Ecological panel inference in repeated cross sections

This paper presents a Markov chain model for the estimation of individual-level binary transitions from a time series of independent repeated cross-sectional (RCS) samples. Although RCS samples lack direct information on individual turnover, it is demonstrated here that it is possible with these data to draw meaningful conclusions on individual state-to-state transitions. We discuss estimation and inference using maximum likelihood, parametric bootstrap and Markov chain Monte Carlo approaches. The model is illustrated by an application to the rise in ownership of computers in Dutch households since 1986, using a 13-wave annual panel data set. These data encompass more information than we need to estimate the model, but this additional information allows us to assess the validity of the parameter estimates. We examine the determinants of the transitions from 'have-not' to 'have' (and back again) using well-known socio-economic and demographic covariates of the digital divide. Parametric bootstrap and Bayesian simulation are used to evaluate the accuracy and the precision of the ML estimates and the results are also compared with those of a first-order dynamic panel model. To mimic genuine repeated cross-sectional data, we additionally analyse samples of independent observations randomly drawn from the panel. Software implementing the model is available

Inferring transition probabilities from repeated cross sections: a cross-level inference approach to US presidential voting

This paper outlines a nonstationary, heterogeneous Markov model designed to estimate entry and exit transition probabilities at the micro-level from a time series of independent cross-sectional samples with a binary outcome variable. The model has its origins in the work of Moffitt (1993) and shares features with standard statistical methods for ecological inference. We show how ML estimates of the parameters can be obtained by the method-of- scoring, how to estimate time-varying covariate effects, and how to include non-backcastable variables in the model. The latter extension of the basic model is an important one as it strongly increases its potential application in a wide array of research contexts. The example illustration uses survey data on American presidential vote intentions from a five-wave panel study conducted by Patterson (1980) in 1976. We treat the panel data as independent cross sections and compare the estimates of the Markov model with the observations in the panel. Directions for future work are discussed

Timing of Vote Decision in First and Second Order Dutch Elections 1978-1995: Evidence from Artificial Neural Networks

A time series (t=921) of weekly survey data on vote intentions in the Netherlands for the period 1978-1995 shows that the percentage of undecided voters follows a cyclical pattern over the election calendar. The otherwise substantial percentage of undecided voters decreases sharply in weeks leading up to an election and gradually increases afterwards. This paper models the dynamics of this asymmetric electoral cycle using artificial neural networks, with the purpose of estimating when the undecided voters start making up their minds. We find that they begin to decide which party to vote for nine weeks before a first order national parliamentary election and one to four weeks before a second order election, depending on the type of election (European Parliament, Provincial States, City-councils). The effect of political campaigns and the implications for political analysis are discussed

Better poll sampling would have cast more doubt on the potential for Hillary Clinton to win the 2016 election

Donald Trump's 2016 election victory took many by surprise - most of the polling had suggested a victory for Hillary Clinton. But were the polls wrong? In new research Manfred te Grotenhuis, Subu Subramanian, Rense Nieuwenhuis, Ben Pelzer and Rob Eisinga examine the election polls' accuracy by randomly sampling from each state's observed voters for Clinton or Trump. They find that a relatively small polling bias which saw Republicans underrepresented in a number of key states tipped the polling – and therefore the predicted probability that she would win – in favor of Hillary Clinton