Testing for non-causality by using the Autoregressive Metric

A new non-causality test based on the notion of distance between ARMA models is proposed in this paper. The advantage of this test is that it can be used in possible integrated and cointegrated systems, without pre-testing for unit roots and cointegration. The Monte Carlo experiments indicate that the proposed method performs reasonably well in nite samples. The empirical relevance of the test is illustrated via two applications.AR metric, Bootstrap test, Granger non-causality, VAR

A Panel Cointegration study of the long-run relationship between Savings and Investments in the OECD economies, 1970-2007

In this paper we test for the existence of a long-run savings-investments relationship in 18 OECD economies over the period 1970-2007. Although individual modelling provides only very weak support to the hypothesis of a link between savings and investments, this cannot be ruled out as individual time series tests may have low power. We thus construct a new bootstrap test for panel cointegration robust to short- and long-run dependence across units. Thid test provides evidence of a long-run savings-investments relationship in about half of the OECD economies examined. The elasticities are however often smaller than 1, the value expected under no capital movements.Savings, Investments, Feldstein-Horioka puzzle, OECD, Panel Cointegration, Stationary Bootstrap.

A residual-based bootstrap test for panel cointegration

We address the issue of panel cointegration testing in dependent panels, showing by simulations that tests based on the stationary bootstrap deliver good size and power performances even with small time and cross-section sample sizes and allowing for a break at a known date. They can thus be an empirically important alternative to asymptotic methods based on the estimation of common factors. Potential extensions include test for cointegration allowing for a break in the cointegrating coefficients at an unknown date.Panel Cointegration, Stationary Bootstrap, Commmon Factors.

Testing for breaks in cointegrated panels

Stability tests for cointegrating coefficients are known to have very low power with small to medium sample sizes. In this paper we propose to solve this problem by extending the tests to dependent cointegrated panels through the stationary bootstrap. Simulation evidence shows that the proposed panel tests improve considerably on asymptotic tests applied to individual series. As an empirical illustration we examined investment and saving for a panel of 14 European countries over the 1960-2002 period. While the individual stability tests, contrary to expectations and graphical evidence, in almost all cases do not reject the null of stability, the bootstrap panel tests lead to the more plausible conclusion that the long-run relationship between these two variables is likely to have undergone a break.Panel cointegration; stationary bootstrap; parameter stability tests

Models of labour demand with fixed costs of adjustment: a generalised tobit approach

Traditional models of factor demand rely upon convex and symmetric adjustment costs: however, the fortune of this highly restrictive model is due more to analytical convenience than to actual empirical relevance. In this note we first examine the model of employment adjustment under the more realistic hypothesis of fixed costs, show that it can be cast in the form of a Double Censored Random Effect Tobit Model, derive its likelihood function, and finally evaluate the empirical performance of the ML estimators through a Monte Carlo experiment. The performances, although strongly dependent on the degree of censoring, appear promising.

Can you do the wrong thing and still be right? Hypothesis testing in I(2) and near-I(2) cointegrated VARs

In this paper, we investigate the small-sample performance of LR tests on long-run coefficients in the I(2) model; we focus on a comparison between I(2) and near-I(2) data, i.e. I(1) data with a second root very close to unity, and report the results of some Monte Carlo experiments. With near-I(2) data, the finite-sample properties of the tests are (i) similar to those found with genuine I(2) data, (ii) systematically superior to those of the analogous tests constructed in the I(1) model, even if the latter is, in principle, correctly specified and the former is not. Therefore, there seems to be strong support to the idea that, in practice, modelling near-I(2) data using the I(2) model may be a good idea, despite the inherent misspecification

Indirect estimation of Markov switching models with endogenous switching

Markov Switching models have been successfully applied to many economic problems. The most popular version of these models implies that the change in the state is driven by a Markov Chain and that the state is an exogenous discrete unobserved variable. This hypothesis seems to be too restrictive. Earlier literature has often been concerned with endogenous switching, hypothesizing a correlation structure between the observed variable and the unobserved state variable. However, in this case the classical likelihood-based methods provide biased estimators. In this paper we propose a simple “estimation by simulation” procedure, based on indirect inference. Its great advantage is in the treatment of the endogenous switching, which is about the same as for the exogenous switching case, without involving any additional difficulty. A set of Monte Carlo experiments is presented to show the interesting performances of the procedure.Markov switching models; indirect inference; simulation estimation; Monte Carlo

A residual-based bootstrap test for panel cointegration

We address the issue of panel cointegration testing in dependent panels, showing by simulations that tests based on the stationary bootstrap deliver good size and power performances even with small time and cross-section sample sizes and allowing for a break at a known date. They can thus be an empirically important alternative to asymptotic methods based on the estimation of common factors. Potential extensions include test for cointegration allowing for a break in the cointegrating coefficients at an unknown date

Testing for a set of linear restrictions in varma models using autoregressive metric: An application to granger causality test

In this paper we propose a test for a set of linear restrictions in a Vector Autoregressive Moving Average (VARMA) model. This test is based on the autoregressive metric, a notion of distance between two univariate ARMA models, M 0 and M 1 , introduced by Piccolo in 1990. In particular, we show that this set of linear restrictions is equivalent to a null distance d(M 0 , M 1 ) between two given ARMA models. This result provides the logical basis for using d(M 0 , M 1 ) = 0 as a null hypothesis in our test. Some Monte Carlo evidence about the finite sample behavior of our testing procedure is provided and two empirical examples are presented