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For the London Stock Exchange we demonstrate that the signs of orders obey a long-memory process. The autocorrelation function decays roughly as a power law with an exponent of 0.6, corresponding to a Hurst exponent H = 0.7. This implies that the signs of future orders are quite predictable from the signs of past orders; all else being equal, this would suggest a very strong market inefficiency. We demonstrate, however, that fluctuations in order signs are compensated for by anti-correlated fluctuations in transaction size and liquidity, which are also long-memory processes that act to make the returns whiter. We show that some institutions display long-range memory and others don’t.

This paper derives optimal monetary policy rules in setups where certainty equivalence does not hold because either central bank preferences are not quadratic, and/or the aggregate supply relation is nonlinear. Analytical results show that these features lead to sign and size asymmetries, and nonlinearities in the policy rule. Reduced-form estimates indicate that US monetary policy can be characterized by a nonlinear policy rule after 1983, but not before 1979. This finding is consistent with the view that the Fed's inflation preferences during the Volcker-Greenspan regime differ considerably from the ones during the Burns-Miller regime.

This paper deals with tests for detecting conditional heteroskedasticity in ARCH-M models using three kinds of methods: neural networks techniques, bootstrap methods and both combined.As regards the ARCH models, Péguin-Feissolle (2000) developed tests based on the modelling techniques with neural network. However, as regards the ARCH-M models, a nuisance parameter is not identified and the tests are not applicable. To solve this problem, we propose to adapt these neural tests to Davies procedure (1987) leading to new tests. The performance of these latter tests are compared with those of Bera and Ra test (1995).However, Bera and Ra test has not really satisfactory performance and suffer from serious size distortion. Our neural test will have the same problem. To solve this second problem, without loss of power, we apply parametric and nonparametric bootstrap methods on the underlying test statistics.Lastly, to examine the size and the power properties of the tests in small samples, Monte Carlo simulations are carried out with various standard and non-standard models for conditional heteroskedasticity as to illustrate a variety of situations. In addition, the graphical presentation of Davidson and MacKinnon (1998a) is used to show the "true" power of the tests and not only the (nominal) power, as it is often the case, that can be meaningless.

We test for the existence of poverty traps and distribution-dependent growth using a nonlinear dynamic panel data model of household incomes allowing for endogenous attrition. Our estimates for Hungary and Russia in the 1990s reveal significant nonlinearity in the dynamics, consistent with the claim that income inequality attenuates growth in mean income. However, we do not find evidence of a threshold effect at low incomes, as postulated by models of dynamic poverty traps. Our results indicate that households generally bounce back from transient shocks, though we find that the adjustment process is slower for households who are poorer in steady state.

The standard literature on working time has modelled the decisions of firms in a deterministic framework in which firms can choose between employment and overtime (given mandated standard hours). Contrary to this approach, we consider the impact of uncertainty and real options on the decision of working time and examine the determinants of employment and hours in a stochastic framework. A number of simulation exercises to illustrate the workings of the model are also undertaken.

In an economy with multiple sources of risk, the short-term interest rate does not capture all the information that determines the conditional distribution of bond yields. This is also true for path-dependent term structure models. In either case, the current short rate level is not a sufficient statistic for the conditional density of future short rates. This paper studies the empirical relevance of both issues from a time-series nonparametric perspective. The analysis is formulated as a test for the dependence of the short rate drift and diffusion on variables other than the short rate, and exploits Ait-Sahalia, Bickel, and Stocker (2001) dimension reduction method. The paper explores the finite sample performance of the method and applies the test to US interest rate data. Results reject a single-factor Markovian model, although conclusions are sensitive to the choice of additional conditioning variables.