Almost all economic data sets are discretized or rounded to some extent. This paper proposes a regression and a density estimator that work especially well when the data is very discrete. The estimators are a weighted average of the data, and the weights are composed of cubic B-splines. Unlike...

We consider expectations of the form E[logy|x] = Σ_j=1-super-d α_j log x_j as a good starting point for a more general analysis. We show why this naturally leads to the following flexible functional form: E[y|x] = f(Σ_j=1-super-dh_j(x_j)), where f(ċ) and the h_j(ċ)'s are estimated by cubic splines....

This paper proposes a new nonparametric estimator for general regression functions with multiple regressors. The method used here is motivated by a remarkable result derived by Kolmogorov (1957) and later tightened by Lorentz (1966). In short, any continuous function f(x_1,...,x_d) has the...

This paper proposes a new nonparametric estimator for general regression functions with multiple regressors. The method used here is motivated by a remarkable result derived by Kolmogorov (1957) and later tightened by Lorentz (1966). In short, they show that any continuous function of multiple...

In this paper, we consider expectations of the form E[log(y)|x] = a'log(x) as a good starting point for a more general analysis. We show why this naturally leads to the following flexible functional form E[y|x] = f(h(x)), where all functions are estimated by cubic splines. One of the main goals...

Actual investment performance reflects the underlying strategy of the portfolio manager and the execution costs incurred in realizing those objectives. Execution costs, especially in illiquid markets, can dramatically reduce the notional return to an investment strategy. This paper examines the...

Estimation and inference for weighted nonlinear least squares regressions are examined for the case in which the regressors are stochastic, rather than fixed, and where erros may be both heteroscedastic and serially correlated. The usual least squares parameter covarience matrix estimator may be...