Measurement Errors and Tests for Rationality

The traditional tests for rationality, the regression and volatility tests, have often rejected the hypothesis of rationality for survey data on expectations. It has been argued that these tests are not valid in the presence of unit roots and hence cointegration tests should be applied. The cointegration tests have often failed to reject the hypothesis of rationality. The present article argues that errors in variables affect tests of rationality. We use multiple sources of expectations to correct for the errors-in-variables bias but find that the hypothesis of rationality is rejected even after this correction. The article uses survey data on interest rates, stock prices, and exchange rates.     

Modeling Technology as a Dynamic Error Components Process: The Case of the Inter-country Agricultural Production Function

In this paper, we propose a dynamic error-components model to represent the unobserved level of technology. This specification implies a well-defined common factor dynamic model for per capita output that can be tested explicitly. The model is applied to data on aggregates of agricultural inputs and outputs for groups of countries from the OECD, Africa (AF), Latin America (LA) as well as centrally planned countries, over a period of 31 years. We find that the proposed model fits the data better than alternative static specifications and satisfies the implied common factor restrictions in two of the samples. The results suggest that although technological change seems to have been a faster process for less developed countries relative to the OECD countries, it has not been fast enough to reduce appreciably the enormous differences in average technological levels that still persist between them.     

Estimation of Short-Run and Long-Run Elasticities of Energy Demand From Panel Data Using Shrinkage Estimators

This article discusses the problem of obtaining short-run and long-run elasticities of energy demand for each of 49 states in the United States using data for 21 years. Estimation using the time series data by each state gave several wrong signs for the coefficients. Estimation using pooled data was not valid because the hypothesis of homogeneity of the coefficients was rejected. Shrinkage estimators gave more reasonable results. The article presents in a unified framework the classical, empirical Bayes, and Bayes approaches for deriving these estimators.     

Bootstrapping time series models

This paper surveys recent development in bootstrap methods and the modifications needed for their applicability in time series models. The paper discusses some guidelines for empirical researchers in econometric analysis of time series. Different sampling schemes for bootstrap data generation and different forms of bootstrap test statistics are discussed. The paper also discusses the applicability of direct bootstrapping of data in dynamic models and cointegrating regression models. It is argued that bootstrapping residuals is the preferable approach. The bootstrap procedures covered include the recursive bootstrap, the moving block bootstrap and the stationary bootstrap.     

Bootstrapping time series models

This paper surveys recent development in bootstrap methods and the modifications needed for their applicability in time series models. The paper discusses some guidelines for empirical researchers in econometric analysis of time series. Different sampling schemes for bootstrap data generation and different forms of bootstrap test statistics are discussed. The paper also discusses the applicability of direct bootstrapping of data in dynamic models and cointegrating regression models. It is argued that bootstrapping residuals is the preferable approach. The bootstrap procedures covered include the recursive bootstrap, the moving block bootstrap and the stationary bootstrap.