Automatic identification and restriction of the cointegration space

We automate the process of finding the cointegration relations in a cointegrated VAR. There is a rigorous separation between the theory part (search directions must be defined, a final model chosen) and the automated search. The decision rules are set in such a way that a theoretical upper limit can be given to the asymptotic size of recovering all overidentifying restrictions. A Monte Carlo study shows that the algorithm works well, but that the properties of the asymptotic tests are rather poor at times. The software (in Matlab) to execute the algorithm is available.

Bartlett corrections in stationary VARs

We derive the Bartlett correction for a simple hypothesis on the regression parameters in a multivariate stationary autoregressive process. Three applications illustrate the use of the correction: the test for absence of autocorrelation of any order, a simple hypothesis on the autoregressive parameters and two tests for weak exogeneity in the cointegrated VAR model. In the first of these tests, the cointegration space is known, in the second it is not. The Bartlett correction performs well in all simulation studies, except in the one of the last test, that is a test for weak exogeneity in the cointegrated VAR with an unknown cointegration space.

Money Demand in the Netherlands

In this paper we discuss money demand in the Netherlands over the period 1979-1999. The model it with a VAR integrated of order two and use full maximumlikelihood for inference of testing. We find a stable money demand function over the period considered. It depends on the short term interest rate available to private investors, not the rate in the money market.

Automatic identification of simultaneous equations models

This paper provides an operational procedure for putting indentifying restrictions on a simultaneous equations models. The algorithm works on the restrictions,not on the parameters, such that the identifying restrictions can be imposed before estimation.Simultaneous equations, identification, restriction, cointegration

Impact factors

In this paper we discuss sensitivity of forecast with respect to the information set considered in prediction; we define a sensitivit measure called impact factor, IF. We calculate this measure in VAR processes integrated of order 0, 1 and 2. For VAR processes this measure is as simple function of the impulse response coefficients. For integrated VAR systems this measure is shown to have a direct interpretation in terms of long-run forecasts. Various applications of this concept are reviewed, including one on the interpretation and effectiveness of economics policies and one on the sensitivity of forecasts with respect to data revisions. A unified approach to inference on the IF is given, showing under what circumstances standard asymptotic inference can be conducted also in systems integrated of order 1 and 2.Forecasting, cointegration, dynamic multipliers, (Generalized) impulse responses, VAR.

Bootstrapping and Bartlett corrections in the cointegrated VAR model

The small sample properties of tests on long-run coefficients in cointegrated systems are still a matter of concern to applied econometricians. We compare the performance of the Bartlett correction, the bootstrap and the fast double bootstrap for tests on cointegration parameters in the maximum likelihood framework. We show by means of a theorical result and simulations that all three procedures should be based on the unrestricted estimate of the cointegration vectors. The fast double bootstrap delivers superior size correction, whereas the Bartlett correction leads to the least loss of power. However all three perform much better than the asymptotic tests and difference between them are small.

Small Sample Improvements in the Statistical Analysis of Seasonally Cointegrated Systems

This paper proposes new iterative reduced-rank regression procedures for seasonal cointegration analysis. The suggested methods are motivated by the idea that modelling the cointegration restrictions jointly at different frequencies may increase efficiency in finite samples. Monte Carlo simulations indicate that the new tests and estimators perform well with respect to already existing statistical procedures.Seasonal Cointegration, Reduced Rank Regression.

Globalization's challenge to pension reform in Western Europe

The following suggests that demographic changes and the creation of a single currency in Europe has compelled greater EU intervention in pension reform. Although, traditionally pension reform has remained the domain of the domestic realm, increased European integration has necessitated lifting the issue of pension reform to the EU level. Capital flows among Eu member states, the economic dependence among members of EMU and the unique institutional structure of the EU has facilitated increased attention at the EU level regarding pension reform. Politically, the EU presents a unique condition since national governments can use Brussels as a scapegoat to implement contested policies such as pension reform and accountability at the EU level is distinct from democratic configurations within member states also facilitating change within a highly contested policy area. Economically, the almost complete economic integration after the introduction of the Euro, means that countries are ever more dependent on policy choices in other Member States: no lOnger are countries able to keep all the benefits of prefunding, like increased investment, within their own borders. This study concludes that both the political and economic importance of the EU and its uniqueness may lead to an important role of Brussels in the context of pension reform.

Stationary preserving and efficiency increasing probability mass transfer made possible

We develop an efficient computational algorithm that produces efficient Markov chain Monte Carlo (MCMC) transition matrices. The first level of efficinecy is measured in terms of the number of operations needed to produce the resulting matrix. The second level of efficiency is evaluaded in terms of the asymptotic variance of the resulting MCMC estimates. The idea is then extended from finite to general state space.Asymptotic efficiency of MCMC estimates, metropolis-hasting, Stationary preserving and efficiency increasing probability mass transfer.