Skip to main content

Über dieses Buch

This book contains eleven articles which provide empirical applications as well as theoretical extensions of some of the most exciting recent developments in time-series econometrics. The papers are grouped around three broad themes: (I) the modeling of multivariate times series; (II) the analysis of structural change; (III) seasonality and fractional integration. Since these themes are closely inter-related, several other topics covered are also worth stressing: vector autoregressive (VAR) models, cointegration and error-correction models, nonparametric methods in time series, and fractionally integrated models. Researchers and students interested in macroeconomic and empirical finance will find in this collection a remarkably representative sample of recent work in this area.



New Developments in Time Series Econometrics: An Overview

New Developments in Time Series Econometrics: An Overview

Empirical data in economics are typically non-experimental, especially in finance and macroeconomics where researchers usually rely on time series gathered by official agencies or other investigators. This raises two basic problems for econometric modeling: first, to understand the dynamic structure of such series, both individually (e.g., stationarity and persistence properties) and jointly (dynamic relations between series); second, to use these series in order to identify and assess potential explanatory (“structural”) models. Because such data are non-experimental, so that observations cannot be made independent and optimal experimental designs are not available, modeling and inference often require an exceptional degree of sophistication. Fortunately, in recent years, statistical methods for the analysis of time series have developed considerably and several remarkable innovations have been introduced.
Jean-Marie Dufour, Baldev Raj

Modelling of Multivariate Economic Time Series


Usefulness of Linear Transformations in Multivariate Time-Series Analysis

Much progress has been made in recent years in multivariate time-series analysis. In this paper we summarize some of the methodological developments that are particularly relevant to empirical economics and highlight especially the usefulness of linear transformations in analyzing multivariate time series. The topics considered include vector ARMA models, principal component analysis, scalar component models, canonical correlation analyses, co-integration, and unit-root tests. We illustrate the methods considered by an example using Taiwan’s interest-rate series and provide critiques of these developments.
George C. Tiao, Ruey S. Tsay, Taychang Wang

VAR Modelling and Haavelmo’s Probability Approach to Macroeconomic Modelling

Some recent developments in the macroeconometric analysis of time series are discussed in the light of Haavelmo (1944). Experimental design in econometrics is discussed and related to the case of passive observation. The general ideas are illustrated with a analysis of the long-run and short-run structure in Danish monetary data.
Katarina Juselius

Inference in Expectations Models of the Term Structure: A Non-parametric Approach

Recent research has examined apparent deviations from the expectations theory of the term structure detectable in regression tests, which may be interpreted as efficiency tests. Efficiency is rejected in many studies. Inference is complicated, however, by the non-normality of regression residuals, invalidating standard parametric test procedures. The present paper examines these rejections using robust diagnostic methods and non-parametric tests. We find some evidence against the expectations theory of the term structure in U.S. data, but not in Canadian. We also investigate the possible explanation of a link between forecast error and the yield spread through models of time-variation in the liquidity premium.
Bryan Campbell, John W. Galbraith

Adjustment Costs and Time-To-Build in Factor Demand in the U.S. Manufacturing Industry

In order to explain cyclical behavior of factor demand, the static neoclassical model of the firm has been extended to include either adjustment costs (e.g. Lucas (1967)) or time-to-build considerations as in Kydland and Prescott (1982). This paper presents an intertemporal factor demand model which accounts for adjustment costs and gestation lags. The closed form solution of the model is a highly restricted vector ARMA-process that is estimated using quarterly data for the manufacturing industry in the U.S., 1960–1988.
The main conclusion is that both sources of dynamics of factor demand are identifiable and found to be empirically of importance.
Franz C. Palm, H. M. M. Peeters, G. A. Pfann

Structural Change Analysis


Parameter Constancy in Cointegrating Regressions

This paper proposes an approach to testing for coefficient stability in cointegrating regressions in time series models. The test statistic considered is the one-sided version of the Lagrange Multiplier (LM) test. Its limit distribution is non-standard but is nuisance parameter free and can be represented in terms of a stochastic bridge process which is tied down like a Brownian bridge but relies on a random rather than a deterministic fraction to do so. The approach provides a test of the null hypothesis of cointegration against specific directions of departure from the null; subset coefficient stability tests are also available. A small simulation studies the size and power properties of these tests and an empirical illustration to Australian data on consumption, disposable income, inflation and money is provided.
Carmela E. Quintos, Peter C. B. Phillips

