Showing papers on "Cointegration published in 1999"

An Autoregressive Distributed-Lag Modelling Approach to Cointegration Analysis

Abstract: This paper examines the use of autoregressive distributed lag (ARDL) models for the analysis of long-run relations when the underlying variables are I(1). It shows that after appropriate augmentation of the order of the ARDL model, the OLS estimators of the short-run parameters are p T -consistent with the asymptotically singular covariance matrix, and the ARDL-based estimators of the long-run coe¢cients are super-consistent, and valid inferences on the long-run parameters can be made using standard normal asymptotic theory. The paper also examines the relationship between the ARDL procedure and the fully modi…ed OLS approach of Phillips and Hansen to estimation of cointegrating relations, and compares the small sample performance of these two approaches via Monte Carlo experiments. These results provide strong evidence in favour of a rehabilitation of the traditional ARDL approach to time series econometric modelling. The ARDL approach has the additional advantage of yielding consistent estimates of the long-run coe¢cients that are asymptotically normal irrespective of whether the underlying regressors are I(1) or I(0). JEL Classi…cations: C12, C13, C15, C22. Key Words: Autoregressive distributed lag model, Cointegration, I(1) and I(0) regressors, Model selection, Monte Carlo simulation. ¤This is a revised version of a paper presented at the Symposium at the Centennial of Ragnar Frisch, The Norwegian Academy of Science and Letters, Oslo, March 3-5, 1995. We are grateful to Peter Boswijk, Clive Granger, Alberto Holly, Kyung So Im, Brendan McCabe, Steve Satchell, Richard Smith, Ron Smith and an anonymous referee for helpful comments. Partial …nancial support from the ESRC (Grant No. R000233608) and the Isaac Newton Trust of Trinity College, Cambridge is gratefully acknowledged.

Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors

Abstract: I. INTRODUCTION In this paper we describe a method for testing the null of no cointegration in dynamic panels with multiple regressors and compute approximate critical values for these tests. Methods for non-stationary panels, including panel unit root and panel cointegration tests, have been gaining increased acceptance in recent empirical research. To date, however, tests for the null of no cointegration in heterogeneous panels based on Pedroni (1995, 1997a) have been limited to simple bivariate examples, in large part due to the lack of critical values available for more complex multivariate regressions. The purpose of this paper is to fill this gap by describing a method to implement tests for the null of no cointegration for the case with multiple regressors and to provide appropriate critical values for these cases. The tests allow for considerable heterogeneity among individual members of the panel, including heterogeneity in both the long-run cointegrating vectors as well as heterogeneity in the dynamics associated with short-run deviations from these cointegrating vectors.

Linear Regression Limit Theory for Nonstationary Panel Data

Abstract: This paper develops a regression limit theory for nonstationary panel data with large numbers of cross section (n) and time series (T) observations. The limit theory allows for both sequential limits, wherein T→∞ followed by n→∞, and joint limits where T, n→∞ simultaneously; and the relationship between these multidimensional limits is explored. The panel structures considered allow for no time series cointegration, heterogeneous cointegration, homogeneous cointegration, and near-homogeneous cointegration. The paper explores the existence of long-run average relations between integrated panel vectors when there is no individual time series cointegration and when there is heterogeneous cointegration. These relations are parameterized in terms of the matrix regression coefficient of the long-run average covariance matrix. In the case of homogeneous and near homogeneous cointegrating panels, a panel fully modified regression estimator is developed and studied. The limit theory enables us to test hypotheses about the long run average parameters both within and between subgroups of the full population.

Panel Data Unit Roots and Cointegration: An Overview

Abstract: Recent developments in the field of the econometrics of panel data with non-stationary series are reviewed and interpreted. In particular, we discuss tests for unit roots and cointegration, and the roles of mean and variance correction, non-parametric correction and full modification for the construction of these tests and estimators. A discussion of the key contributions of the papers in this special issue is placed within the framework of the current literature and areas for further development are proposed.

Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors

Abstract: In this paper we describe a method for testing the null of no cointegration in dynamic panels with multiple regressors and compute approximate critical values for these tests. Methods for non-stationary panels, including panel unit root and panel cointegration tests, have been gaining increased acceptance in recent empirical research. To date, however, tests for the null of no cointegration in heterogeneous panels based on Pedroni (1995, 1997a) have been limited to simple bivariate examples, in large part due to the lack of critical values available for more complex multivariate regressions. The purpose of this paper is to fill this gap by describing a method to implement tests for the null of no cointegration for the case with multiple regressors and to provide appropriate critical values for these cases. The tests allow for considerable heterogeneity among individual members of the panel, including heterogeneity in both the long-run cointegrating vectors as well as heterogeneity in the dynamics associated with short-run deviations from these cointegrating vectors.

Forecasting Non-Stationary Economic Time Series

Abstract: Economies evolve and are subject to sudden shifts precipitated by legislative changes, economic policy, major discoveries, and political turmoil. Macroeconometric models are a very imperfect tool for forecasting this highly complicated and changing process. Ignoring these factors leads to a wide discrepancy between theory and practice.In their second book on economic forecasting, Michael P. Clements and David F. Hendry ask why some practices seem to work empirically despite a lack of formal support from theory. After reviewing the conventional approach to economic forecasting, they look at the implications for causal modeling, present a taxonomy of forecast errors, and delineate the sources of forecast failure. They show that forecast-period shifts in deterministic factors--interacting with model misspecification, collinearity, and inconsistent estimation--are the dominant source of systematic failure. They then consider various approaches for avoiding systematic forecasting errors, including intercept corrections, differencing, co-breaking, and modeling regime shifts; they emphasize the distinction between equilibrium correction (based on cointegration) and error correction (automatically offsetting past errors). Finally, they present three applications to test the implications of their framework. Their results on forecasting have wider implications for the conduct of empirical econometric research, model formulation, the testing of economic hypotheses, and model-based policy analyses.

Are Asian stock market fluctuations due mainly to intra-regional contagion effects? Evidence based on Asian emerging stock markets

Abstract: The main purpose of the study is: (i) to examine the long- and short-term dynamic linkages among international and Asian emerging stock markets and then (ii) try to quantify the extent of the Asian stock market fluctuations which are explained by intra-regional contagion effect. The study, therefore, proceeds first by examining the dynamic causal linkages among eight national daily stock price indices (four major established markets and four Asian emerging markets) and then quantifying the extent of their dynamic interdependencies through the application of recent time-series econometric techniques (a) vector error-correction model (Toda and Phillips, 1993) and (b) level VAR model containing integrated and cointegrated processes of arbitrary orders (Toda and Yamamoto, 1995). At the global level, the findings tend to confirm the widely-held view of the leadership of the US over both the short- and long-term and the existence of a significant short- and long-term relationship between the established OECD and the emerging Asian markets. At the regional level in Southeast Asia, the results tend to confirm, as expected, the leading role of Hong Kong. And consistent with the `contagion effect' hypothesis, the results tend to lend strong support to the view that the stock market fluctuations in all these Asian markets are explained mostly by their regional markets (rather than the advanced markets). Finally, at a methodological level, this analysis also provides a primer for the wealth of applied financial econometric research focusing on dynamic causal inference which involves systems containing possibly integrated and cointegrated processes.

