Helmut Herwartz

Long-Run Links Among Money, Prices, and Output: World-Wide Evidence

Abstract Starting from the quantity theory of money we analyse the dynamic relationships between money, real output and prices for an unbalanced panel of 110 economies. Complementary to trivariate analyses we also adopt a P-star model explaining inflation via an equilibrium price level ( P-star), which in turn depends on potential output and money. A key issue of the paper is the cross-sectional stability of estimation and inference results. We find cointegration among the considered variables. Particularly for high inflation countries homogeneity between prices and money cannot be rejected. Given homogeneity we find evidence for an error-correction mechanism linking current price changes and the lagged price gap. Parameter estimates indicating the adjustment towards the price equilibrium are larger in absolute value for high inflation countries. The latter results indicate that central banks, even in high inflation countries, can improve price stability by controlling monetary growth.

Option pricing under linear autoregressive dynamics, heteroskedasticity, and conditional leptokurtosis

Daily returns of financial assets are frequently found to exhibit positive autocorrelation at lag 1. When specifying a linear AR(1) conditional mean, one may ask how this predictability affects option prices. We investigate the dependence of option prices on autoregressive dynamics under stylized facts of stock returns, i.e. conditional heteroskedasticity, leverage effect, and conditional leptokurtosis. Our analysis covers both a continuous and discrete time framework. The results suggest that a non-zero autoregression coefficient tends to increase the deviation of option prices from Black and Scholes prices caused by stochastic volatility.

Regional Estimates of the Shadow Economy in Europe

The aim of the paper is to estimate the extent of the shadow economy in the regions of the European Union. For this purpose the multiple-indicators multiple-causes approach combined with elements of spatial econometrics is implemented. The analysis shows that the shadow economy is most extensive in Eastern and Southern Europe, confirming results from previous literature. Within countries, the poorest regions tend to exhibit the highest shadow economy quotas. The smallest extent of shadow activities is obtained for the Netherlands and the United Kingdom, while in Poland the shadow economy is most extensive.

Homogenous panel unit root tests under cross sectional dependence: Finite sample modifications and the wild bootstrap

First generation panel unit root tests are known to be invalid under cross sectional dependence. Focussing on the subclass of homogenous tests, three extensions of existing approaches are proposed. First, a test based on a generalized variance estimator is suggested for panels with small time and relatively large cross sectional dimension. Second, the application of refined residuals in variance estimators is proposed to reduce finite sample biases. Third, the wild bootstrap is proved to be an asymptotically valid method of resampling homogenous panel unit root test statistics. A Monte Carlo study shows that the wild bootstrap yields unbiased inference, even in cases where existing procedures are biased. Most accurate results under the null hypothesis are obtained by resampling robust statistics while there is no, or minor, evidence of power loss invoked by the wild bootstrap. An empirical illustration underpins that the current account to GDP ratio is likely panel stationary.

Testing for linear autoregressive dynamics under heteroskedasticity

A puzzling characteristic of asset returns for various frequencies is the often observed positive autocorrelation at lag one. To some extent this can be explained by standard asset pricing models when assuming time-varying risk premia. However, one often finds better results when directly fitting an autoregressive model, for which there is little economic foundation. One may ask whether the underlying process does in fact contain an autoregressive component. It is therefore of interest to have a statistical test at hand that performs well under the stylized facts of financial returns. In this paper, we investigate empirical properties of competing devices to test for autoregressive dynamics in case of heteroskedastic errors. For the volatility process we assume GARCH, TGARCH and stochastic volatility. The results indicate that standard quasi-maximum-likelihood inference for the autoregressive parameter is negatively affected by misspecification of the volatility process. We show that bootstrapped versions of least-squares-based statistics have better empirical size and comparable power properties. Applied to German stock return data, the alternative tests yield very different p-values for a considerable number of stock return processes.

