Cointegration

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

Causal Relationship Between Domestic Savings and Economic Growth: Evidence From Seven African Countries

Abstract: This paper utilizes cointegration and the vector error-correction model (VECM) to explore the causal relationship between economic growth and growth rate of domestic savings for Congo, Cote d’Ivoire, Ghana, Kenya, South Africa, and Zambia. Specifically, three analyses were undertaken. First, the time series properties of economic growth and domestic savings were ascertained with the help of the augmented Dickey–Fuller unit root procedure. Second, the long-run relationship between economic growth and growth rate of domestic savings was examined in the context of the Johansen and Juselius (1990) framework. Finally, a Granger-causality test was undertaken to determine the direction of causality between economic growth and growth rate of domestic savings. The results indicate one order of integration [I(1)] for each of the series. The results of the cointegration tests suggest that there is a long-run relationship between economic growth and growth rate of savings. The results from the Granger-causality tests indicate that contrary to the conventional wisdom, economic growth prima facie causes growth rate of domestic savings for most of the countries under consideration.

Indexing and Statistical Arbitrage

Abstract: There are two basic methodologies for portfolio optimization: tracking error variance (TEV) minimization (the industry standard for indexing), and a cointegration–optimal strategy (advocated by econometricians). Cointegration is a statistical tool that seeks to exploit a long–run equilibrium relationship between a portfolio and a benchmark, ensuring that the two are connected in the long term. For simple index tracking, the additional feature of cointegration is found to provide no clear advantages or disadvantages over TEV. Both models produce optimal portfolios that outperform a price–weighted benchmark during market crashes, assuming a long enough model calibration period. When tracking becomes more difficult, ensuring a cointegration relationship enhances performance. Cointegration–optimal portfolios dominate TEV equivalents for all the statistical arbitrage strategies based on enhanced indexation in all market circumstances.

MIMIC models, cointegration and error correction: an application to the French shadow economy

Abstract: The analysis of economic loss attributed to the shadow economy has attracted much attention in recent years by both academics and policy makers. Often, multiple indicators multiple causes (MIMIC) models are applied to time series data estimating the size and development of the shadow economy for a particular country. This type of model derives information about the relationship between cause and indicator variables and a latent variable, here the shadow economy, from covariance structures. As most macroeconomic variables do not satisfy stationarity, long run information is lost when employing first differences. Arguably, this shortcoming is rooted in the lack of an appropriate MIMIC model which considers cointegration among variables. This paper develops a MIMIC model which estimates the cointegration equilibrium relationship and the error correction short run dynamics, thereby retaining information for the long run. Using France as our example, we demonstrate that this approach allows researchers to obtain more accurate estimates about the size and development of the shadow economy.