Semiparametric Fractional Cointegration Analysis

Fractional cointegration is viewed from a semiparametric viewpoint as a narrow-band phenomenon at frequency zero. We study a narrow-band frequency domain least squares estimate of the cointegrating vector, and related semiparametric methods of inference for testing the memory of observables and the presence of fractional cointegration. These procedures are employed in analysing empirical macroeconomic series; their usefulness and feasibility in finite samples is supported by results of a Monte Carlo experiment.Semiparametric analysis, fractional cointegration.

Cointegration Analysis with State Space Models

This paper presents and exemplifies results developed for cointegration analysis with state space models by Bauer and Wagner in a series of papers. Unit root processes, cointegration and polynomial cointegration are defined. Based upon these definitions the major part of the paper discusses how state space models, which are equivalent to VARMA models, can be fruitfully employed for cointegration analysis. By means of detailing the cases most relevant for empirical applications, the I(1), MFI(1) and I(2) cases, a canonical representation is developed and thereafter some available statistical results are briefly mentioned.State space models, unit roots, cointegration, polynomial cointegration, pseudo maximum likelihood estimation, subspace algorithms

Cointegration Analysis-Causality Testing and Wagner's Law The Case of Turkey, 1950-1990

This paper investigates statistically the existence of a long-run relationship between public expenditure and GNP (Wagner’s Law) using data for Turkey over the period 1950-1990. Recent advances in time series analysis have permitted the investigation of the long-run relationship between public expenditure and GNP in terms of cointegration analysis. In the case of Wagner’s Law, evidence of cointegration is sufficient to establish a long-run relationship between public expenditure and income. However, to support Wagner’s Law would require unidirectional causality from income to public expenditure. Therefore cointegration should be seen as a necessary condition for Wagner’s Law, but not sufficient. Hence, conditional on cointegration results, it is necessary to look at the causality properties of the model(s). Using the Engle and Granger cointegration test, the Granger Causality test and Turkish time series aggregate data for the period 1950-1990, we find no empirical support for Wagner’s Law.Wagner's Law; Public Expenditure Growth; Unit Root Test; Cointegration Analysis; Causality

Dynamic cointegration and relevant vector machine: the relationship between gold and silver

We use the Relevant Vector Machine, a technique of supervised learning introduced by Tipping (2001), to conduct a dynamic cointegration analysis on the time series of the price of gold and silver over the period 1971-2004. Unlike the results of traditional cointegration analysis, this study reveals that there is a dynamic long run relationship over the whole periodDynamic cointegration, relevant vector machine

Testing the Market Integration in Regional Cantaloupe and Melon Markets between the U.S. and Mexico: An Application of Error Correction Model

Examine the integration between U.S. and Mexican cantaloupe and watermelon prices using cointegration and error correction model approach. Cointegration analysis shows significant post-2002 improvement in market integration, particularly in the speed at which the market adjusts to departures from its long-run equilibrium.cointegration, error correction model, Johansen test, market cointegration., Marketing,

A meta analytic approach to testing for panel cointegration

We propose new tests for panel cointegration by extending the panel unit root of Choi [2001] and Maddala and Wu [1999] to the panel cointegration case. The tests are flexible, intuitively appealing and relatively easy to compute. We investigate the finite sample behavior in a simulation study. Several variants of the tests compare favorably in terms of both size and power with other widely used panel cointegration tests. --panel cointegration tests,Monte Carlo study,meta analysis

Non-Cointegration and Econometric Evaluation of Models of Regional Shift and Share

This paper tests for cointegration between regional output of an industry and national output of the same industry. An equilibrium economic theory is presented to argue for the plausibility of cointegration, however, regional economic forecasting using the shift and share framework often acts as if cointegration does not exist. Data analysis on broad industrial sectors for 20 states finds very little evidence for cointegration. Forecasting models with and without imposing cointegration are than constructed and used to forecast out of sample. The simplest, non-cointegrating models are the best.

Tests for cointegration in panels with regime shifts

In the paper we extend Gregory and Hansen’s (1996)ADF, Za, Zt cointegration tests to panel data, using the method proposed in Maddala and Wu (1999). We test the null hypothesis of no cointegration for all the units in the panel against the alternative hypothesis of cointegration, while allowing for a one-time regime shift of unknown timing for at least some regressions. We derive the panel tests for the ADF, Za, Zt tests , and compare these tests with Pedroni’s (1999) panel cointegration tests. We show that Gregory and Hansen’s (1996) panel tests have higher power to reject null when there is a structural change in the cointegration vector. We apply the statistics to the analysis of the well known Feldstein-Horioka puzzle for a sample of sixteen OCDE countries. After we allow for a structural break in the cointegration regression, we find strong evidence of cointegration between saving and investment rates.Panel data, Panel cointegration tests, Structural breaks, Feldstein-Horioka puzzle

A Comparison of Cointegration & Tracking Error Models for Mutual Funds & Hedge Funds

We present a detailed study of portfolio optimisation based on cointegration, a statistical tool that here exploits a long-run equilibrium relationship between stock prices and an index price. We compare the theoretical and empirical properties of cointegration optimal equity portfolios with those of portfolios optimised on the tracking error variance. From an eleven year out of sample performance analysis we find that for simple index tracking the additional feature of cointegration between the tracking portfolio and the index has no clear advantages or disadvantages relative to the tracking error variance (TEV) minimization model. However ensuring a cointegration relationship does pay off when the tracking task becomes more difficult. Cointegration optimal portfolios clearly dominate the TEV equivalents for all of the statistical arbitrage strategies based on enhanced indexation, in all market circumstancescointegration, tracking error, index tracking, statistical arbitrage