Multicointegration and present value relations

Journal of Applied Econometrics, 1996

Journal of Time Series Analysis, 1994

Abstract. Alternative common factor representations for cointegrated vectors are studied. This is done by embedding them into the dynamic factor model proposed by Peña and Box (Identifying a simplifying structure in time series. J. Am. Statist. Assoc. 82 (1987), 836–43). It is shown that dynamic factor models produce as a particular case the alternative common trend representations for cointegrated variables available in the literature. Furthermore a new normalization is proposed which has the advantage of producing common trend representations with moving-average polynomials and under certain circumstances with uncorrelated shocks.

International Trade, 2005

Using cointegration techniques, we estimate two models that capture the long-term relationship between Spanish prices and agricultural production. The models were estimated over Spanish agricultural data from 1970 to 2000, a period spanning Spain’s implementation of the Common Agricultural Policy in 1986 and the application of the MacSharry Reforms in 1992. The models, as well as plausible counterfactual scenarios constructed to assess the production changes induced by the CAP, lead to three principal results. First, we find that Spanish agricultural output is responsive to agricultural prices. Second, we find that the MacSharry reforms have been instrumental in restraining agricultural production. Third, we find that Spanish agricultural output would have been higher if Spain had not applied the CAP. These results are important and have broad implications. First, they strengthen the position of those reformers both within and outside of Europe that argue for lower price supports as...

Journal of Futures Markets, 1991

he topic of cointegration and related topics of nonstationarity and unit root T econometrics .have been the center of considerable attention in the applied econometric literature over the last several years. A partial listing of articles includes: Engle and Granger (1987), Engle and Yo0 (1987), Granger (1986), Hendry (1986), and . This article explores the application of cointegration techniques to the study of daily futures and cash prices on live cattle.

Oxford Bulletin of Economics and Statistics, 1996

Journal of Applied Econometrics, 1990

This paper investigates the long-run relationships within a set of six quarterly time-series on the Austrian economy by means of cointegration. After analysing the univariate properties, especially with respect to the appropriate seasonaI filter, the maximum-likelihood method proposed by Johansen (1988) is applied to estimate and test the cointegrating relationships. We found three such relations, implying that the system is driven by three independent stochastic time trends. In a next stage we investigate whether the empirically determined cointegrating relationships are compatible with implications derived from the neoclassical growth model with exogenous stochastic technical progress. It is found that the Austrian data strongly reject the propositions that the real interest rate and the log ratios of consumption to output, investment to output, and the real gross wage sum to output are stationary.

The Indian Economic Journal, 2009

The issue of agricultural supply response is a very important one as it has an impact on growth, poverty, and environment. The size of agricultural supply response is expected to improve after removing some of the constraints that farmers were facing before. Though many constraints have been removed from agrarian system and many incentives have been provided to farmers, still the supply response for Indian agriculture is price inelastic. Hence the question "why supply response is price inelastic" becomes relevant. The present study is an attempt to find supply response through cointegration approach and to see if the response has been better at the all India level in comparison to previous studies. Further, it also focuses on the question whether there is difference in the supply response among highly agricultural based, medium agricultural based, and low agricultural based states. The study indicates that aggregate agricultural output elasticity with respect to agricultural TOT is very low and not statistically different from zero.

2009

Earlier studies usually indicate that farmland prices and cash rents are not cointegrated, a finding that seems at odds with the implications of the present value model. The main objective of this study is to explore whether this absence of empirical support for the present value model can be attributed to the restrictiveness of conventional time series methods. I suggest a panel unit root model with two regimes in which the adjustment process may be characterized by the presence of thresholds and discontinuities reflecting the presence of transactions costs and other barriers to adjustment. Using farmland value and cash rents data for 10 agricultural states of the U.S. between 1960 and 2008, empirical findings give modest improvement over the linear unit root process. It is suggested that there might be a bias caused by cross sectional dependence and an inadequate time span of the data.

1996

One of the most cornmonly used and, at the same time. rejected models in finance and macroeconomics is the exact present value model (PVM). where a variable Y t is expressed as the expected value at time t of the sum of discounted future values of another variable ~. This paper generalizes the PVM by making it non-exact (NEPVM) in a simple way. allowing us to study situations with time varying discount factors, transitory deviations from the exact PVM. as well as situations with correlated market returns. The proposed NEPVM satisfies aH the equilibrium conditions the exact PVM does. but at the same time it is more robust in the sense that rejections produced by the standard volatility and cross-equation restriction tests are not enough to reject the NEPVM. The paper presents the new variance bounds and cross-equation restrictions implied by the NEPVM and it shows how to test them. This paper also shows how to discriminate between the exact PVM and the NEPVM by testing for a deeper level of cointegration: multicointegration. The paper finished by analyzing empirically the cases of stock prices and dividens. short-and long-term interest rates. and farmland prices. Although the exact PVM is rejected in the first two examples, as the literature has largely reported, we are unable to reject the NEPVM. This fact, together with the theoretical results contained in the paper, suggests that the pro po sed NEPVM could be compatible with sorne of the empírical findings in the literature.

