In this paper, we describe how to test for the presence of measurement error in explanatory variables. First, we discuss the test of such hypotheses in parametric models such as linear regressions and then introduce a new Stata command [R] dgmtest for a nonparametric test proposed in Wilhelm (2018b).

We propose a new specification test for assessing the validity of fuzzy regression discontinuity designs (FRD-validity). We derive a new set of testable implications, characterized by a set of inequality restrictions on the joint distribution of observed outcomes and treatment status at the cut-off. We show that this new characterization exploits all the information in the data useful for detecting violations of FRD-validity.

Many time-series exhibit “long memory”: Their autocorrelation function decays slowly with lag. This behavior has traditionally been modeled via unit roots or fractional Brownian motion and explained via aggregation of heterogenous processes, nonlinearity, learning dynamics, regime switching or structural breaks. This paper identifies a different and complementary mechanism for long memory generation by showing that it can naturally arise when a large number of simple linear homogenous economic subsystems with short memory are interconnected to form a network such that the outputs of the subsystems are fed into the inputs of others.

This article introduces and investigates the properties of a new bootstrap method for time-series data, the kernel block bootstrap.

Given additional distributional information in the form of moment restrictions, kernel density and distribution function estimators with implied generalised empirical likelihood probabilities as weights achieve a reduction in variance due to the systematic use of this extra information.

This paper shows how to increase the power of Hausman’s (1978) specification test as well as the difference test in a large class of models.

This paper proposes a simple nonparametric test of the hypothesis of no measurement error in explanatory variables and of the hypothesis that measurement error, if there is any, does not distort a given object of interest. We show that, under weak assumptions, both of these hypotheses are equivalent to certain restrictions on the joint distribution of an observable outcome and two observable variables that are related to the latent explanatory variable.

This paper considers inference on fixed effects in a linear regression model estimated from network data.

This chapter sets out the extension of the scope of the classical IV model to cases in which unobserved variables are set-valued functions of observed variables.

We examine the labor market consequences of an exogenous increase in the supply of skilled labor in several cities in Norway, resulting from the construction of new colleges in the 1970s.

We develop a theory for estimation of a high-dimensional sparse parameter 𝛳 defined as a minimizer of a population loss function LD(𝛳,g0) which, in addition to 𝛳, depends on a, potentially infinite dimensional, nuisance parameter g0.

In our laboratory experiment, subjects, in sequence, have to predict the value of a good. We elicit the second subject’s belief twice: first (“first belief”), after he observes his predecessor’s action; second (“posterior belief”), after he observes his private signal. Our main result is that the second subjects weigh the private signal as a Bayesian agent would do when the signal confirms their first belief; they overweight the signal when it contradicts their first belief.