International r&d spillovers: an application of estimation and inference in panel cointegration

Abstract: In this paper, we consider the application of recent results on the estimation and inference in panel cointegration to the study of empirical economic growth. The emergence of endogenous growth theory in the 1980s has led to a resurgence of interest in the sources of economic growth. Coe and Helpman (1995), among other researchers, state that commercially oriented innovation efforts which respond to economic incentives are the major engine of technological progress and productivity growth. Coe and Helpman argue that, in a global economy, a country’s productivity depends on its own R&D efforts as well as the R&D efforts of its trading partners. Using data from 21 OECD countries plus Israel during 1971-1990, they find that both domestic and foreign R&D capital stocks have important effects on total factor productivity (TFP). We intend to re-examine the econometric foundation of Coe and Helpman’s paper. Coe and Helpman (1995) discovered that all of their data exhibit a clear trend, and unit root tests on these data indicate that the TFP and both the domestic and foreign R&D capital stocks are non-stationary. They then confirm the presence of cointegration for TFP and the domestic and foreign R&D capital stocks by testing for a unit root in the residuals. In other words, although all the variables are individually non-stationary, there exists a linear combination of these variables so that the regression containing these variables has a stationary error term. Coe and Helpman’s use of a cointegrating regression enables us to exploit the relationship among the variables in levels, without transforming the data, such as differencing, to avoid the spurious regression problem. Unfortunately, at the time of their article the econometrics of panel cointegra

Asymptotics for nonlinear transformations of integrated time series

Abstract: An asymptotic theory for stochastic processes generated from nonlinear transformations of nonstationary integrated time series is developed. Various nonlinear functions of integrated series such as ARIMA time series are studied, and the asymptotic distributions of sample moments of such functions are obtained and analyzed. The transformations considered in the paper include a variety of functions that are used in practical nonlinear statistical analysis. It is shown that their asymptotic theory is quite different from that of integrated processes and stationary time series. When the transformation function is exponentially explosive, for instance, the convergence rate of sample functions is path dependent. In particular, the convergence rate depends not only on the size of the sample but also on the realized sample path. Some brief applications of these asymptotics are given to illustrate the effects of nonlinearly transformed integrated processes on regression. The methods developed in the paper are useful in a project of greater scope concerned with the development of a general theory of nonlinear regression for nonstationary time series. Nonstationary time series arising from autoregressive models with roots on the unit circle have been an intensive subject of recent research. The asymptotic behavior of regression statistics based on integrated time series (those for which one or more of the autoregressive roots are unity) has received the most attention, and a fairly complete theory is now available for linear time series regressions. The resulting limit theory forms the basis of much ongoing empirical econometric work, especially on the subject of unit root testing and cointegration model

Cointegration, causality, and forecasting : a festschrift in honour of Clive W.J. Granger

Abstract: Chapter 1: A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series Chapter 2: A Multivariate Time Series Analysis of the Data Revision Process for Industrial Production and the Composite Leading Indicator Chapter 3: Evaluating Density Forecasts: The Survey of Professional Forecasters Chapter 4: Ranking Competing Multi-step Forecasts Chapter 5: The Pervasiveness of Granger Causality in Econometrics Chapter 6: A Class for Tests for Integration and Cointegration Chapter 7: Order Selection in Testing for the Cointegration Rank of a VAR Process Chapter 8: Granger's Representation Theorem and Multicointegration Chapter 9: Dimensionality Effect in Cointegration Analysis Chapter 10: Testing DHSY as a Restricted Conditional Model of a Trivariate Seasonally Integrated System Chapter 11: A Unit Root Test in the Presence of Structural Changes in I(1) and I(0) Models Chapter 12: Investigating Inflation Transmission by Stages of Processing Chapter 13: Price Convergence in the Medium and Long Run: an I(2) Analysis of Six Price Indices Chapter 14: M-testing using Finite and Infinite Dimensional Parameter Estimators Chapter 15: Asymptotic Properties of Some Specification Tests in Linear Models with Integrated Processes Chapter 16: Residual Variance Estimates and Order Determination in Panels of Intercorrelated Autoregressive Time Series Chapter 17: Partial Pooling: a Possible Answer to 'To Pool or not to Pool' Chapter 18: A Simultaneous Binary Choice/Count Model with an Application to Credit Card Approvals Chapter 19: Statistical Properties of the Asymmetric Power ARCH Process Chapter 20: A Long-run and Short-run Component Model of Stock Return Volatility

