Allan P. Layton

The impact of socio‐economic factors on gambling expenditure

This paper examines the socio‐economic determinants of gambling expenditure on lotteries, Lotto and Instant Lotto, TAB/on‐course betting, poker machines and casino‐type games. Using a sample of 8,389 Australian households in 1993‐1994, the impact of income source and level, sex, age, ethnicity, occupational status and family composition on the decision to gamble is assessed. The results indicate that these variables exert a significant influence on the probability of households gambling. Furthermore, the effect of these same variables is likely to vary across the large range of gambling products currently available.

Dating and predicting phase changes in the U.S. business cycle

Abstract Hamiltons quasi-Bayesian, Markovian, regime-switching model is applied to monthly growth rates of leading, long-leading and coincident indexes of the US economy. A simple rule applied to regime probabilities for each data point of the coincident index produces a phase chronology which exactly reproduces the turning points of the index produced by the Bry-Boschan method. The chronology is also therefore almost identical to the officially recognised US business cycle chronology. To gain some insight into how quickly the last eight business cycle turning points could have been identified, the dating algorithm is applied to the data sequentially, augmenting the sample period one monthly observation at a time. The regime-switching model is also applied to the leading and long-leading indexes. The application of a simple rule to the regime probabilities is found to result in a very reliable advance signalling system for the US business cycle.

Comparison of regime switching, probit and logit models in dating and forecasting US business cycles

Abstract Three non-linear model specifications are tested for their efficacy in dating and forecasting US business cycles, viz. a probit specification, a logit specification — both binomial and multinomial alternatives — and a markov, regime-switching specification. The models employ leading indicators compiled by the Economic Cycle Research Institute as putative explanators. They are tested within sample to determine their relative abilities to produce a business cycle chronology similar to the official NBER chronology. They are also tested in a post-sample context to test their relative abilities in anticipating future turning points with the result that the regime-switching model with time-varying transition probabilities performs the best.

A further test of the influence of leading indicators on the probability of US business cycle phase shifts

Abstract In earlier work Filardo (1994) used a variable transition probability Markov regime-switching model to investigate the usefulness of a number of leading indicators in anticipating phase changes in the business cycle. Filardo used Industrial Production as a proxy for the business cycle and found the leading indicators were not only statistically significant determinants of the transition probabilities but that they also substantially improved the dating of the business cycle. However, Industrial Production is a very narrow proxy for the business cycle and represents a relatively small and decreasing component of economic activity. Here a broader, more comprehensive proxy for the business cycle is employed to test the usefulness of leading indicators in forecasting the likelihood of future business cycle phase shifts. Specifically, the Economic Cycle Research Institutes (ECRI) coincident composite index is employed as a summative measure of the business cycle. Then, following Diebold et al. (1994) , the transition probability parameters are allowed to vary. In particular, the ECRI leading and long leading indexes are used as putative determinants of these transition probabilities.

What is a recession?: A reprise

This paper draws its title from a paper written over 35 years ago by Geoffrey H. Moore (1967). Why the need for a reprise? First, there would appear currently to be somewhat diverging views as to what properly constitutes a recession. Second, largely as a result of this, in many countries other than the US, there does not exist a single, widely accepted business cycle chronology for the country in question. This paper will argue that, in addition to output, there are other important aspects to aggregate economic activity that need to be taken into account in determining the business cycle, viz., income, sales and employment. As such, our perspective would seem to be at odds with the apparent position taken by some other recent commentators on this issue who argue that GDP is all that is needed to represent a countrys business cycle. We will also argue against using the currently popular ‘two negative quarterly growth rate’ rule in dating the onset of a recession.

A test of the cost of carry relationship for the Australian 90 day bank accepted bill futures market

In competitive markets participants in the markets rapidly drive out arbitrage profits. Thus, assuming markets are competitive, cointegration provides one test of arbitrage based pricing models as this test identifies ‘longer-term’ deviations from an equilibrium relationship. In this paper the equilibrium is described by the cost of carry model, an arbitrage relationship between the bank accepted bill price, the bank accepted bill futures price and the interest rate to futures contract maturity. Time series observations are available for 90 day bank accepted bill futures contracts starting with the contract maturing in June 1980 extending through to the contract maturing in March 1990. Unit root tests are conducted on the times series and these identify the underlying variables in the cost of carry relationship as I(1) processes. Cointegration tests are then applied to test for longer-term deviations from the cost of carry relationship. The level of cointegration identified in these series increases in th...

A suggested reconciliation of recent cointegration testing of foreign exchange market spot rates

Cointegration testing by different researchers using different sampling frequencies recently has led them to opposite conclusions regarding the presence of common stochastic trends in foreign exchange markets. Since cointegration implies the existence of an underlying long-term relationship this would seem to be logically inconsistent. It is argued that this is due to the different sampling frequencies used. Specifically, we argue foreign exchange markets display well-known varying degrees of temporally correlated volatility depending upon how frequently the data are sampled and that this may be an important factor in influencing cointegration findings. An empirical analysis of Australian foreign exchange markets is supportive