A popular approach to perform inference on a target parameter in the presence of nuisance parameters is to construct estimating equations that are orthogonal to the nuisance parameters, in the sense that their expected first derivative is zero. Such first-order orthogonalization may, however,...

Multidimensional heterogeneity and endogeneity are important features of a wide class of econometric models. With control variables to correct for endogeneity, nonparametric identification of treatment effects requires strong support conditions. To alleviate this requirement, we consider varying...

We develop and implement methods for determining whether relaxing sparsity constraints on portfolios improves the investment opportunity set for risk-averse investors. We formulate a new estimation procedure for sparse second-order stochastic spanning based on a greedy algorithm and Linear...

This paper presents a novel copula-based autoregressive framework for multilayer arrays of integer-valued time series with tensor structure. It complements recent advances in tensor time series that predominantly focus on real-valued data and overlook the unique properties of integer-valued time...

Sample selection is pervasive in applied economic studies. This paper develops semiparametric selection models that achieve point identification without relying on exclusion restrictions, an assumption long believed necessary for identification in semiparametric selection models. Our...

We develop a score-driven time-varying parameter model where no particular parametric error distribution needs to be specified. The proposed method relies on a versatile spline-based density, which produces a score function that follows a natural cubic spline. This flexible approach nests the...

We develop a practical way of addressing the Errors-In-Variables (EIV) problem in the Generalized Method of Moments (GMM) framework. We focus on the settings in which the variability of the EIV is a fraction of that of the mismeasured variables, which is typical for empirical applications. For...

When evaluating partial effects, it is important to distinguish between structural endogeneity and measurement errors. In contrast to linear models, these two sources of endogeneity affect partial effects differently in nonlinear models. We study this issue focusing on the Instrumental Variable...

The propensity score defining the probability of completing a given degree of education - to balance covariates - and the Mincer equation is here estimated at various degrees of higher education. The novelty is in implementing propensity score and regression estimators together in a...

Star variables, such as potential output and the neutral real interest rate, are fundamental to economic policymaking but challenging to identify due to their latent nature. Buncic, Pagan, and Robinson (2023) highlight the difficulty of identifying star variables within short macroeconomic...

We argue that measurement error in historical price data has led researchers to erroneously believe that there was little persistence of inflation during the 19th century. Using a statistical technique that accounts for these errors, we estimate the persistence of (a) US inflation and (b)...

Purpose - The authors' conclusions are based on mathematical derivations that are supported by computer simulations and three worked examples in applications of economics and finance. Finally, the authors provide a link to a computer program so that researchers can perform the analyses easily....

Several process parameters affect product reliability. Traditional reliability improvement methods primarily focus on maximizing product lifetime, often overlooking the variation in product lifetime. Manufacturers, however, aim to produce products with minimal variations in their performance....

This paper focuses on the estimation and testing of multiple breaks that occur at unknown dates in multivariate long memory time series regression models, allowing for fractional cointegration. A likelihood-ratio based approach for estimating the breaks in the parameters and in the covariance of...

In many manuscripts, researchers use multivariable logistic regression to adjust for potential confounding variables when estimating a direct relationship of a treatment or exposure on a binary outcome. After choosing how variables are entered into that model, researchers can calculate an...

The article focuses on properties generalised to the multidimensional case of known coefficients of spatial correlation. The main result of the work is the decomposition of the introduced generalised autocorrelation coefficients into the sum of ordinary autocorrelation coefficients, but...