Micromotives of Vote Switchers and Macrotransitions: The Case of the Immigration Issue in a Regional Earthquake Election in Germany 2018

Which issue-related motives underlie voters' decision to switch parties at the polls? Do switchers stick to the newly chosen party, or do they oscillate in a short-term way at intermediate elections? Relying on the behavioral theory of elections, we assumed aspiration-based voting of boundedly rational voters. We elicited issue-related switch and stay motives in an open-ended survey question format to identify the individual dominant aspirational frame. We traced the respondents' voting trajectories over three consecutive elections, including two state (2013 and 2018) elections in Bavaria (Germany) and one German federal election (2017). We focused on one of the most polarizing and salient issues in these elections, namely immigration. The case of reference is the 2018 Bavarian state election. Here, the incumbent majoritarian center-right party Christian Social Union tried to deter the entry of the right-wing populist party Alternative for Germany by adapting to it on the immigration issue in tone and position. The selected case allows assessment of the impact of issue-based adaptive behavior of the incumbent party at the level of the voters' switch or stay choices. We estimated the direction and number of voter flows for two interelection sequences of different lengths between different types of polls (federal and state). Our transition estimates are based on the hybrid multinomial Dirichlet model, a new technique integrating individual-level survey data and official aggregate data. Our estimates uncover substantial behavioral differences in the immigration issue public

Micromotives of Vote Switchers and Macrotransitions: The Case of the Immigration Issue in a Regional Earthquake Election in Germany 2018

Which issue-related motives underlie voters' decision to switch parties at the polls? Do switchers stick to the newly chosen party, or do they oscillate in a short-term way at intermediate elections? Relying on the behavioral theory of elections, we assumed aspiration-based voting of boundedly rational voters. We elicited issue-related switch and stay motives in an open-ended survey question format to identify the individual dominant aspirational frame. We traced the respondents' voting trajectories over three consecutive elections, including two state (2013 and 2018) elections in Bavaria (Germany) and one German federal election (2017). We focused on one of the most polarizing and salient issues in these elections, namely immigration. The case of reference is the 2018 Bavarian state election. Here, the incumbent majoritarian center-right party Christian Social Union tried to deter the entry of the right-wing populist party Alternative for Germany by adapting to it on the immigration issue in tone and position. The selected case allows assessment of the impact of issue-based adaptive behavior of the incumbent party at the level of the voters' switch or stay choices. We estimated the direction and number of voter flows for two interelection sequences of different lengths between different types of polls (federal and state). Our transition estimates are based on the hybrid multinomial Dirichlet model, a new technique integrating individual-level survey data and official aggregate data. Our estimates uncover substantial behavioral differences in the immigration issue public

Multiple nonparametric regression and model validation for mixed regressors

The dissertation covers four essays on nonparametric (kernel and/or spline) regression, where tools and methods for the corresponding model validation are provided and discussed for a setup of mixed (discrete and continuous) covariates

On Nonparametric Estimation of a Hedonic Price Function

Haupt H, Schnurbus J, Tschernig R. On nonparametric estimation of a hedonic price function. Journal of Applied Econometrics. 2010;25(5):894-901.Recently, using mixed data on Canadian housing, Parmeter, Henderson, and Kumbhakar (Journal of Applied Econometrics 2007; 22: 695-699) found that a nonparametric approach for estimating a hedonic house price function is superior to formerly suggested parametric and semipararnetric specifications. We carefully reanalyze these specifications for this dataset by applying a recent nonparametric specification test and simulation-based prediction comparisons. For the case at issue our results suggest that a previously proposed parametric specification does not have to be rejected and we illustrate how nonparametric methods provide valuable insights during all modeling steps. Copyright (C) 2010 John Wiley & Sons, Ltd

Statistical validation of functional form in multiple regression using R

In applied statistical research the practitioner frequently faces the problem that there neither is clear guidance from grounds of theoretical reasoning nor exists empirical (meta) evidence on the choice of functional form of a tentative regression model. Thus, parametric modeling resulting in a parametric benchmark model may easily miss important features of the data. Using recently advanced nonparametric regression methods we illustrate two powerful techniques to validate a parametric benchmark model. We discuss an empirical example using a well-known data set and provide R code snippets for the implementation of simulations and examples

Estimation of grouped, time-varying convergence in economic growth

Haupt H, Schnurbus J, Semmler W. Estimation of grouped, time-varying convergence in economic growth. ECONOMETRICS AND STATISTICS. 2018;8(SI):141-158.Classical growth convergence regressions fail to account for various sources of heterogeneity and nonlinearity. Recent contributions advocating nonlinear dynamic factor models remedy these problems by identifying group-specific convergence paths. Similar to statistical clustering methods, those results are sensitive to choices made in the clustering/grouping mechanism. Classical models also do not allow for a time-varying influence of initial endowment on growth. A novel application of a nonparametric regression framework to time-varying, grouped heterogeneity and nonlinearity in growth convergence is proposed. The approach rests upon group-specific transition paths derived from a nonlinear dynamic factor model. Its fully nonparametric nature avoids problems of neglected nonlinearity while alleviating the problem of underspecification of growth convergence regressions. The proposed procedure is backed by an economic rationale for leapfrogging and falling-back of countries due to the time-varying heterogeneity of number, size, and composition of convergence groups. The approach is illustrated by using a current Penn World Table data set. An important aspect of the illustration is empirical evidence for leapfrogging and falling-back of countries, as nonlinearities and heterogeneity in convergence regressions vary over time. (c) 2017 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved

Cross-validating fit and predictive accuracy of nonlinear quantile regressions

The paper proposes a cross-validation method to address the question of specification search in a multiple nonlinear quantile regression framework. Linear parametric, spline-based partially linear, and kernel-based fully nonparametric specifications are contrasted as competitors using cross-validated weighted L1-norm based goodness-of-fit and prediction error criteria. The aim is to provide a fair comparison with respect to estimation accuracy and/or predictive ability for different semi- and nonparametric specification paradigms. This is challenging as the model dimension cannot be estimated for all competitors and the meta-parameters such as kernel bandwidths, spline knot numbers and polynomial degrees are difficult to compare. General issues of specification comparability and automated data-driven meta-parameter selection are discussed. The proposed method further allows to assess the balance between fit and model complexity. An extensive Monte-Carlo study and an application to a well known data set provide empirical illustration of the method

Practical aspects of using quadratic moment conditions in linear dynamic panel data models

We study the estimation of the lag parameter of linear dynamic panel data models with first order dynamics based on the quadratic Ahn and Schmidt (1995) moment conditions. Our contribution is twofold: First, we show that extending the standard assumptions by mean stationarity and time series homoscedasticity and employing these assumptions in estimation restores standard asymptotics and mitigates the non-standard distributions found in the literature. Second, we consider an IV estimator based on the quadratic moment conditions that consistently identifies the true population parameter under standard assumptions. Standard asymptotics hold for the estimator when the cross section dimension is large and the time series dimension is finite. We also suggest a data-driven approach to obtain standard errors and confidence intervals that preserves the time series dependence structure in the data