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This paper seeks to explain the serial persistence as well as the substantial number of zeros characterizing global bilateral investment holdings. We explore the different sources of serial persistence in the data (unobserved country pair effects, genuine state dependence, and transitory shocks) and examine the crucial factors affecting the decision to invest in a host country.

Based on a gravity setup, we consider investment behavior at the extensive (participation) margin and employ dynamic first-order Markov probit models, controlling for unobserved cross-sectional heterogeneity and serial correlation in the transitory error component, in order to explore the sources of persistence. Within this modeling framework we explore the importance of institutional quality of the host country in attracting foreign investment.

The data support that the strong persistence is driven by true state dependence, implying that past investment experiences strongly impact on the trajectory of future investment holdings. Institutional quality appears to play a significant role to attract foreign investment.

The empirical findings suggest that due to the existence of genuine state dependence, inward-investment stimulating policy measures could have a more pronounced effect since they are likely to induce a permanent change to the future trajectory of inward investment.

Both the substantial number of zeros and the salient persistence characterizing bilateral investment holdings decision have been previously overlooked in the literature. A study modeling jointly the levels and the selection mechanism could prove a fruitful direction for future research.

We examine the roles of sample initial conditions and unobserved individual effects in consistent estimation of the dynamic binary response panel data model. Different specifications of the model are estimated using female welfare and labor force participation data from the Survey of Income and Program Participation. These include alternative random effects (RE) models, in which the conditional distributions of both the unobserved heterogeneity and the initial conditions are specified, and fixed effects (FE) conditional logit models that make no assumptions on either distribution. There are several findings. First, the hypothesis that the sample initial conditions are exogenous is rejected by both samples. Misspecification of the initial conditions results in drastically overstated estimates of the state dependence and understated estimates of the short- and long-run effects of children on labor force participation. The FE conditional logit estimates are similar to the estimates from the RE model that is flexible with respect to both the initial conditions and the correlation between the unobserved heterogeneity and the covariates. For female labor force participation, there is evidence that fertility choices are correlated with both unobserved heterogeneity and pre-sample participation histories.

We investigate predictive abilities of nonlinear models for stock returns when density forecasts are evaluated and compared instead of the conditional mean point forecasts. The aim of this paper is to show whether the in-sample evidence of strong nonlinearity in mean may be exploited for out-of-sample prediction and whether a nonlinear model may beat the martingale model in out-of-sample prediction. We use the Kullback–Leibler Information Criterion (KLIC) divergence measure to characterize the extent of misspecification of a forecast model. The reality check test of White (2000) using the KLIC as a loss function is conducted to compare the out-of-sample performance of competing conditional mean models. In this framework, the KLIC measures not only model specification error but also parameter estimation error, and thus we treat both types of errors as loss. The conditional mean models we use for the daily closing S&P 500 index returns include the martingale difference, ARMA, STAR, SETAR, artificial neural network, and polynomial models. Our empirical findings suggest the out-of-sample predictive abilities of nonlinear models for stock returns are asymmetric in the sense that the right tails of the return series are predictable via many of the nonlinear models, while we find no such evidence for the left tails or the entire distribution.

A major stumbling block in multivariate discrete data analysis is the problem of evaluating the outcome probabilities that enter the likelihood function. Calculation of these probabilities involves high-dimensional integration, making simulation methods indispensable in both Bayesian and frequentist estimation and model choice. We review several existing probability estimators and then show that a broader perspective on the simulation problem can be afforded by interpreting the outcome probabilities through Bayes’ theorem, leading to the recognition that estimation can alternatively be handled by methods for marginal likelihood computation based on the output of Markov chain Monte Carlo (MCMC) algorithms. These techniques offer stand-alone approaches to simulated likelihood estimation but can also be integrated with traditional estimators. Building on both branches in the literature, we develop new methods for estimating response probabilities and propose an adaptive sampler for producing high-quality draws from multivariate truncated normal distributions. A simulation study illustrates the practical benefits and costs associated with each approach. The methods are employed to estimate the likelihood function of a correlated random effects panel data model of women's labor force participation.

This paper is concerned with estimation and inference for difference-in-difference regressions with errors that exhibit high serial dependence, including near unit roots, unit roots, and linear trends. We propose a couple of solutions based on a parametric formulation of the error covariance. First stage estimates of autoregressive structures are obtained by using the Han, Phillips, and Sul (2011, 2013) X-differencing transformation. The X-differencing method is simple to implement and is unbiased in large N settings. Compared to similar parametric methods, the approach is computationally simple and requires fewer restrictions on the permissible parameter space of the error process. Simulations suggest that our methods perform well in the finite sample across a wide range of panel dimensions and dependence structures.

This paper investigates the effect of household size, and in particular of the number of children of different age groups, on poverty, defined as being in a situation of low income. We apply various static and dynamic probit models to control for the endogeneity of the variables of interest and to account for unobserved heterogeneity, state dependence, and serially correlated error components. Using Luxembourg longitudinal data, we show that the number of children of different age groups significantly affects the probability of being poor. However, the magnitude of the effect varies across different specifications. In addition, we find strong evidence of true poverty persistency due to past experience, spurious poverty persistency due to individual heterogeneity, and transitory random shocks.

The paper analyses the effects of individual and household characteristics on current poverty status, while controlling for initial conditions, past poverty status and unobserved heterogeneity in 14 European countries for the period 1994–2001, using the European Community Household Panel. The distinction between true state dependence and individual heterogeneity has important policy implications, since if the former is the main cause of poverty it may be crucial to break the ‘vicious circle’ of poverty using income-supporting social policies, whereas if it is the latter anti-poverty policies should focus primarily on education, training, development of personal skills and other labour market oriented policies. The empirical results are similar in qualitative terms but rather different in quantitative terms across the EU countries covered in the paper. State dependence remains significant in all model specifications, even after controlling for unobserved heterogeneity or when removing possible endogeneity bias. Higher poverty rates and higher poverty persistence are associated with particular welfare state regimes, although the link is substantially weakened when other explanatory variables are included in the analysis.

We examine the informational roles of trades and time between trades in the domestic and overseas US Treasury markets. A vector autoregressive model is employed to assess the information content of trades and time duration between trades. We find significant impacts of trades and time duration between trades on price changes. Larger trade size induces greater price revision and return volatility, and higher trading intensity is associated with a greater price impact of trades, a faster price adjustment to new information and higher volatility. Higher informed trading and lower liquidity contribute to larger bid–ask spreads off the regular daytime trading period.

This paper gives a selective review on some recent developments of nonparametric methods in both continuous and discrete time finance, particularly in the areas of nonparametric estimation and testing of diffusion processes, nonparametric testing of parametric diffusion models, nonparametric pricing of derivatives, nonparametric estimation and hypothesis testing for nonlinear pricing kernel, and nonparametric predictability of asset returns. For each financial context, the paper discusses the suitable statistical concepts, models, and modeling procedures, as well as some of their applications to financial data. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and more importantly, the paper points to several aspects that deserve further investigation.