Comovement in GDP trends and cycles among trading partners

It has long been recognized that business cycle comovement is greater between countries that trade more intensively with one another. However, nations face shocks to both the cyclical and trend components of their GDP series. Contrary to the result for cyclical fluctuations, we find comovement of shocks to the trend component of real GDP is weaker among countries that trade more intensively with one another. We simulate changes in ten-year output growth correlations corresponding to the estimated effects of trade and show that the impact of trade on trend comovement is quantitatively more important than its effect on cyclical comovement.     

YTS and the Labour Market

This article contains results from a survey of the influence of the Youth Training Scheme (YTS) in the labour market. Tighter conditions in this market in 1989 led to changes in the role of the YTS towards recruitment and training rather than job creation. The effectiveness of YTS in meeting skill shortages is examined, as are the motives of employers who participate in the scheme and the extent to which trainees aim for qualifications. Youth training for the labour markets of the 1990s is considered in relation to expected future trends in the demand for skilled labour in that period.     

Differencing of random walks and near random walks

The traditional rationale for differencing time series data is to attain stationarity. For a nearly non-stationary first-order autoregressive process—AR (1) with positive slope parameter near unity—we were led to a complementary rationale. If one suspects near non-stationarity of the AR (1) process, if the sample size is ‘small’ or ‘moderate’, and if good one-step-ahead prediction performance is the goal, then it is wise to difference the data and treat the differences as observations on a stationary AR (1) process. Estimation by Ordinary Least Squares then appears to be at least as satisfactory as nonlinear least squares. Use of differencing for an already stationary process can be motivated by Bayesian concepts: differencing can be viewed as an easy way to incorporate non-diffuse prior judgement—that the process is nearly non-stationary—into one's analysis. Random walks and near random walks are often encountered in economics. Unless one's sample size is large, the same statistical analyses apply to either.     

Errors, robustness, and the fourth quadrant

The paper presents evidence that econometric techniques based on variance–L² norm–are flawed and do not replicate. The result is un-computability of the role of tail events. The paper proposes a methodology to calibrate decisions to the degree (and computability) of forecast error. It classifies decision payoffs in two types: simple (true/false or binary) and complex (higher moments); and randomness into type-1 (thin tails) and type-2 (true fat tails), and shows the errors for the estimation of small probability payoffs for type 2 randomness. The fourth quadrant is where payoffs are complex with type-2 randomness. We propose solutions to mitigate the effect of the fourth quadrant, based on the nature of complex systems.     

Learning and stability of the Bayesian–Walrasian equilibrium

Under the Bayesian–Walrasian Equilibrium (BWE) (see Balder and Yannelis, 2009), agents form price estimates based on their own private information, and in terms of those prices they can formulate estimated budget sets. Then, based on his/her own private information, each agent maximizes interim expected utility subject to his/her own estimated budget set. From the imprecision due to the price estimation it follows that the resulting equilibrium allocation may not clear the markets for every state of nature, i.e., exact feasibility of allocations may not occur. This paper shows that if the economy is repeated from period to period and agents refine their private information by observing the past BWE, then in the limit all agents will obtain the same information and market clearing will be reached. The converse is also true. The analysis provides a new way of looking at the asymmetric equilibrium which has a statistical foundation.     

The Nested PIGLOG Model: An Application to U.S. Food Demand

A new demand system is introduced, the Nested PIGLOG model, nesting thirteen other demand systems including five that are also new. This new model and its nested special cases are applied to models of U.S. food demand that include food-at-home (FAH), food-away-from-home (FAFH), and alcoholic beverages. Although nested tests and out-of-sample forecasting performance favor generalizing models to a certain degree, statistically insignificant improvements to in-sample-fit and even poorer out-of-sample forecast accuracy undermine further generalizations. Based on a subset of preferred models, FAFH is found to be price and income elastic compared to FAH which is price and income inelastic.