Cointegration and direct tests of the rational expectations hypothesis

The paper is concerned with direct tests of the rational expectations hypothesis (REH) in the presence of stationary and non-stationary variables. Alternative methods of converting qualitative survey responses into quantitative expectations series are examined. Testing of orthogonality and the issue of generated regressors for models estimated by two step methods are re-evaluated when the variable to be explained is stationary. A methodological approach for testing the REH is provided for models using qualitative response data when there are unit roots and cointegration, and alternative reasons are examined for rejecting the null hypothesis of orthogonality. The usefulness of cointegration analysis for both the probability and regression conversion procedures is also analysed. Cointegration is found to be directly applicable for the probability conversion approach with uniform, normal and logistic distributions of expectations and for the linear regressicn conversion approach. In the light of new techniques, an existing empirical example testing the REH for British manufacturing firms is re-examined and tested over an extended data set.     

On the term structure of interest rates — Empirical results for Germany

This paper analyzes implications of the expectations hypothesis of the term structure using different German money-market rates and bond yields for the period from January 1977 to April 1993. The investigation applies different methodological approaches: the cointegration approach and frequency domain methods. Money market rates are driven by one common trend and spreads of these rates are stationary, whereas for the bond market and the relation between the money market and the bond market significant level relations between interest rates of different maturities do not seem to exist.     

Measurement Errors and Tests for Rationality

The traditional tests for rationality, the regression and volatility tests, have often rejected the hypothesis of rationality for survey data on expectations. It has been argued that these tests are not valid in the presence of unit roots and hence cointegration tests should be applied. The cointegration tests have often failed to reject the hypothesis of rationality. The present article argues that errors in variables affect tests of rationality. We use multiple sources of expectations to correct for the errors-in-variables bias but find that the hypothesis of rationality is rejected even after this correction. The article uses survey data on interest rates, stock prices, and exchange rates.     

Model specification tests against non-nested alternatives

Non-nested hypothesis tests provide a way to test the specification of an econometric model against the evidence provided by one or more non-nested alternatives. This paper surveys the recent literature on non-nested hypothesis testing in the context of regression and related models. Much of the purely statistical 1iterature which has evolved from the fundamental work of Cox (1961, 1962) is discussed briefly or not at all. Instead, emphasis is placed on those techniques which are easy to employ in practice and are likely to be useful to applied workers.     

Model specification tests against non-nested alternatives

Non-nested hypothesis tests provide a way to test the specification of an econometric model against the evidence provided by one or more non-nested alternatives. This paper surveys the recent literature on non-nested hypothesis testing in the context of regression and related models. Much of the purely statistical 1iterature which has evolved from the fundamental work of Cox (1961, 1962) is discussed briefly or not at all. Instead, emphasis is placed on those techniques which are easy to employ in practice and are likely to be useful to applied workers.     

Universal codes as a basis for time series testing

We suggest a new approach to hypothesis testing for ergodic and stationary processes. In contrast to standard methods, the suggested approach gives a possibility to make tests, based on any lossless data compression method even if the distribution law of the codeword lengths is not known. We apply this approach to the following four problems: goodness-of-fit testing (or identity testing), testing for independence, testing of serial independence and homogeneity testing and suggest nonparametric statistical tests for these problems. It is important to note that practically used so-called archivers can be used for suggested testing.     

An Empirical Analysis of Backlog,Inventory, Production,and Price Adjustments: An Application of Recursive Systems of Log-Linear Models

This article presents an empirical analysis of firms' order backlogs, inventories, production, and price adjustments to unanticipated demand shocks. The data are obtained from quarterly INSEE Business Survey Tests on firms' realizations, expectations, and appraisals of some various economic variables. The analysis is based on the formulation and the estimation of a recursive system of conditional log-linear probability models.     

A comparison of alternative unit root tests

In this paper we evaluate the performance of three methods for testing the existence of a unit root in a time series, when the models under consideration in the null hypothesis do not display autocorrelation in the error term. In such cases, simple versions of the Dickey-Fuller test should be used as the most appropriate ones instead of the known augmented Dickey-Fuller or Phillips-Perron tests. Through Monte Carlo simulations we show that, apart from a few cases, testing the existence of a unit root we obtain actual type I error and power very close to their nominal levels. Additionally, when the random walk null hypothesis is true, by gradually increasing the sample size, we observe that p-values for the drift in the unrestricted model fluctuate at low levels with small variance and the Durbin-Watson (DW) statistic is approaching 2 in both the unrestricted and restricted models. If, however, the null hypothesis of a random walk is false, taking a larger sample, the DW statistic in the restricted model starts to deviate from 2 while in the unrestricted model it continues to approach 2. It is also shown that the probability not to reject that the errors are uncorrelated, when they are indeed not correlated, is higher when the DW test is applied at 1% nominal level of significance.     

