We propose a method to generate flexible mixture distributions that are useful for estimating models such as the mixed logit model using simulation. The method is easy to implement, yet it can approximate essentially any mixture distribution. We test it with good results in a simulation study...

We analyse statistical inference for top income shares in finite samples. The asymptotic inference performs poorly even in large samples. The standard bootstrap tests give some improvement, but can be unreliable. The semi-parametric bootstrap approach is accurate in moderate and larger samples.

This paper examines how much financial development facilitates economic growth by nonparametrically estimating the effect of financial development on reducing the costs of external finance to firms. The data reveal substantial evidence of diminishing returns to improvement in financial development.

This paper evaluates the performance of a recently emerging multivariate quadrature-based Sparse Grids Integration (SGI) and the well-known Geweke–Hajivassiliou–Keane (GHK) simulator in estimating multivariate binary probit models. Monte Carlo exercises demonstrate that in lower dimension...

This note shows that two ways of simulation based bias correction–indirect inference and bootstrap bias correction–are equivalent for two-stage-least-squares, as well as k-class estimators for the standard linear model with endogenous regressors.

In this paper we extend the FMLS-based CUSUM cointegration test (Xiao and Phillips, 2002) for testing the smooth time-varying cointegration null hypothesis. For this purpose we use Chebyshev time polynomials to specify time-varying coefficients under the null. We derive the limiting distribution...

The paper studies the interaction between aggregation and persistence pertaining to skip sampling of stock variables as well as temporal aggregation of flow variables for the generalized fractional processes. We show that, for skip sampling, the long memory feature at the zero frequency can...

We examine finite sample properties of estimators for approximate factor models when N is small. Contrary to the “rule-of-thumb”, we find that the principal component analysis estimator and the quasi-maximum likelihood estimator perform well even when N is small.

This paper considers a multivariate extension of the test for neglected nonlinearity proposed by Tsay (1986) that uses principal components to overcome the problem of dimensionality that is common with tests of this type. Monte Carlo experiments reveal that the modified multivariate test...

We develop a sieve bootstrap range test for poolability of cointegrating regressions in dependent panels and evaluate by simulation its performances. The test seems to have good size and power properties even with small cross-sections, moderate time samples, and low heterogeneity.