We develop a nonparametric specification test for continuous-time models using the transition density. Using a data transform and correcting for the boundary bias of kernel estimators, our test is robust to serial dependence in data and provides excellent finite sample performance. Besides univariate diffusion models, our test is applicable to a wide variety of continuous-time and discrete-time dynamic models, including time-inhomogeneous diffusion, GARCH, stochastic volatility, regime-switching, jump-diffusion, and multivariate diffusion models. A class of separate inference procedures is also proposed to help gauge possible sources of model mis-specification. We strongly reject a variety of univariate diffusion models for daily Eurodollar spot rates and some popular multivariate affine term structure models for monthly U.S. Treasury yields.

Managements (“insiders”) of many corporations, especially small or newly-public firms, invest considerable resources in investor relations. We develop a model to explore the incentives of insiders to undertake such costly investments. We point out that insiders may undertake such investments not necessarily to improve the share price, but to enhance the liquidity of their block of shares. This leads to a divergence of interest between insiders and dispersed outside shareholders regarding investor relations. Our model predicts that the demographics of insiders (e.g. liquidity needs, size of equity stakes) are important determinants of the extent of investor relations across firms.

Entropy is a classical statistical concept with appealing properties. Establishing asymptotic distribution theory for smoothed nonparametric entropy measures of dependence has so far proved challenging. In this paper, we develop an asymptotic theory for a class of kernel-based smoothed nonparametric entropy measures of serial dependence in a time-series context. We use this theory to derive the limiting distribution of Granger and Lin's (1994) normalized entropy measure of serial dependence, which was previously not available in the literature. We also apply our theory to construct a new entropy-based test for serial dependence, providing an alternative to Robinson's (1991) approach. To obtain accurate inferences, we propose and justify a consistent smoothed bootstrap procedure. The naive bootstrap is not consistent for our test. Our test is useful in, for example, testing the random walk hypothesis, evaluating density forecasts, and identifying important lags of a time series. It is asymptotically locally more powerful than Robinson's (1991) test, as is confirmed in our simulation. An application to the daily S&P 500 stock price index illustrates our approach.

This paper studies the market for monopolistically supplied sweepstakes. We derive equilibrium demands for fixed-prize and variable-prize sweepstakes and determine the profit-maximizing prize level and pay-out ratio respectively. It can be profitable to offer each type of sweepstake when there is a large enough number of weighted utility consumers who have constant absolute risk attitudes, are strictly averse to small as well as symmetric risks, and display longshot preference behaviour. Moreover, for the variable-prize sweepstake, the supplier will generally find it profitable to combine sweepstakes targeting two smaller populations, and offer a single sweepstake to the combined population. This implication is corroborated by the recent spate of mergers of smaller state lotteries into larger ones.

We study the problem of parameter inference in (possibly non-linear and non-smooth) econometric models when the data are measured with error. We allow for arbitrary correlation between the true variables and the measurement errors. To solve the identification problem, we require the existence of an auxiliary data-set that contains information about the conditional distribution of the true variables given the mismeasured variables. Our main assumption requires that the conditional distribution of the true variables given the mismeasured variables is the same in the primary and auxiliary data. Our methods allow the auxiliary data to be a validation sample, where the primary and validation data are from the same distribution, and more importantly, a stratified sample where the auxiliary data-set is not from the same distribution as the primary data. We also show how to combine the two data-sets to obtain a more efficient estimator of the parameter of interest. We establish the large sample properties of the sieve based estimators under verifiable conditions. In particular, we allow for the mismeasured variables to have unbounded supports without employing the tedious trimming scheme typically used in kernel based methods. We illustrate our methods by estimating a returns to schooling censored quantile regression using the CPS/SSR 1978 exact match files where the dependent variable is measured with error of arbitrary kind.

We develop a nonparametric specification test for continuous-time models using the transition density. Using a data transform and correcting for the boundary bias of kernel estimators, our test is robust to serial dependence in data and provides excellent finite sample performance. Besides univariate diffusion models, our test is applicable to a wide variety of continuous-time and discrete-time dynamic models, including time-inhomogeneous diffusion, GARCH, stochastic volatility, regime-switching, jump-diffusion, and multivariate diffusion models. A class of separate inference procedures is also proposed to help gauge possible sources of model misspecification. We strongly reject a variety of univariate diffusion models for daily Eurodollar spot rates and some popular multivariate affine term structure models for monthly U.S. Treasury yields. Copyright 2005, Oxford University Press.

Economic theories in time series contexts usually have implications on and only on the conditional mean dynamics of underlying economic variables. We propose a new class of specification tests for time series conditional mean models, where the dimension of the conditioning information set may be infinite. Both linear and nonlinear conditional mean specifications are covered. The tests can detect a wide range of model misspecifications in mean while being robust to conditional heteroscedasticity and higher order time-varying moments of unknown form. They check a large number of lags, but naturally discount higher order lags, which is consistent with the stylized fact that economic behaviours are more affected by the recent past events than by the remote past events. No specific estimation method is required, and the tests have the appealing "nuisance parameter free" property that parameter estimation uncertainty has no impact on the limit distribution of the tests. A simulation study shows that it is important to take into account the impact of conditional heteroscedasticity; failure to do so will cause overrejection of a correct conditional mean model. In a horse race competition on testing linearity in mean, our tests have omnibus and robust power against a variety of alternatives relative to some existing tests. In an application, we find that after removing significant but possibly spurious autocorrelations due to nonsynchronous trading, there still exists significant predictable nonlinearity in mean for S&P 500 and NASDAQ daily returns. Copyright 2005, Wiley-Blackwell.

ABSTRACT A mutual fund manager is more likely to buy (or sell) a particular stock in any quarter if other managers in the same city are buying (or selling) that same stock. This pattern shows up even when the fund manager and the stock in question are located far apart, so it is distinct from anything having to do with local preference. The evidence can be interpreted in terms of an epidemic model in which investors spread information about stocks to one another by word of mouth.