This paper demonstrates how time consistency of the Ramsey policy–the optimal fiscal and monetary policy under commitment–can be achieved. Each government should leave its successor with a unique maturity structure for the nominal and indexed debt, such that the marginal benefit of a surprise inflation exactly balances the marginal cost. Unlike in earlier papers on the topic, the result holds for quite general Ramsey policies, including timevarying polices with positive inflation and positive nominal interest rates. We compare our results with
This study undertakes a systematic analysis of several theoretic and statistical assumption s used in many empirical models of female labor supply. Using a singl e data set (PSID 1975 labor supply data) the author is able to replic ate most of the range of estimated income and substitution effects fo und in previous studies in this field. He undertakes extensive specif ication tests and finds that most of this range should be rejected du e to statistical and model misspecifications. The two most important assumptions appear to be the Tobit assumption used to control for sel f-selection into the labor force and exogeneity assumptions on the wi fe's wage rate and her labor market experience. Copyright 1987 by The Econometric Society.
We derive lower bounds on the asymptotic variances for regular distribution-free estimators of the parameters of the binary choice model and the censored regression (Tobit) model. A distribution-free (or semiparametric) estimator is one that does not require any assumption about the distribution of the stochastic error term in the model, apart from regularity conditions. For the binary choice model, we obtain an explicit lower bound for the asymptotic variance for the slope parameters, or more generally the parameters of a nonlinear regression function in the underlying latent variable model, but we find that there is no regular semiparametric estimator of the constant term (identified by requiring the error distribution to have zero median). Lower bounds are also obtained under the further assumption that the error distribution is symmetric, and in this case there is a finite lower bound for the constant term too. Comparison of the bounds with those for the classical parametric problem shows the loss of information due to lack of a priori knowledge of the functional form of the error distribution. We give the conditions for equality of the parametric and semiparametric lower bounds (in which case adaptive estimation may be possible), both with and without the assumption of a symmetric error distribution. In general, adaptive estimation is not possible, but one special case where these conditions hold is when the regression function is linear and the explanatory variables have a multivariate normal distribution. The Tobit model considered here is the censored nonlinear regression model, with a fixed censoring point. We again give an explicit lower bound for the asymptotic variance for the regression parameters, this time including a constant term (if the error term has zero median). Comparison with the corresponding lower bound for the parametric case shows that adaptive estimation is in general not possible for this model.
The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples. If each element of a vector of time series x first achieves stationarity after differencing, but a linear combination a'x is already stationary, the time series x are said to be co-integrated with co-integrating vector a. There may be several such co-integrating vectors so that a becomes a matrix. Interpreting a'x,= 0 as a long run equilibrium, co-integration implies that deviations from equilibrium are stationary, with finite variance, even though the series themselves are nonstationary and have infinite variance. The paper presents a representation theorem based on Granger (1983), which connects the moving average, autoregressive, and error correction representations for co-integrated systems. A vector autoregression in differenced variables is incompatible with these representations. Estimation of these models is discussed and a simple but asymptotically efficient two-step estimator is proposed. Testing for co-integration combines the problems of unit root tests and tests with parameters unidentified under the null. Seven statistics are formulated and analyzed. The critical values of these statistics are calculated based on a Monte Carlo simulation. Using these critical values, the power properties of the tests are examined and one test procedure is recommended for application. In a series of examples it is found that consumption and income are co-integrated, wages and prices are not, short and long interest rates are, and nominal GNP is co-integrated with M2, but not M1, M3, or aggregate liquid assets.
[Andersen (1970) considered the problem of inference on random effects linear models from binary response panel data. He showed that inference is possible if the disturbances for each panel member are known to be white noise with logistic distribution and if the observed explanatory variables vary over time. A conditional maximum likelihood estimator consistently estimates the model parameters up to scale. The present paper shows that inference remains possible if the disturbances for each panel member are known only to be time-stationary with unbounded support and if the explanatory variables vary enough over time. A conditional version of the maximum score estimator (Manski, 1975, 1985) consistently estimates the model parameters up to scale.]
This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction. It also establishes consistency of the estimated covariance matrix under fairly general conditions.
For every range of admissible incomes, the authors characterize the class of Engel curves with the property that if an economy has, first, a price independent distribution of income and, second, preferences which are identical across consumers and generate Engel curves in the class, then the corresponding aggregate demand function satisfies the Weak Axiom of Revealed Preference. This class is defined by two simple conditions. The no-torsion condition says that, in the relevant range of income, the Engel curve is contained in a plane through the origin. The uniform-curvature condition says that, in addition, the Engel curve is either convex or concave to the origin. Copyright 1987 by The Econometric Society.
This paper extends the simple errors-in-variable bound to the setting of systems of equations. Both diagonal and nondiagonal measurement error covariance matrices are considered. In the nondiagonal case, the analogue of the simple errors-in-variable interval of estimates is an ellipsoid with diagonal equal to the line segment connecting the direct least squares with a two-stage least squares estimate. For the diagonal case, the set of estimates under some conditions must lie within the convex hull of 2k points.