Global Equilibrium Exchange Rates: Euro, Dollar, 'Ins', 'Outs', and Other Major Currencies in a Panel Cointegration Framework

Abstract: This paper presents a methodology for calculating bilateral equilibrium exchange rates for a panel of currencies in a way that guarantees global consistency. The methodology has three parts: a theoretical model that encompasses the balance of payments and the Balassa-Samuelson approaches to real exchange rate determination; an unobserved components decomposition in a cointegration framework that identifies a time-varying equilibrium real exchange rate; and an algebraic transformation that extracts bilateral equilibrium nominal rates. The results uncover that, by the start of Stage III of the European Economic and Monetary Union (EMU), the euro was significantly undervalued against the dollar and the pound, but overvalued against the yen. The paper also shows that the four major EMU currencies locked their parities with the euro at a rate close to equilibrium.

The Economics of EC Competition Law: Concepts, Application and Measurement

Abstract: Introduction. Part I - Concepts. Effective competition. The assessment of market power. The relevant market. Part II - Application. Article 81. Article 82. Merger control. Part III - Measurement. Introduction to empirical analysis. Introducing multiple regression analysis as a tool of antitrust policy. Estimating elasticities. Price correlation analysis. Shipment and transport costs tests. Price concentration studies. Bidding studies. Granger causality and cointegration tests. Summary. Appendices.

Optimal hedging using cointegration

Abstract: Cointegration is a time-series modelling methodology that has many applications to financial markets. When spreads are mean reverting, prices are cointegrated. Then a multivariate model will provide further insight into the price equilibria and returns causalities within the system. Spot–futures arbitrage, yield–curve modelling, index tracking and spread trading are some of the applications of cointegration that are reviewed in this paper. With the demand for new quantitative approaches to active investment management strategies there is considerable interest in cointegration theory. This paper presents a model of cointegrated international equity portfolios which is currently used for hedging within the European, Asian and Far East countries.

Price and income elasticities of disaggregated import demand: results from UECMs and an application

Abstract: This paper presents an empirical analysis of the demand for maize, milk powder, butter, and rice imports in Cyprus, using annual time series data covering the period 1975–1994. None of these products is produced in Cyprus and all the necessary quantities are imported to meet domestic demand. The primary objective of the paper is to derive long-run price and income elasticities of import demand that can be used to analyse the impact of various policies such as the adoption of the Common Agricultural Policy (CAP) when, and if, the Republic of Cyprus joins the European Union (EU). In so doing the paper takes on board some recent developments in time series econometrics. The cointegration test used (the ‘bounds’ test) is a recent test and is based on the estimation of an unrestricted error-correction model (UECM). Parsimonious models were derived using Hendry's ‘general to specific’ approach. The estimated elasticities were subsequently used to quantify some of the implications for Cyprus had the CAP been ope...

Who Moves the Asia-Pacific Stock Markets—US or Japan? Empirical Evidence Based on the Theory of Cointegration

Abstract: This study examines the recent debacle of the Asian-Pacific stock markets by utilizing the theory of cointegration to investigate which developing markets are moved by the markets of Japan and the United States. The empirical evidence suggests that some countries are dominated by the US, some are dominated by Japan, and the remaining countries are dominated by neither during the time period investigated. The appropriate error correction model is estimated and is used to perform out-of-sample forecasting.

