David L. Edgerton

Testing autocorrelation in a system perspective testing autocorrelation

The Breusch-Godfrey test for autocorrelated errors is generalised to cover systems of equations, and the properties of 18 versions of the test are studied using Monte Carlo methods. We show that only one group of tests regularly has actual size close to the nominal size; namely the likelihood ratio tests of the auxiliary regression system that are corrected in some manner for degrees-of-freedom. The Rao Ftest exhibits the best performance, whilst the commonly used TR2 test behaves badly even in single equations. However, the size and power properties of all tests deteriorate sharply as the number of equations increases, the system becomes more dynamic, the exogenous variables become more autocorrelated and the sample size decreases. This performance has, in general, an unknown degree since the interaction amongst these factors does not permit a predictive summary, as might be hoped for by response surface-type approaches.

New Improved Tests for Cointegration with Structural Breaks

This article proposes Lagrange multiplier-based tests for the null hypothesis of no cointegration. The tests are general enough to allow for heteroskedastic and serially correlated errors, deterministic trends, and a structural break of unknown timing in both the intercept and slope. The limiting distributions of the test statistics are derived, and are found to be invariant not only with respect to the trend and structural break, but also with respect to the regressors. A small Monte Carlo study is also conducted to investigate the small-sample properties of the tests. The results reveal that the tests have small size distortions and good power relative to other tests.

The small sample properties of the reset test as applied to systems of equations

The RESET test for functional misspecification is generalised to cover systems of equations, and the properties of 7 versions are studied using Monte Carlo methods. The Rao F -test clearly exhibits the best performance as regards correct size, whilst the commonly used LRT (uncorrected for degrees-of-freedom), and LM and Wald tests (both corrected and uncorrected) behave badly even in single equations. The Rao test exhibits correct size even in ten equation systems, which is better than previous research concerning autocorrelation tests. The power of the test is low, however, when the number of equations grows and the correlation between the omitted variables and the RESET proxies is small.

Should stochastic or non-stochastic exogenous variables be used in Monte Carlo experiments?

Abstract Most Monte Carlo studies of test properties use the same values of the exogenous variables in all replications. We show that a considerable improvement in precision can be obtained by drawing separate samples at each replication.

A Note On The Optimal Level Of Monetary Aggregation In The United Kingdom

Weak separability is a key admissibility property in the Divisia approach to monetary aggregation. We test groups of U.K. household sector monetary assets for weak separability using new data underlying the Bank of Englands benchmark revision of its household sector Divisia index. Nonparametric tests are used to identify four monetary asset groupings, which are weakly separable over all or almost all of the post-ERM period (1992:4-2005:1). We construct Divisia monetary aggregates for these four groupings and investigate their information content in two applications. The main findings are that Divisia money has direct effects on aggregate demand and that the growth rates of the nominal Divisia monetary aggregates Granger cause nominal output growth, but not inflation. (Less)

Testing Utility Maximization with Measurement Errors in the Data

Revealed preference axioms provide a simple way of testing data from consumers or firms for consistency with optimizing behavior. The resulting non-parametric tests are very attractive, since they do not require any ad hoc functional form assumptions. A weakness of such tests, however, is that they are non-stochastic. In this paper, we provide a detailed analysis of two non-parametric approaches that can be used to derive statistical tests for utility maximization, which account for random measurement errors in the observed data. These same approaches can also be used to derive tests for separability of the utility function.

Non-Parametric Tests of the Necessary and Sufficient Conditions for Separability

We survey the current state of the art in the use of nonparametric methods to test separability. We focus on three tests: Swofford and Whitney’s (1994) joint test of the necessary and sufficient conditions for weak separability; Fleissig and Whitney’s (2003) sequential test of those conditions; and Jones and Stracca’s (2006) test of the necessary and sufficient conditions for additive separability. We illustrate the latter two tests by applying them to data generated from Barnett and Choi’s (1989) WS-branch model. The empirical results show that these two tests are able to correctly identify separable structure in nearly all

Consumer Demand Theory

The purpose of this chapter is to present the theory underlying our empirical work on the demand for food, and our presentation emphasises those results that are used in the applications. It is based on Deaton and Muellbauer (1980a), Phlips (1983), Deaton (1986) and Varian (1992). The theory is also discussed within the food demand framework by, for example, Johnson et al. (1986) and Capps and Havlicek (1987).

The Specification and Estimation of Demand Systems

In the previous chapter we discussed some of the principal notions behind the theory of consumer demand. In this chapter we consider how to make this theory operational.

The Consumption of Food in the Nordic Countries

In this chapter we shall describe the development of food consumption in Denmark, Finland, Norway and Sweden. The descriptive part of our analysis aims at getting a feeling for the trends and general development of food consumption and relative prices, as well as being a means of detecting peculiarities in the data.