Estimation and Inference in Two-Step Econometric Models

A commonly used procedure in a wide class of empirical applications is to impute unobserved regressors, such as expectations, from an auxiliary econometric model. This two-step (T-S) procedure fails to account for the fact that imputed regressors are measured with sampling error, so hypothesis tests based on the estimated covariance matrix of the second-step estimator are biased, even in large samples. We present a simple yet general method of calculating asymptotically correct standard errors in T-S models. The procedure may be applied even when joint estimation methods, such as full information maximum likelihood, are inappropriate or computationally infeasible. We present two examples from recent empirical literature in which these corrections have a major impact on hypothesis testing.     

Checks of model adequacy for univariate time series models and their application to econometric relationships

Tests derived from time series analysis play an important role in many empirical studies. These tests are frequently applied to the residuals obtained by fitting an econometric model using some standard estimator. We focus attention here on tests developed for univariate time series models. Various approaches to testing the adequacy of such models are discussed and compared. The validity and sefulness of applying these tests to econometric residuals are then examined and some Monte Carlo evidence is reported.     

Comment on testing for spurious and cointegrated regressions: a wavelet approach

In a recent paper, Leong and Huang [6] proposed a wavelet-correlation-based approach to test for cointegration between two time series. However, correlation and cointegration are two different concepts even when wavelet analysis is used. It is known that statistics based on non-stationary integrated variables have non-standard asymptotic distributions. However, wavelet analysis offsets the integrating order of non-stationary series so that traditional asymptotics on stationary variables suffices to ascertain the statistical properties of wavelet-based statistics. Based on this, this note shows that wavelet correlations cannot be used as a test of cointegration.     

Tests for Parameter Instability in Regressions with 1(1) Processes

This article derives the large-sample distributions of Lagrange multiplier (LM) tests for parameter instability against several alternatives of interest in the context of cointegrated regression models. The fully modified estimator of Phillips and Hansen is extended to cover general models with stochastic and deterministic trends. The test statistics considered include the SupF test of Quandt, as well as the LM tests of Nyblom and of Nabeya and Tanaka. It is found that the asymptotic distributions depend on the nature of the regressor processes—that is, if the regressors are stochastic or deterministic trends. The distributions are noticeably different from the distributions when the data are weakly dependent. It is also found that the lack of cointegration is a special case of the alternative hypothesis considered (an unstable intercept), so the tests proposed here may also be viewed as a test of the null of cointegration against the alternative of no cointegration. The tests are applied to three data sets—an aggregate consumption function, a present value model of stock prices and dividends, and the term structure of interest rates.     

The Bayesian and frequentist approaches to testing a one-sided hypothesis about a multivariate mean

This paper compares the Bayesian and frequentist approaches to testing a one-sided hypothesis about a multivariate mean. First, this paper proposes a simple way to assign a Bayesian posterior probability to one-sided hypotheses about a multivariate mean. The approach is to use (almost) the exact posterior probability under the assumption that the data has multivariate normal distribution, under either a conjugate prior in large samples or under a vague Jeffreys prior. This is also approximately the Bayesian posterior probability of the hypothesis based on a suitably flat Dirichlet process prior over an unknown distribution generating the data. Then, the Bayesian approach and a frequentist approach to testing the one-sided hypothesis are compared, with results that show a major difference between Bayesian reasoning and frequentist reasoning. The Bayesian posterior probability can be substantially smaller than the frequentist p-value. A class of example is given where the Bayesian posterior probability is basically 0, while the frequentist p-value is basically 1. The Bayesian posterior probability in these examples seems to be more reasonable. Other drawbacks of the frequentist p-value as a measure of whether the one-sided hypothesis is true are also discussed.     