Causality Between Energy Consumption and Economic Growth in India : An Application of Cointegration and Error-Correction Modeling

Abstract: Applying the Johansen cointegration test, this study finds that energy consumption, economic growth, capital and labor are cointegrated However, this study detects no causality from energy consumption to economic growth using Hsiao's version of the Granger causality method with the aid of cointegration and error-correction modelling Interestingly, it is discerned that causality runs from economic growth to energy consumption both in the short run and in the long run and causality flows from capital to economic growth in the short run

Does the stock market predict real activity? Time series evidence from the G-7 countries

Abstract: This paper extends one aspect of the US stock market study of Fama (1990) and Schwert (1990). We examine the relationship between industrial production (IP) growth rates and lagged real stock returns for the G-7 countries using both in-sample cointegration and error-correction models and the out-of-sample forecast-evaluation procedure of Ashley et al. (1980). The cointegration tests show a long-run equilibrium relationship between the log levels of IP and real stock prices, while the error-correction models indicate a correlation between IP growth and lagged real stock returns for all countries except Italy. The out-of-sample tests show that in several sub-periods the US, UK, Japanese, and Canadian stock markets enhance predictions of future IP. ” 1999 Elsevier Science B.V. All rights reserved.

A Revival of the Autoregressive Distributed Lag Model in Estimating Energy Demand Relationships

Abstract: The findings in the recent energy economic literature that energy economic variables are non-stationary, heve led to an implicit or explicit dismissal of the standard autoregressive distributed lag (ARDL) model in estimating energy demand relationships. However, Pesaran and Shin (1997) show that the ARDL model remains valid when the underlying variables are non-stationary, provided the variables are cointegrated. In this paper we use the ARDL approach to estimated a demand relationship for Danish residential energy consumption, and the ARDL estimates are compared to the estimates obtained using cointegration techniques and error-correction models (ECMs). It turns out that both quantitavely and qualitatively, the ARDL approach and the cointegration/ECM approach give very similar results.

Macroeconomic Variables and Stock Prices in Malaysia: An Empirical Analysis

Abstract: The article investigates the dynamic interactions between seven macroeconomic variables and the stock prices for an emerging market, Malaysia, using cointegration and Granger causality tests. The results strongly suggest informational inefficiency in the Malaysian market. The bivariate analysis suggests cointegration between the stock prices and three macroeconomic variables – consumer prices, credit aggregates and official reserves. From bivariate error-correction models, we note the reactions of the stock prices to deviations from the long run equilibrium. These results are further strengthened when we extend the analysis to multivariate settings. We also note some evidence that the stock prices are Granger-caused by changes in the official reserves and exchange rates in the short run.

Causality tests of budget and current account deficits: cross-country comparisons

Abstract: This study attempts to determine the causal relationship between budget and current account deficits as well as the direction of such causality. A selected sample of some developed and developing countries with annual time series data is used and cointegration techniques are applied to bring evidence regarding this important issue. Our results do not support any long-run relationship between the two deficits for developed countries while the data for developing countries do not reject such a relationship. However, our results suggest a causal relationship between the two deficits for most of the sample countries.

Government size and economic growth: evidence from a multivariate cointegration analysis

Abstract: This study uses multivariate cointegration techniques and attempts to model the dynamic interactions between government size and economic growth in a five variable system consisting of the growth rates of GDP, total government spending, investment, exports, and imports. Using data on ten OECD countries the analysis shows: (i) Government size Granger-causes growth in all the countries with some disparities concerning the proportion by which government size contributes to explaining future changes in the growth rates. An innovation shock at the growth rate of government size generates a permanent effect on the growth rate of GDP that, for some countries, reaches from 26% to 60% of the total change in growth: (ii) Government size also Granger-causes investment and international trade and, for some countries government size Granger-causes growth indirectly either through investment or the trade variables; and (iii) In almost all countries, international trade and investment generate permanent effects on growt...

The twin deficits phenomenon: evidence from Greece

Abstract: This paper explores the relationship between budget and trade deficits in a small open economy using annual data. The purpose of the paper is to test empirically the validity and rationale of the Keynesian proposition (conventional view) and the Ricardian equivalence hypothesis. The econometric methodology is based on cointegration analysis, error-correction modelling and Granger trivariate causality. The ECM empirical findings suggest that budget deficit has short- and long-run positive and significant causal effects on trade deficit.