Predicting How People Play Games: A Simple Dynamic Model of Choice

We use the model developed in Sarin and Vahid (1999, GEB) to explain the experiments reported in Erev and Roth (1998, AER). The model supposes that players maximize subject to their "beliefs" which are non-probabilistic and scalar-valued. They are intended to describe the payoffs the players subjectively assess they will obtain from a strategy. In an earlier paper (Sarin and Vahid (1997) we showed that the model predicted behavior in repeated coordination games remarkably well, and better than equilibrium theory or reinforcement learning models. In this paper we show that the same one-parameter model can also explain behavior in games with a unique mixed strategy Nash equilibrium better than alternative models. Hence, we obtain further support for the simple dynamic model

Statistical Inference on Changes in Income Inequality in Australia

This paper studies the changes in income inequality of individuals in Australia between 1986 and 1999. Individuals are divided into various subgroups along several dimensions, such as region of residence, age, employment status etc. The changes in inequality over time, between and within the various subgroups is studied, and the bootstrap method is used to establish whether these changes are statistically significant

Strategy Similarity and Coordination

This paper introduces similarity among strategies in the payoff assessment model of choice (Sarin and Vahid (1999, GEB)). The assessments of strategies that are more similar to the chosen strategy are updated more similarly to the chosen strategy. We use this model to explain a recent experiment. The coordination game repeatedly played by the experimental subjects had two symmetric, efficient and strict stage game Nash equilibria. In the experiment, the subjects always converged to play one of these equilibria, and converged to this equilibrium remarkably fast. The model we propose converges to choose the same equilibrium, and does so in roughly the same number of repetitions. Statistical tests are performed to distinguish between the choice distributions generated by the model and the observed choice distributions

A complete VARMA modelling methodology based on scalar components

This paper proposes an extension to scalar component methodology for the identification and estimation of VARMA models. The complete methodology determines the exact positions of all free parameters in any VARMA model with a predetermined embedded scalar component structure. This leads to an exactly identified system of equations that is estimated using full information maximum likelihood

Market Architecture and Nonlinear Dynamics of Australian Stock and Future Indices

This paper studies the All Ordinaries Index in Australia, and its futures contract known as the Share Price Index. We use a new form of smooth transition model to account for a variety of nonlinearities caused by transaction costs and other market/data imperfections, and given the recent interest in the effects of market automation on price discovery, we focus on how the nonlinear properties of the basis and returns have changed, now that floor trading in the futures contract has been replaced by electronic trading

Nonlinear Correlograms and Partial Autocorrelograms

This paper proposes neural network based measures of predictability in conditional mean, and then uses them to construct nonlinear analogues to autocorrelograms and partial autocorrelograms. In contrast to other measures of nonlinear dependence that rely on nonparametric estimation of densities or multivariate integration, our autocorrelograms are simple to calculate and appear to work well in relatively small samples

Predicting the Probability of a Recession With Nonlinear Autoregressive Leading Indicator Models

We develop nonlinear leading indicator models for GDP growth, with the interest rate spread and growth in M2 as leading indicators. Since policy makers are typically interested in whether or not a recession is imminent, we evaluate these models according to their ability to predict the probability of a recession. Using data for the United States, we find that conditional on the spread, the marginal contribution of M2 growth in predicting recessions is negligible

The Missing Link: Using the NBER Recession Indicator to Construct Coincident and Leading Indices of Economic Activity

We use the information content in the decisions of the NBER Business Cycle Dating Committee to construct coincident and leading indices of economic activity for the United States. Although several authors have devised sophisticated coincident indices with the ultimate goal of matching NBER recessions, no one has used past information on NBER recessions to construct a coincident index. A second ingredient of our method is that we only use the cyclical part of the coincident series to explain the NBER recession indicator. Specifically, we use canonical correlation analysis to filter out the noisy information contained in the coincident series. Finally, to construct our preferred coincident index of the U.S. business cycle, we take account of measurement error in the commonly used coincident series by using instrumental-variable methods. The resulting index is a simple linear combination of four coincident series that encompasses currently popular coincident indices

The Importance of Common Cyclical Features in VAR Analysis: a Monte-carlo Study

Despite the commonly held belief that aggregate data display short-run comovement, there has been little discussion about the econometric consequences of this feature of the data. We use exhaustive Monte-Carlo simulations to investigate the importance of restrictions implied by common-cyclical features for estimates and forecasts based on vector autoregressive models. First, we show that the "best" empirical model developed without common cycle restrictions need not nest the "best" model developed with those restrictions. This is due to possible differences in the lag-lengths chosen by model selection criteria for the two alternative models. Second, we show that the costs of ignoring common cyclical features in vector autoregressive modelling can be high, both in terms of forecast accuracy and efficient estimation of variance decomposition coefficients. Third, we find that the Hannan-Quinn criterion performs best among model selection criteria in simultaneously selecting the lag-length and rank of vector autoregressions

Nonlinear Autoregressive Leading Indicator Models of Output in G-7 Countries

This paper studies linear and nonlinear autoregressive leading indicator models of business cycles in G7 countries. The models use the spread between short-term and long-term interest rates as leading indicators for GDP, and their success in capturing business cycles is gauged by non-parametric shape tests, and their ability to predict the probability of recession. We find that bivariate nonlinear models of output and the interest rate spread can successfully capture the shape of the business cycle in cases where linear models fail. Also, our nonlinear leading indicator models for USA, Canada and the UK outperform other models of GDP with respect to predicting the probability of recession