Consistent testing for non‐correlation of two cointegrated ARMA time series

A consistent approach to the problem of testing non‐correlation between two univariate infinite‐order autoregressive models was proposed by Hong (1996). His test is based on a weighted sum of squares of residual cross‐correlations, with weights depending on a kernel function. In this paper, the author follows Hong's approach to test non‐correlation of two cointegrated (or partially non‐stationary) ARMA time series. The test of Pham, Roy & Cédras (2003) may be seen as a special case of his approach, as it corresponds to the choice of a truncated uniform kernel. The proposed procedure remains valid for testing non‐correlation between two stationary invertible multivariate ARMA time series. The author derives the asymptotic distribution of his test statistics under the null hypothesis and proves that his procedures are consistent. He also studies the level and power of his proposed tests in finite samples through simulation. Finally, he presents an illustration based on real data.     

Econometric tests of rationality and market efficiency

Many economic theories give rise to restrictions between the future rational expectations of a set of variables. This paper describes how such theories can be tested from vector time series models. Particular attention is given to problems of nonstationarity and the use of the concept of cointegration in the modeling and testing procedure.     

Threshold Cointegration and the PPP Hypothesis

Self-Exciting Threshold Autoregressive (SETAR) models are a non-linear variant of conventional linear Autoregressive (AR) models. One advantage of SETAR models over conventional AR models lies in its flexible nature in dealing with possible asymmetric behaviour of economic variables. The concept of threshold cointegration implies that the Error Correction Mechanism (ECM) at a particular interval is inactive as a result of adjustment costs, and active when deviations from equilibrium exceed certain thresholds. For instance, the presence of adjustment costs can, in many circumstances, justify the fact that economic agents intervene to recalibrate back to a tolerable limit, as in the case when the benefits of adjustment are superior to its costs. We introduce an approach that accounts for potential asymmetry and we investigate the presence of the relative version of the purchasing power parity (PPP) hypothesis for 14 countries. Based on a threshold cointegration adaptation of the unit root test procedure suggested by Caner & Hansen (2001), we find evidence of an asymmetric adjustment for the relative version of PPP for eight pairs of countries.     

Bayesian inference in the triangular cointegration model using a jeffreys prior

This paper presents a strategy for conducting Bayesian inference in the triangular cointegration model. A Jeffreys prior is used to circumvent an identification problem in the parameter region in which there is a near lack of cointegration. Sampling experiments are used to compare the repeated sampling performance of the approach with alternative classical cointegration methods. The Bayesian procedure is applied to testing for substitution between private and public consumption for a range of countries, with posterior estimates produced via Markov Chain Monte Carlo simulators.     

GIL-PELAEZ-ROSÉN TRANSFORMATION OF DATA

Several statistics based on the empirical characteristic function have been proposed for testing the simple goodness-of-fit hypothesis that the data come from a population with a completely specified characteristic function which cannot be inverted in a closed form, the typical example being the class of stable characteristic functions. As an alternative approach, it is pointed out here that the inversion formula of Gil-Pelaez and Rosén, as applied to the data and the hypothetical characteristic function via numerical integration, is the natural replacement of the probability integral transformation in the given situation. The transformed sample is from the uniform (0, l) distribution if and only if the null hypothesis is true, and for testing uniformity on (0,1) the whole arsenal of methods statistics so far produced can be used.     

Cointegration Detection Using Dynamic Factor Models

A method of information-criterion-based cointegration detection using dynamic factor models is proposed. The results of the data-based and non data-based Monte Carlo simulations suggest that this method is as effective as conventional hypothesis-testing methods. In the proposed method, an observed multivariate time series is described in terms of common stochastic trends plus stationary autoregressive cycles. Then the best model is selected from among alternative models obtained by changing the number of common stochastic trends, on the basis of information criteria. Consequently, the cointegration rank is determined on the basis of the selected model. Two advantages of the proposed method are also discussed.     

Lagrance-multiplier tersts for weak exogeneity: a synthesis

This paper unifies two seemingly separate approaches to test weak exogeneity in dynamic regression models with Lagrange-mulptiplier statistics. The first class of tests focuses on the orthogonality between innovations and conditioning variables, and thus is related to the Durbin-Wu-Hausman specification test. The second approach has been developed more recently in the context of context of cointegration and error correction models, ad concentrates on the question whether the conditioning variables display error correction behaviour. It is shown that the vital difference between the two approaches stems from the choice of the parmeters of interest. A new test is derived, which encompasses both its predecessors. The test is applied to an error correction model of the demand for money in Switzerland.