published paper on sunspot activity, and a new class of models of tail-dependence in time series subject to jumps. Of course, we are also pleased to include Rob's and Clive's remarks on their careers and their views on innovation in econometric theory and practice that were given at the Third Annual Advances in Econometrics Conference held at Louisiana State University, Baton Rouge, on November 5–7, 2004.

Volume 20 of Advances in Econometrics is dedicated to Rob Engle and Sir Clive Granger, winners of the 2003 Nobel Prize in Economics, for their many valuable contributions to the econometrics profession. The Royal Swedish Academy of Sciences cited Rob “for methods of analyzing economic time series with time-varying volatility (ARCH),” while Clive was cited “for methods of analyzing economic time series with common trends (cointegration).” Of course, these citations are meant for public consumption but we specialists in time-series analysis know their contributions go far beyond these brief citations. Consider some of Rob's other contributions to our literature: Aggregation of Time Series, Band Spectrum Regression, Dynamic Factor Models, Exogeneity, Forecasting in the Presence of Cointegration, Seasonal Cointegration, Common Features, ARCH-M, Multivariate GARCH, Analysis of High Frequency Data, and CAViaR. Some of Sir Clive's additional contributions include Spectral Analysis of Economic Time Series, Bilinear Time Series Models, Combination Forecasting, Spurious Regression, Forecasting Transformed Time Series, Causality, Aggregation of Time Series, Long Memory, Extreme Bounds, Multi-Cointegration, and Non-linear Cointegration. No doubt, their Nobel Prizes are richly deserved. And the 48 authors of the two parts of this volume think likewise. They have authored some very fine papers that contribute nicely to the same literature that Rob's and Clive's research helped build.

Volume 20 of Advances in Econometrics is dedicated to Rob Engle and Sir Clive Granger, winners of the 2003 Nobel Prize in Economics, for their many valuable contributions to the econometrics profession. The Royal Swedish Academy of Sciences cited Rob “for methods of analyzing economic time series with time-varying volatility (ARCH)” while Clive was cited “for methods of analyzing economic time series with common trends (cointegration).” Of course, these citations are meant for public consumption but we specialists in time series analysis know their contributions go far beyond these brief citations. Consider some of Rob's other contributions to our literature: Aggregation of Time Series, Band Spectrum Regression, Dynamic Factor Models, Exogeneity, Forecasting in the Presence of Cointegration, Seasonal Cointegration, Common Features, ARCH-M, Multivariate GARCH, Analysis of High Frequency Data, and CAViaR. Some of Sir Clive's additional contributions include Spectral Analysis of Economic Time Series, Bilinear Time Series Models, Combination Forecasting, Spurious Regression, Forecasting Transformed Time Series, Causality, Aggregation of Time Series, Long Memory, Extreme Bounds, Multi-Cointegration, and Non-linear Cointegration. No doubt, their Nobel Prizes are richly deserved. And the 48 authors of the two parts of this volume think likewise. They have authored some very fine papers that contribute nicely to the same literature that Rob's and Clive's research helped build.

In a brilliant career spanning almost five decades, Sir Clive Granger has made numerous contributions to time series econometrics. This paper reappraises his very first paper, published in 1957 on sunspot numbers.

Relying on Clive Granger's many and varied contributions to econometric analysis, this paper considers some of the key econometric considerations involved in estimating Taylor-type rules for US data. We focus on the roles of unit roots, cointegration, structural breaks, and non-linearities to make the case that most existing estimates are based on an unbalanced regression. A variety of estimates reveal that neglected cointegration results in the omission of a necessary error correction term and that Federal Reserve (Fed) reactions during the Greenspan era appear to have been asymmetric. We argue that error correction and non-linearities may be one way to estimate Taylor rules over long samples when the underlying policy regime may have changed significantly.