Austrian Journal of Statistics, 2016

The problem of detecting unit roots in time series data is treated as a problem of multiple decisions instead of a testing problem, as is otherwise common in the econometric and statistical literature. The multiple decision design is based on a distinction between continuous primary and discrete secondary parameters. Four examples for such multiple decision designs are considered: first-and second-order integrated univariate processes; cointegration in a bivariate model; seasonal integration for semester data; seasonal integration for quarterly data. In all cases, restricted optimum decision rules are established based on Monte Carlo simulation. Zusammenfassung: Die Bestimmung von Einheitswurzeln in Zeitreihendaten wird als multiples Entscheidungsproblem behandelt und nicht als Hypothesentest-Problem, wie es sonst in derökonometrischen und statistischen Literaturüblich ist. Der verwendete entscheidungstheoretische Ansatz benützt eine Unterscheidung zwischen stetigen Primärparametern und diskreten Sekundärparametern. Vier Beispiele für die Anwendung des Ansatzes werden im Detail behandelt: univariate Prozesse mit unbekannter Integrationsordnung; Kointegration in bivariaten Modellen; saisonale Integration bei Halbjahresdaten; saisonale Integration bei Quartalsdaten. In allen Fällen werden optimale Entscheidungsregeln mittels Monte-Carlo-Simulation gefunden.

Journal of Applied Mathematics and Decision Sciences, 2002

Under traditional cointegration tests, some eligible I(1) time series systems Xt, that are not cointegrated over a given time period, say (0, T 1 ], sometimes test as cointegrated over sub-periods. That is, the system appears to have a stationary linear structure ξ Xt for certain vector ξ in the period 0 < t ≤ T 1 . Understanding the dynamics between cointegration test power and restricted sample size that causes this inversion of results is a crucial issue when forecasting over extended future time periods. In this paper, we consider non-cointegrated systems that are closely related to collinear systems. We apply a residual based procedure to such systems and establish a criterion for making the decision whether or not Xt can be continuously accepted as I(0) for t > T 1 when Xt was accepted as I(0) for t ≤ T 1 .

The Indian Economic Journal, 2009

The issue of agricultural supply response is a very important one as it has an impact on growth, poverty, and environment. The size of agricultural supply response is expected to improve after removing some of the constraints that farmers were facing before. Though many constraints have been removed from agrarian system and many incentives have been provided to farmers, still the supply response for Indian agriculture is price inelastic. Hence the question "why supply response is price inelastic" becomes relevant. The present study is an attempt to find supply response through cointegration approach and to see if the response has been better at the all India level in comparison to previous studies. Further, it also focuses on the question whether there is difference in the supply response among highly agricultural based, medium agricultural based, and low agricultural based states. The study indicates that aggregate agricultural output elasticity with respect to agricultural TOT is very low and not statistically different from zero.

Advances in Social Sciences Research Journal, 2016

Cointegration is the simplest way of detrending series whose mean, variance as well as autocorrelation functions changes over time due to the presence of unit roots. However, the issue has shifted to the application of the appropriate cointegration technique as various cointegration techniques abound. Hence, this study reviews the issues surrounding the application and interpretation cointegration techniques within the context of Johansen-Juselius multivariate cointegration framework. The study shows that the adoption of the Johansen-Juselius multivariate cointegration technique rests on the pretests for unit roots. The study reveals that Johansen-Juselius multivariate cointegration technique is preferable when dealing with more than two variables that are integrated of the same and as well different order, I(d) .However, the Johansen-Juselius multivariate cointegration technique is robust when dealing with variables of the same order of integration. The number of cointegrating vectors is detected through the two likelihood ratio test statistics (trace and maximum test). Although the major difficulty lies in the identification of the cointegrating vectors where there are multiple cointegrating vectors. In this approach, cointegration is said to be established when there is at least one cointegrating vector. Based on forecast and policy implications, this paper explores the conditions that necessitate the application of the Johansen and Juselius cointegration technique. This is to avoid its wrongful application, which may in turn lead to model misspecification, inconsistent and unrealistic estimates. However, this paper cannot claim to have treated the underlying issues in their greatest details, but have endeavoured to provide sufficient insight into the issues surrounding Johansen and Juselius cointegration technique to practitioners to enable them apply and interpret and also, follow discussions of the issues in some more advanced texts.