Cointegration

About: Cointegration is a research topic. Over the lifetime, 17130 publications have been published within this topic receiving 506215 citations.

Exchange Rate Volatility and Cointegration in Tourism Demand

Abstract: This study investigates the long-run demand for Australian outbound leisure tourism during the period 1983 (quarter 1) to 1997 (quarter 4) for nine major tourism destinations. The study is unique in an international context by using exchange rate volatility as an explanatory variable, while it is unique in an Australian context by using a composite substitute price variable. The estimation and hypothesis-testing processes are undertaken using both the Johansen and Engle and Granger procedures. The variance of the exchange rate was found to be a significant determinant of long-run tourism demand in 50% of estimates. Real disposable income and substitute prices were found to have inelastic long-run effects on tourism, while the long-run relative price elasticity tended to differ widely across countries. Indonesia was the only country to find that the exchange rate has a significantly different impact on tourism than relative prices.

Linking Information Communication Technology, trade globalization index, and CO2 emissions: evidence from advanced panel techniques

Abstract: Previous studies consider ICT a two-edged sword that can harm or benefit the environment. In recent years, ICT penetration has considerably increased in the ASEAN-6 countries and the leaders of ASEAN are willing to bring a digital revolution by increasing ICT infrastructure and reducing trade barriers in the region under the Master Plan of ASEAN Connectivity-2025. Hence, this paper explores the effect of ICT and the recently developed trade globalization index on CO2 emissions in ASEAN-6 countries. The study relies on advanced panel econometric approaches, including Westerlund (2007, 2008) and Pedroni cointegration tests, CUP-FM long-run method, and panel DH causality approach. The results suggest cointegration among variables. The results of CUP-FM indicate that ICT contributes to improving environmental quality by mitigating CO2 emissions. Similarly, trade globalization is also sustainable in the region as it reduces emissions. The results are also confirmed by using the CUP-BC estimator. The findings from the DH causality test unfold causality from ICT and trade globalization index to CO2 emissions. Besides, the long-run estimates reveal the detrimental effect of energy consumption on emissions and the U-shaped association between GDP and emissions. Moreover, unidirectional causality from ICT to trade globalization index and energy consumption indicates that ICT influences trade globalization and energy consumption. Finally, environmental policies in the context of ASEAN are extensively discussed.

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.

Modeling Volatility Dynamics

Abstract: Good macroeconomic and financial theorists, like all good theorists, want to get the facts straight before theorizing; hence, the explosive growth in the methodology and application of time-series econometrics in the last twenty-five years. Many factors fueled that growth, ranging from important developments in related fields (see Box and Jenkins, 1970) to dissatisfaction with the “incredible identifying restrictions” associated with traditional macroeconometric models (Sims, 1980) and the associated recognition that many tasks of interest, such as forecasting, simply do not require a structural model (see Granger and Newbold, 1979). A short list of active subfields includes vector autoregressions, index and dynamic factor models, causality, integration and persistence, cointegration, seasonality, unobserved-components models, state-space representations and the Kalman filter, regime-switching models, nonlinear dynamics, and optimal nonlinear filtering. Any such list must also include models of volatility dynamics. Models of autoregressive conditional heteroskedasticity (ARCH), in particular, provide parsimonious approximations to volatility dynamics and have found wide use in macroeconomics and finance1. The family of ARCH models is the subject of this chapter.