Testing for structural change in the dynamic adjustment model with autoregressive errors

The dynamic CUSUM test for structural change proposed by Kr?mer, Ploberger and Alt (1988) is investigated when the errors are serially correlated in a linear dynamic model. We show that the dynamic CUSUM test can be modified to allow for serial correlation in the disturbance using the same procedure as in Kao and Ross (1995), and that the modified dynamic CUSUM test retains its asymptotic significance levels. Monte Carlo results suggest that the empirical size of the dynamic CUSUM test is highly distorted while the empirical size of the modified dynamic CUSUM test is fairly robust to the change on the degree of autocorrelation. We also find that the power of the modified test essentially depends on the angle between the mean regressors and the structural shift. First version received: April 1997/Final version received: January 1998     

Asymptotic confidence bands for the estimated autocovariance and autocorrelation functions of vector autoregressive models

This paper provides closed-form formulae for computing the asymptotic covariance matrices of the estimated autocovariance and autocorrelation functions of stable VAR models by means of the delta method. These covariance matrices can be used to construct asymptotic confidence bands for the estimated autocovariance and autocorrelation functions to assess the underlying estimation uncertainty. The usefulness of the formulae for empirical work is illustrated by an application to inflation and output gap data for the U.S. economy indicating the existence of a significant short-run Phillips-curve tradeoff.First version received: November 2002/Final version received: September 2003     

Nonlinear regression for unit root models with autoregressive errors

This paper shows that the nonlinear least squares estimator for unit root models has the limiting distribution free of nuisance parameters and is more efficient than the augmented Dickey–Fuller estimator when the sum of coefficients for lagged variables is negative.     

Cumulant instrument estimators for hedge fund return models with errors in variables

We revisit the factors incorporated in asset pricing models following the recent developments in financial markets – i.e., the rise of shadow banking and the change in the transmission channel of monetary policy. We propose two versions of the Fung and Hsieh (2004) hedge fund return model, especially an augmented market model which accounts for the new dynamics of financial markets and the procyclicality of hedge fund returns. We run these models with an innovative Hausman procedure, tackling the measurement errors embedded in the models factor loadings. Our empirical method also allows for confronting the drawbacks of the instruments used to estimate hedge fund asset pricing models.     

Questioning the empirical basis of the environmental Kuznets curve for CO2: New evidence from a panel stationarity test robust to multiple breaks and cross-dependence

This paper investigates the time series properties of per capita CO₂ emissions and per capita GDP levels for a sample of 86 countries over the period 1960-2000. For that purpose, we employ a state-of-the-art panel stationarity test which incorporates multiple shifts in level and slope, thereby controlling for cross-sectional dependence through bootstrap methods. Our analysis renders clear-cut evidence that per capita GDP levels are nonstationary for the world as a whole while per capita CO₂ is found to be regime-wise trend stationary. The analysis of country-groups shows that for Africa and Asia, per capita CO₂ is best described as nonstationary, while per capita GDP appears stationary around a broken trend. In addition, we find evidence of regime-wise trend stationarity in both variables for the country-groups consisting of America, Europe and Oceania. The results of our analysis carry important implications for the statistical modelling of the Environmental Kuznets curve for CO₂, since the differing order of integration in both variables for the world as a whole and for Africa and Asia calls into question the validity of panel cointegration techniques which assume that both variables are nonstationary and cointegrated with one another. Cointegration techniques would not be appropriate either for the case of America, Europe and Oceania which are characterised by per capita GDP and CO₂ emissions being stationary around a broken trend. Similar conclusions are reached when we analyse country-groups based on levels of development. Failure to properly characterise the time series properties of the data by not controlling for an unknown number of structural breaks and for cross-sectional dependence could be responsible for the fragility and lack of robustness surrounding the estimation of environmental Kuznets curves.