Research Database

This paper describes a method developed to predict the advance (first) estimate of inflation-adjusted gross national product (real GNP) using hours-worked data. Besides generating fairly accurate forecasts of advance GNP, the method has two implications. First, the Commerce Department seems to weigh the hours-worked data most heavily in its early estimates of real GNP but less and less so in its revised estimates. Second, analysts attempting to predict current-quarter outcomes in real time need to consider the availability and reliability of data at the time the forecasts are made.

In this paper I develop a practical extension of the Method of Simulated Moments (MSM) estimator for limited dependent variable models to the panel data case. The method is based on a factorization of the MSM first order condition into transition probabilities, along with the development of a new highly accurate method for simulating these transition probabilities. A series of Monte-Carlo tests show that this MSM estimator performs quite well relative to quadrature-based ML estimators, even when large numbers of quadrature points are employed. The estimator also performs well relative to simulated ML, even when a highly accurate method is used to simulate the choice probabilities. In terms of computational speed, complex panel data models involving random effects and ARMA errors may be estimated via MSM in times similar to those necessary for estimation of simple random effects models via ML-quadrature.

This paper studies the accuracy of two versions of the procedure proposed by Kydland and Prescott (1980, 1982) for approximating the optional decision rules in problems in which the objective fails to be quadratic and the constraints linear. The analysis is carried out in the context of a particular example: a version of the Brock-Mirman (1972) model of optimal economic growth. Although the model is not linear quadratic, its solution can nevertheless be computed with arbitrary accuracy using a variant of the value function iteration procedures described in Bertsekas (1976). I find that the Kydland-Prescott approximate decision rules are very similar to those implied by value function iteration.

The paper discusses a new, fully recursive approach to the adaptive modeling, forecasting and seasonal adjustment of nonstationary economic time-series. The procedure is based around a time variable parameter (TVP) version of the well known “component” or “structural” model. It employs a novel method of sequential spectral decomposition (SSD), based on recursive state-space smoothing, to decompose the series into a number of quasi-orthogonal components. This SSD procedure can be considered as a complete approach to the problem of model identification and estimation, or it can be used as a first step in maximum likelihood estimation. Finally, the paper illustrates the overall adaptive approach by considering a practical example of a UK unemployment series which exhibits marked nonstationarity caused by various economic factors.

Leamer (1983) suggested to study the range of estimators β˅0 in the model y=Xβ + δ when imposing linear constraints of the form M (Cβ - c) = 0 where only C and c are fixed. However the extremes may come from models with a bad R^2, say. In this paper we give the exact bounds when only considering models with R^2 ≥ (1 - δ) R^2 max + δR^2 min. These exact bounds can be found from calculating only two regressions. We apply our techniques to study the velocity of money.