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This paper studies the role of fluctuations in the aggregate consumption–wealth ratio for predicting stock returns. Using U.S. quarterly stock market data, we find that these fluctuations in the consumption–wealth ratio are strong predictors of both real stock returns and excess returns over a Treasury bill rate. We also find that this variable is a better forecaster of future returns at short and intermediate horizons than is the dividend yield, the dividend payout ratio, and several other popular forecasting variables. Why should the consumption–wealth ratio forecast asset returns? We show that a wide class of optimal models of consumer behavior imply that the log consumption–aggregate wealth ~human capital plus asset holdings! ratio summarizes expected returns on aggregate wealth, or the market portfolio. Although this ratio is not observable, we provide assumptions under which its important predictive components for future asset returns may be expressed in terms of observable variables, namely in terms of consumption, asset holdings and labor income. The framework implies that these variables are cointegrated, and

This paper examines the use of autoregressive distributed lag (ARDL) models for the analysis of long-run relations when the underlying variables are I(1). It shows that after appropriate augmentation of the order of the ARDL model, the OLS estimators of the short-run parameters are p T-consistent with the asymptotically singular covariance matrix, and the ARDL-based estimators of the long-run coe¢cients are super-consistent, and valid inferences on the long-run parameters can be made using standard normal asymptotic theory. The paper also examines the relationship between the ARDL procedure and the fully modi…ed OLS approach of Phillips and Hansen to estimation of cointegrating relations, and compares the small sample performance of these two approaches via Monte Carlo experiments. These results provide strong evidence in favour of a rehabilitation of the traditional ARDL approach to time series econometric modelling. The ARDL approach has the additional advantage of yielding consistent estimates of the long-run coe¢cients that are asymptotically normal irrespective of whether the underlying regressors are I(1) or I(0).

: Many explanations of the stylized facts concerning real exchange rate movements focus on monetary shocks, but it is often found empirically that monetary shocks are unimportant. I provide evidence that is contrary to this empirical finding. Using over 100 years of data, I estimate the contribution of various shocks to explaining variation in the real pound-dollar exchange rate. Monetary shocks consist of both monetary base and money multiplier shocks; real shocks include fiscal, productivity, and preference shocks. Estimates of several alternative VAR specifications provide a range for the contribution of the various shocks: from 19 to 60 percent in the short-run for monetary shocks and 4 to 26 percent for fiscal and productivity shocks combined. My modeling strategy and results are compared directly to related work. The results lend empirical support to the convention in recent quantitative general equilibrium modeling of focusing on monetary shocks. Keywords: exchange rates, moneta...

This paper presents and applies some results on the interpretation of cointegrating regressions. The key concept is the irreducible cointegrating (IC) relation, one from which no variable can be omitted without loss of the cointegration property. Extending earlier results, it is shown that under certain circumstances, IC relations are identified structural forms. It is possible, at least in principle, to learn about the structure of simultaneous long-run relations directly from cointegration analyses, in contrast with the well-known fact that no such knowledge can be obtained from the correlations between stationary variables. IC relations can also be estimated by asymptotically mixed Gaussian and median unbiased estimators, permitting standard inference. MINIMAL, an algorithm for extracting the IC subsets of a data set, is applied to variety of artificial and actual data. # 1998 Elsevier Science S.A. All rights reserved. JEL classification: C32 Keywords: Structural; Identification; ...

Standard tests for the cointegrating rank of a vector autoregressive (VAR) process have nonstandard limiting distributions which depend on the characteristics of intercept terms and time trends in the system. In practice these characteristics are often unknown. Therefore modified tests are proposed with limiting distributions which do not depend on the characteristics of deterministic terms under the null hypothesis. One type of tests makes use of lag augmentation, that is, a VAR process of order p + 1 is fitted when the true order is p while the tests are based on the coefficient matrices of the first p lags only. It is shown that Ø 2 limiting distributions are obtained in this way. The price for this simplicity will be reduced power, however. Therefore, we also consider LM (Lagrange multiplier) type tests. These tests are shown to have nonstandard limiting distributions which do not depend on deterministic terms and have better local power than competing LR (likelihood ratio) test...

Data on monetary aggregates are subject to periodic redefinitions, presumably in part to improve their link to measures of output. Money data are also revised on a regular basis. Taking these data imperfections into account, we reassess the evidence on the marginal predictive content of M1 and M2 for real and nominal output. In particular, by first using the latest version of the data that is available, and then using sequences of historical time series that would have been available to forecasters in real-time, we are able to provide a comprehensive assessment of whether money is useful for predicting output. We conclude that the generally significant marginal predictive content of M1 or M2 for output that is found using a recently revised data set is not duplicated in a realtime setting, although M2 is shown to remain useful when 1-year ahead forecasts are constructed using fitted vector autoregressive models.

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this article we review some of these developments. For a given time series X 1 ; . . . ; X n , nonparametric techniques are used to analyze various features of interest. Generally, the idea underlying many of these techniques is that the characteristic of interest is allowed to have a general form which is approximated increasingly precisely with growing sample size. For example, if a process is assumed to be composed of periodic components, a general form of spectral density may be assumed which can be approximated with increasing precision when the sample size gets larger. Similarly, if the autocorrelation structure of a stationary process is of interest the spectral density may be estimated as a summary of the second moment properties. A brief review of this classical method of nonparametric time series analysis is given in Section 2. Because the final objective of many time series analyses is prediction, it is often of interest to study the conditional means, conditional variances or complete conditional densities in some period, given the past of the process. When a point prediction is the final objective, an estimate of some conditional mean may be desired, while the conditional variances are needed if interval forecasts or assessments of future volatility are desired. Moreover, if higher order moments of a series are potentially important, the focus may be on estimating the complete conditional density. In order to analyze the conditional mean nonparametrically one may, for instance, start from a model of the form

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