The HUMP-Shaped Behavior of Macroeconomic Fluctuations

We analyze the nature of persistence in macroeconomic fluctuations. The current view is that shocks to macroeconomic variables (in particular real GNP) have effects that endure over an indefinite horizon. This conclusion is drawn from the presence of a unit root in the univariate time series representation. Following Perron (1989), we challenge this assessment arguing that most macroeconomic variables are better construed as stationary fluctuations around a breaking trend function. The trend function is linear in time except for a sudden change in its intercept in 1929 (The Great Crash) and a change in slope after 1973 (following the oil price shock). Using a measure of persistence suggested by Cochrane (1988) we find that shocks have small permanent effects, if any. To analyze the effects of shocks at finite horizon, we select a member of the ARMA(p, q) class applied to the appropriately detrended series. For the majority of the variables analyzed the implied weights of the moving-average representation have the once familiar humped shape.
Pierre Perron

The Sources of the U.S. Money Demand Instability

The structural stability of money demand relations has been the issue of a substantial number of empirical studies. In most studies for the U.S. structural breaks were found in the 1970s and the 1980s. In the present study a money demand function is specified in error-correction-form which involves real M 1, real GNP, the deflator and a short-term interest rate. Using flexible least squares it is shown for the U.S. that the long-run coefficients of M 1, GNP and the interest rate are relatively stable over a period of more than 30 years while the deflator does not enter the relation. The instability of the relation is mainly due to changes in the short-term dynamics.
Helmut Lütkepohl

Seasonality, Cointegration and Fractional Integration


On the (Mis)Specification of Seasonality and its Consequences: An Empirical Investigation with US Data

It is well known that mis-specification of a trend leads to spurious cycles in detrended data (see, e.g., Nelson and Kang (1981)). Seasonal-adjustment procedures make assumptions, either implicitly or explicitly, about roots on the unit circle both at the zero and seasonal frequencies. Consequently, seasonal-adjustment procedures may produce spurious seasonal variation and other statistically undesirable effects. In this paper we document for a large class of widely used US quarterly macroeconomic series the effects of competing seasonal-adjustment procedures on the univariate time-series properties of the adjusted series. We also investigate which procedures are most appropriate given the properties of the data. Overall, we find very significant differences and evidence that several U.S. macroeconomic time series contain a mixture of deterministic and stochastic seasonal components.
Eric Ghysels, Hahn S. Lee, Pierre L. Siklos

Seasonal Cointegration, Common Seasonals, and Forecasting Seasonal Series

Seasonal cointegration generalizes the idea of cointegration to processes with unit roots at frequencies different from 0. Here, “common seasonals,” also a dual notion of common trends, is adopted for the seasonal case. The features are demonstrated in exemplary models for German and U.K. data. An evaluation of the predictive value of accounting for seasonal cointegration shows that seasonal cointegration may be difficult to exploit to improve predictive accuracy even in cases where seasonal non-cointegration is clearly rejected on statistical grounds. The findings from the real-world examples are corroborated by Monte Carlo simulation.
Robert M. Kunst

A Note on Johansen’s Cointegration Procedure when Trends are Present

This note discusses some issues that arise when Johansen’s (1991) framework is used to analyze cointegrating relationships among variables with deterministic linear time trends. We distinguish “stochastic” and “deterministic” cointegration, arguing that stochastic cointegration is sufficient for the existence of an error correction representation and that it is often the hypothesis of interest in empirical applications. We show that Johansen’s (1991) method, which includes only a constant term in the estimated regression system, does not allow for stochastic cointegration. We propose to modify Johansen’s method by including a vector of deterministic linear trends in the estimated model. We present tabulated critical values of the maximal eigenvalue and trace statistics appropriate for this case. We discuss the circumstances under which our modification may be useful.
Pierre Perron, John Y. Campbell

Small Sample Bias in Conditional Sum-of-Squares Estimators of Fractionally Integrated ARMA Models

This paper considers estimation of the parameters for the fractionally integrated class of processes known as ARFIMA. We consider the small sample properties of a conditional sum-of-squares estimator that is asymptotically equivalent to MLE. This estimator has the advantage of being relatively simple and can estimate all the parameters, including the mean, simultaneously. The simulation evidence we present indicates that estimation of the mean can make a considerable difference to the small sample bias and MSE of the other parameter estimates.
Ching-Fan Chung, Richard T. Baillie
Weitere Informationen