Efficient estimators of cointegrating vectors are presented for systems involving deterministic components and variables of differing, higher orders of integration. The estimators are computed using GLS or OLS, and Wald Statistics constructed from these estimators have asymptotic x2 distributions. These and previously proposed estimators of cointegrating vectors are used to study long-run U.S. money (Ml) demand. Ml demand is found to be stable over 1900-1989; the 95 % confidence intervals for the income elasticity and interest rate semielasticity are (.88,1.06) and (-.13,-.08), respectively. Estimates based on the postwar data alone, however, are unstable, with variances which indicate substantial sampling uncertainty.

This paper examines the behavior of forecasts made from a co-integrated system as introduced by

Are business cycles mainly the result of permanent shocks to productivity? This paper uses a long-run restriction implied by a large class of real-business-cycle models-identifying permanent productivity shocks as shocks to the common stochastic trend in output, consumption, and investment-to provide new evidence on this question. Econometric tests indicate that this common-stochastic-trend / cointegration implication is consistent with postwar U.S. data. However, in systems with nominal variables, the estimates of this common stochastic trend indicate that permanent productivity shocks typically explain less than half of the business-cycle variability in output, consumption, and investment. (JEL E32, C32) A central, surprising, and controversial result of some current research on real business cycles is the claim that a common stochastic trend-the cumulative effect of permanent shocks to productivity-underlies the bulk of economic fluctuations. If confirmed, this finding would imply that many other forces have been relatively unimportant over historical business cycles, including the monetary and fiscal policy shocks stressed in traditional macroeconomic analysis. This paper shows that the hypothesis of a common stochastic productivity trend has a set of econometric implications that allows us to test for its presence, measure its importance, and extract estimates of its realized value. Applying these procedures to consumption, investment, and output for the postwar United States, we find results that both support and contradict this claim in the real-businesscycle literature. The U.S. data are consis-

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Both textbook economics and common sense teach us that the value of household wealth should be related to consumer spending. Early academic work by Franco Modigliani (1971) suggested that a dollar increase in wealth (holding � xed labor income) leads to an increase in consumer spending of about � ve cents. Since then, the so-called “wealth effect ” on consumption has increasingly crept into both mainstream and policy discussions of the macroeconomy. 1 Today, it is commonly presumed that signi �-cant movements in wealth will be associated with movements in consumer spending, either contemporaneously or subsequently. Quantitative estimates of roughly the magnitude reported by Modigliani are routinely cited in

This paper develops a new methodology that makes use of the factor structure of large dimensional panels to understand the nature of non-stationarity in the data. We refer to it as PANIC – a ‘Panel Analysis of Non-stationarity in Idiosyncratic and Common components’. PANIC consists of univariate and panel tests with a number of novel features. It can detect whether the nonstationarity is pervasive, or variable-specific, or both. It tests the components of the data instead of the observed series. Inference is therefore more accurate when the components have different orders of integration. PANIC also permits the construction of valid panel tests even when cross-section correlation invalidates pooling of statistics constructed using the observed data. The key to PANIC is consistent estimation of the components even when the regressions are individually spurious. We provide a rigorous theory for estimation and inference. In Monte Carlo simulations, the tests have very good size and power. PANIC is applied to a panel of inflation series.

Abstract—This paper shows that yields to maturity of U.S. Treasury bills are cointegrated, and that during periods when the Federal Reserve specifically targeted short-term interest rates, the spreads between yields of different maturity define the cointegrating vectors. This cointegrating relationship im-plies that a single non-stationary common factor underlies the time series behavior of each yield to maturity and that risk premia are stationary. An error correction model which uses spreads as Ihe error correction terms is unstable over the Federal Reserve's policy regime changes, but a model using post 1982 data is stable and is shown to be useful for forecast-ing changes in yields. I.

A dynamic factor model with regime switching is proposed as an empirical characterization of business cycles. The approach integrates the idea of comovements among macroeconomic variables and asymmetries of business cycle expansions and contractions. The first is captured with an unobservable dynamic factor and the second by allowing the factor to switch regimes. The model is estimated by maximizing its likelihood function and the empirical results indicate that the combination of these two features leads to a successful representation of the data relative to extant literature. This holds for within and out-of-sample and for both revised and real time data.

Abstract I show that when house prices are high relative to rents (that is, when the rent-price ratio is low) changes in real rents tend to be larger than usual and changes in real prices tend to be smaller than usual. Standard error-correction models provide inconclusive results about the predictive power of the rent-price ratio at a quarterly frequency. I use a long-horizon regression approach to show that the rent-price ratio helps predict changes in real rents and real prices over three-year periods. This result withstands the inclusion of a measure of the user cost of capital. I show that a longhorizon regression approach can yield biased estimates of the degree of error correction if prices have a unit root but do not follow a random walk. I construct bootstrap distributions to conduct appropriate inference in the presence of this bias. The results lend empirical support to the view that the rent-price ratio is an indicator of valuation in the housing market.

Abstract not found