Heino Bohn Nielsen

The Likelihood Ratio Test For Cointegration Ranks In The I(2) Model

This paper presents the likelihood ratio (LR) test for the number of cointegrating relations in the I(2) vector autoregressive model. It is shown that the asymptotic distribution of the LR test for the cointegration ranks is identical to the asymptotic distribution of the much applied test statistic based on the two-step estimation procedure in Johansen (1995, Econometric Theory 11, 25–59), Paruolo (1996, Journal of Econometrics 72, 313–356), and Rahbek, Kongsted, and JA¸rgensen (1999, Journal of Econometrics 90, 265–289). By construction the LR test statistic is smaller than the non-LR test statistic from the two-step procedure, and application of the LR test may change rank selection in empirical work. Based on a study of existing empirical applications and related Monte Carlo simulations we conclude that the LR test has much better size properties when compared to the two-step-based test. Overall, we propose use of the LR test for rank determination in I(2) analysis.Discussions with SA¸ren Johansen, Hans Christian Kongsted, and Bent Nielsen are gratefully acknowledged. We also thank the editor and three anonymous referees for very constructive comments that have led to a much improved version of the paper. This research was supported by Danish Social Sciences Research Council grant 2114-04-0001.

An I(2)Cointegration Analysis of Price and Quantity Formation in Danish Manufactured Exports

The long-run and short-run structure of the Danish manufacturing export sector is analyzed within a cointegrated vector autoregressive model. The price variables of the analysis can be characterized as integrated of second order, I(2), but long-run homogeneity seems to cancel the I(2)-trend allowing the analysis of a transformed data set to take place within the cointegrated I(1)-framework. Two long-run relations are found and identified as a demand-relation for Danish exports and a polynomially cointegrated price relation. In the price formation a large weight to foreign prices and an effect from the rate of inflation to the steady-state markup are found. The latter effect is interpreted as an element of caution in the price setting in an inflationary environment. To characterize the short-run behavior of the Danish export-sector a structural representation of the model is developed. Copyright 2002 by Blackwell Publishing Ltd

Unit Root Vector Autoregression with Volatility Induced Stationarity

We propose a discrete-time multivariate model where lagged levels of the process enter both the conditional mean and the conditional variance. This way we allow for the empirically observed persistence in time series such as interest rates, often implying unit-roots, while at the same time maintain stationarity despite such unit-roots. Specifically, the model bridges vector autoregressions and multivariate ARCH models in which residuals are replaced by levels lagged. An empirical illustration using recent US term structure data is given in which the individual interest rates are found to have unit roots, have no finite first-order moments, but remain strictly stationary and ergodic. Moreover, they co-move in the sense that their spread has no unit root. The model thus allows for volatility induced stationarity, and the paper shows conditions under which the multivariate process is strictly stationary and geometrically ergodic. Interestingly, these conditions include the case of unit roots and a reduced rank structure in the conditional mean, known from linear co-integration. Asymptotic theory of the maximum likelihood estimators for a particular structured case (so-called self-exciting) is provided, and it is shown that T-convergence to Gaussian distributions apply despite unit roots as well as absence of finite first and higher order moments. Monte Carlo simulations illustrate the asymptotic theory.

Monetary Policy in the Greenspan Era: A Time Series Analysis of Rules vs. Discretion*

Relationships between the Federal funds rate, unemployment, inflation and the long‐term bond rate are investigated with cointegration techniques. We find a stable long‐term relationship between the Federal funds rate, unemployment and the bond rate. This relationship is interpretable as a policy target because deviations are corrected via the Federal funds rate. Deviations of the actual Federal funds rate from the estimated target give simple indications of discretionary monetary policy, and the larger deviations relate to special episodes outside the current information set. A more traditional Taylor‐type target, where inflation appears instead of the bond rate, does not seem congruent with the data.