Specification of varying coefficient time series models via generalized flexible least squares

Abstract Flexible Least Squares (FLS) is a method for recursively estimating the time paths of the coefficients of a regression model with time-varying coefficients. In its standard form the FLS solution is capable of capturing smooth changes of the coefficients over the sample period. For time series models erratic coefficient changes, for instance, due to seasonal variation, are possible. A generalization of FLS is proposed which can account for such phenomena. The method is applied to artificially generated as well as real economic data. Specifically, West German income and consumption time series are analyzed in some detail.

Growth, unemployment and labour market institutions: evidence from a cross-section of EU regions

The responsiveness of unemployment to growth is an issue of ongoing political and academic interest. Economic growth is supposed to be the key to increase labour demand and reduce unemployment. Departing from Okuns law, most research on the unemployment intensity of growth has focused on national disparities and the role of labour market institutions. Empirical evidence at the regional level is scarce. We investigate differences in regional labour market responsiveness and their potential determinants for a cross section of European regions. The data set covers the NUTS 2 regions in the EU15 for the period 1980 to 2002. Following a spatial modelling approach interaction among neighbouring labour markets is taken into account. Our findings point to substantial differences in labour market effects of output growth among European countries and regions. Both national labour market institutions and regional characteristics, such as structural change explain a significant part of these disparities.

HEALTH CARE AND IDEOLOGY: A RECONSIDERATION OF POLITICAL DETERMINANTS OF PUBLIC HEALTHCARE FUNDING IN THE OECD

In this article, we examined if partisan ideology and electoral motives influence public healthcare expenditure (HCE) in countries of the Organization for Economic Cooperation and Development. We distinguished between the effects on the growth of the expenditures and its adjustment to violations of a long-run equilibrium linking HCE with macroeconomic and demographic trends. Regarding the influence of partisan ideology, we found that if governments are sufficiently long in power, right-wing governments spend less on public health than their left-wing counterparts. Furthermore, if a right-wing party governs without coalition partners, it responds more strongly to deviations from the long-run HCE equilibrium than left-wing governments. With regard to electoral motives, we found that health expenditure increases in years of elections. Independent of their partisan ideology, single-party (minority) governments induce higher (lower) growth of public HCE. Each of these political factors by its own may increase (decrease) HCE growth by approximately one percentage point. Given an average annual growth of HCE of approximately 4.1%, political factors turn out to be important determinants of trends in public HCE.

A note on model selection in (time series) regression models - general-to-specific or specific-to-general?

This article provides Monte Carlo evidence on the performance of general-to-specific and specific-to-general selection of explanatory variables in linear (auto)regressions. In small samples the former is markedly inefficient in terms of ex-ante forecasting performance.

Forecasting market share using predicted values of competitive behavior: further empirical results

Abstract Forecasting is an important marketing activity for evaluating the expected performance of alternative marketing plans, especially in order to predict earnings, sales or market shares. The purpose of this paper is fourfold. Firstly, we develop and evaluate alternative econometric approaches to predict competitors’ future actions. Secondly, the forecasting performance of attraction models is compared to those of linear and multiplicative market share models not only if competitors’ actions are known a priori but also if competitors’ actions are forecasts. Thirdly, the effects of alternative structural specifications of attraction models on the forecasting accuracy are investigated. Finally, we reinvestigate the impact of OLS estimation versus GLS estimation on the forecasting performance. The adopted empirical methods account for the interdependence of marketing instruments. We also allow for competitive reactions up to 10 periods ago and introduce a new approach concentrating on so-called marketing events characterizing directly the contemporaneous choice of several promotional activities within a brand. Analyzing weekly scanner data from three markets we find that attraction models outperform the share predictions of the linear and multiplicative models even if competitors’ actions are forecast. This result is valid on the market and brand level. In addition, response models outperform the naive model on the market level irrespective of whether competitors’ actions are known a priori or if they are forecasts. On the brand level the superiority of response models over naive models diminishes though it still exists. With respect to the best method of predicting competitors’ actions it turns out that parsimonious specifications like autoregressive price predictions or binary logit models perform conveniently.