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730
A new approach to the economic analysis of nonstationary time series and the business cycle
 ECONOMETRICA
, 1989
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Is public expenditure productive?
 JOURNAL OF MONETARY ECONOMICS
, 1989
"... This paper considers the relationship between aggregate productivity and stock and flow governmentspending variables. The empirical results indicate that (i) the nonmilitary public capital stock is dramatically more important in determining productivity than is either the flow of nonmilitary or mil ..."
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Cited by 902 (2 self)
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This paper considers the relationship between aggregate productivity and stock and flow governmentspending variables. The empirical results indicate that (i) the nonmilitary public capital stock is dramatically more important in determining productivity than is either the flow of nonmilitary or military spending, (ii) military capital bears little relation to productivity, and (iii) a 'core' infrastructure of streets, highways, airports, mass transit, sewers, water systems, etc. has most explanatory power for productivity. The paper also suggests an important role for the net public capital stock in the 'productivity slowdown ' of the last fifteen years.
Testing for Common Trends
 Journal of the American Statistical Association
, 1988
"... Cointegrated multiple time series share at least one common trend. Two tests are developed for the number of common stochastic trends (i.e., for the order of cointegration) in a multiple time series with and without drift. Both tests involve the roots of the ordinary least squares coefficient matrix ..."
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Cited by 453 (7 self)
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Cointegrated multiple time series share at least one common trend. Two tests are developed for the number of common stochastic trends (i.e., for the order of cointegration) in a multiple time series with and without drift. Both tests involve the roots of the ordinary least squares coefficient matrix obtained by regressing the series onto its first lag. Critical values for the tests are tabulated, and their power is examined in a Monte Carlo study. Economic time series are often modeled as having a unit root in their autoregressive representation, or (equivalently) as containing a stochastic trend. But both casual observation and economic theory suggesthat many series might contain the same stochastic trendso that they are cointegrated. If each of n series is integrated of order 1 but can be jointly characterized by k < n stochastic trends, then the vecto representation of these series has k unit roots and n k distinct stationary linear combinations. Our proposed tests can be viewed alternatively as tests of the number of common trends, linearly independent cointegrating vectors, or autoregressive unit roots of the vector process. Both of the proposed tests are asymptotically similar. The firstest (qf) is developed under the assumption that certain components of the process have a finiteorder vector autoregressive (VAR) representation, and the nuisance parameters are handled by estimating this VAR. The second test (q,) entails computing the eigenvalues of a corrected sample firstorder autocorrelation matrix, where the correction is essentially a sum of the autocovariance matrices. Previous researchers have found that U.S. postwar interest rates, taken individually, appear to be integrated of order 1. In addition, the theory of the term structure implies that yields on similar assets of different maturities will be cointegrated. Applying these tests to postwar U.S. data on the federal funds rate and the three and twelvemonth treasury bill rates providesupport for this prediction: The three interest rates appear to be cointegrated.
Does monetary policy matter? A new test
 in the spirit of Friedman and Schwartz. NBER Macroeconomics Annual
, 1989
"... This paper investigates whether nominal disturbances have important real effects. What differentiates the paper from the countless others on the same subject is that it focuses not on purely statistical evidence but on evidence derived from the historical recordevidence based on what ..."
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Cited by 419 (20 self)
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This paper investigates whether nominal disturbances have important real effects. What differentiates the paper from the countless others on the same subject is that it focuses not on purely statistical evidence but on evidence derived from the historical recordevidence based on what
Long memory processes and fractional integration in Econometrics
 JOURNAL OF NOMETRI ELSEVIER JOURNAL OF ECONOMETRICS 73{1996) 5 59
, 1996
"... This paper provides a survey and review of the major econometric work on long memory processes, fractional integration, and their applications in economics and finance. Some of the definitions of long memory are reviewed, together with previous work in other disciplines. Section 3 describes the popu ..."
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Cited by 366 (0 self)
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This paper provides a survey and review of the major econometric work on long memory processes, fractional integration, and their applications in economics and finance. Some of the definitions of long memory are reviewed, together with previous work in other disciplines. Section 3 describes the population characteristics of various long memory processes in the mean, including ARFIMA. Section 4 is concerned with estimation and examines emiparametric procedures in both *he frequency and time domain, and also the properties of various regression based and maximum likelihood techniques. Long memory volatility processes are discussed in Section 5, while Section 6 discusses applications in economics and finance. The paper also has a concluding section.
Common Persistence in Conditional Variances
 ECONOMETRIC REVIEWS
, 1993
"... Since the introduction of the autoregressive conditional heteroskedastic (ARCH) model in Engle (1982), numerous applications of this modeling strategy have already appeared. A common finding in many of these studies with high frequency financial or monetary data concerns the presence of an approxima ..."
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Cited by 327 (20 self)
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Since the introduction of the autoregressive conditional heteroskedastic (ARCH) model in Engle (1982), numerous applications of this modeling strategy have already appeared. A common finding in many of these studies with high frequency financial or monetary data concerns the presence of an approximate unit root in the autoregressive polynomial in the univariate time series representation for the conditional second order moments of the process, as in the socalled integrated generalized ARCH (IGARCH) class of models proposed in Engle and Bollerslev (1986). In the IGARCH models shocks to the conditional variance are persistent, in the sense that they remain important for forecasts of all horizons. This idea is readily extended to a multivariate framework. Even though many time series may exhibit persistence in variance, it is likely that several different variables share the same common longrun component. In that situation, the variables are naturally defined to be copersistent in variance, and the copersistent linear combination is interpretable as a longrun relationship. Conditions for copersistence to occur in the multivariate linear GARCH model are presented. These conditions parallel the conditions for linear cointegration in the mean, as developed by Engle and Granger (1987). The presence of copersistence has important implications for asset pricing relationships and in optimal portfolio allocation decisions. An empirical example relating to the time series properties of nominal U.S. dollar exchange rates for the deutschemark and the British pound provides a simple illustration of the ideas.