An I(2) Cointegration Model with Piecewise Linear Trends: Likelihood Analysis and Application

This paper presents likelihood analysis of the I(2) cointegrated vector autoregression with piecewise linear deterministic terms. Limiting behavior of the maximum likelihood estimators are derived, which is used to further derive the limiting distribution of the likelihood ratio statistic for the cointegration ranks, extending the result for I(2) models with a linear trend in Nielsen and Rahbek (2007) and for I(1) models with piecewise linear trends in Johansen, Mosconi, and Nielsen (2000). The provided asymptotic theory extends also the results in Johansen, Juselius, Frydman, and Goldberg (2009) where asymptotic inference is discussed in detail for one of the cointegration parameters. To illustrate, an empirical analysis of US consumption, income and wealth, 1965 - 2008, is performed, emphasizing the importance of a change in nominal price trends after 1980.

Influential observations in cointegrated VAR models: Danish money demand 1973-2003

This paper suggests a set of simple diagnostic tools for assessing the influence of a patch of κ observations in a cointegrated vector autoregressive model. The diagnostics are based on the leave-κ-out principle ( Bruce and Martin, 1989Journal of the Royal Statistical Society, Series B, 51, 363--424) and the influence is measured by the likelihood displacement ( Cook and Weisberg, 1982Residuals and Influence in Regression. London: Chapman and Hall). An application to Danish money demand 1973--2003 suggests that the observations for real money in 1999 are affected by institutional factors related to the definition of broad money, and that the dynamic adjustment following the international oil-price shock in 1973 is very influential for the long-run parameters. Copyright Royal Economic Society 2007

Bootstrap Testing of Hypotheses on Co-Integration Relations in Vector Autoregressive Models

It is well known that the finite‐sample properties of tests of hypotheses on the co‐integrating vectors in vector autoregressive models can be quite poor, and that current solutions based on Bartlett‐type corrections or bootstrap based on unrestricted parameter estimators are unsatisfactory, in particular in those cases where also asymptotic χ-super-2 tests fail most severely. In this paper, we solve this inference problem by showing the novel result that a bootstrap test where the null hypothesis is imposed on the bootstrap sample is asymptotically valid. That is, not only does it have asymptotically correct size, but, in contrast to what is claimed in existing literature, it is consistent under the alternative. Compared to the theory for bootstrap tests on the co‐integration rank (Cavaliere, Rahbek, and Taylor, 2012), establishing the validity of the bootstrap in the framework of hypotheses on the co‐integrating vectors requires new theoretical developments, including the introduction of multivariate Ornstein–Uhlenbeck processes with random (reduced rank) drift parameters. Finally, as documented by Monte Carlo simulations, the bootstrap test outperforms existing methods.

An I(2) cointegration model with piecewise linear trends

This paper presents likelihood analysis of the I(2) cointegrated vector autoregression which allows for piecewise linear deterministic terms. Limiting behaviour of the maximum likelihood estimators are derived, which is used to further derive the limiting distribution of the likelihood ratio statistic for the cointegration ranks, extending Nielsen and Rahbek. The provided asymptotic theory extends also the results in Johansen et al. where asymptotic inference is discussed in detail for one of the cointegration parameters. An empirical analysis of US consumption, income and wealth, 1965–2008, is performed, emphasizing the importance of a change in nominal price trends after 1980.

The Co-Integrated Vector Autoregression with Errors–in–Variables

The co-integrated vector autoregression is extended to allow variables to be observed with classical measurement errors (ME). For estimation, the model is parametrized as a time invariant state-space form, and an accelerated expectation-maximization algorithm is derived. A simulation study shows that (i) the finite-sample properties of the maximum likelihood (ML) estimates and reduced rank test statistics are excellent (ii) neglected measurement errors will generally distort unit root inference due to a moving average component in the residuals, and (iii) the moving average component may–in principle–be approximated by a long autoregression, but a pure autoregression cannot identify the autoregressive structure of the latent process, and the adjustment coefficients are estimated with a substantial asymptotic bias. An application to the zero-coupon yield-curve is given.

Properties of estimated characteristic roots

Summary: Estimated characteristic roots in stationary autoregressions are shown to give rather noisy information about their population equivalents. This is remarkable given the central role of the characteristic roots in the theory of autoregressive processes. In the asymptotic analysis the problems appear when multiple roots are present as this imply a non-differentiability so the δ-method does not apply, convergence rates are slow, and the asymptotic distribution is non-normal. In finite samples this has a considerable influence on the finite sample distribution unless the roots are far apart. With increasing order of the autoregressions it becomes increasingly difficult to place the roots far apart giving a very noisy signal from the characteristic roots.