Economics and Finance

We study dynamic panel data models where the long run outcome for a particular cross-section is affected by a weighted average of the outcomes in the other cross-sections. We show that imposing such a structure implies several cointegrating relationships that are nonlinear in the coefficients to be estimated. Assuming that the weights are exogenously given, we extend the dynamic ordinary least squares methodology and provide a dynamic two-stage least squares estimator. We derive the large sample properties of our proposed estimator and investigate its small sample distribution in a simulation study. Then our methodology is applied to US financial market data, which consist of credit default swap spreads, firm specific and industry data. A "closeness" measure for firms is based on input-output matrices. Our estimates show that this particular form of spatial correlation of credit default spreads is substantial and highly significant.

Growth rate data that are collected incompletely in cross-sections is a quite frequent problem. Chow and Lin (1971) have developed a method for predicting unobserved disaggregated time series and we propose an extension of the procedure for completing cross-sectional growth rates similar to the spatial Chow-Lin method of Liano et al. (2009). Disaggregated growth rates cannot be predicted directly and requires a system estimation of two Chow-Lin prediction models, where we compare classical and Bayesian estimation and prediction methods. We demonstrate the procedure for Spanish regional GDP growth rates between 2000 and 2004 at a NUTS-3 level. We evaluate the growth rate forecasts by accuracy criteria, because for the Spanish data-set we can compare the predicted with the observed values.

System of panel models are popular models in applied sciences and the question of spatial errors has created the recent demand for spatial system estimation of panel models. Therefore we propose new diagnostic methods to explore if the spatial component will change significantly the outcome of non-spatial estimates of seemingly unrelated regression (SUR) systems. We apply a local sensitivity approach to study the behavior of generalized least squares (GLS) estimators in two spatial autoregression SUR system models: a SAR model with SUR errors (SAR-SUR) and a SUR model with spatial errors (SUR-SEM). Using matrix derivative calculus we establish a sensitivity matrix for spatial panel models and we show how a first order Taylor approximation of the GLS estimators can be used to approximate the GLS estimators in spatial SUR models. In a simulation study we demonstrate the good quality of our approximation results.

We study the relationship between education and cognitive functioning at older ages by exploiting compulsory schooling reforms, implemented in six European countries during the 1950s and 1960s. Using data of individuals aged 50+ from the Survey of Health, Aging and Retirement in Europe (SHARE), we assess the causal effect of education on old-age memory, fluency, numeracy, orientation and dementia. We find a positive impact of schooling on memory. One year of education increases the delayed memory score by about 0.3, which amounts to 16% of the standard deviation. Furthermore, for women, we find that more education reduces the risk of dementia.

We study the benefits of forecast combinations based on forecast-encompassing tests relative to uniformly weighted forecast averages across rival models. For a realistic simulation design, we generate multivariate time-series samples of size 40 to 200 from a macroeconomic DSGE-VAR model. Constituent forecasts of the combinations are formed from four linear autoregressive specifications, one of them a more sophisticated factor-augmented vector autoregression (FAVAR). The forecaster is assumed not to know the true data-generating model. Results depend on the prediction horizon. While one-step prediction fails to support test-based combinations at all sample sizes, the test-based procedure clearly dominates at prediction horizons greater than two.

We study the asset allocation of a quadratic loss-averse (QLA) investor and derive conditions under which the QLA problem is equivalent to the mean-variance (MV) and conditional value-at-risk (CVaR) problems. Then we solve analytically the two-asset problem of the QLA investor for a risk-free and a risky asset. We find that the optimal QLA investment in the risky asset is finite, strictly positive and is minimal with respect to the reference point for a value strictly larger than the risk-free rate. Finally, we implement the trading strategy of a QLA investor who reallocates her portfolio on a monthly basis using 13 EU and US assets. We find that QLA portfolios (mostly) outperform MV and CVaR portfolios and that incorporating a conservative dynamic update of the QLA parameters improves the performance of QLA portfolios. Compared with linear loss-averse portfolios, QLA portfolios display significantly less risk but they also yield lower returns.

The purpose of this paper is to explore how the concept of a Drèze equilibrium can be extended to multiperiod production economies with incomplete markets. Constrained efficiency cannot serve as a basis for such an extension because multiperiod models tend to violate even weak constrained efficiency requirements. We show by means of examples how the difficulties that arise in the case of sequential trade can be taken into account. Finally, we employ the concept of minimal efficiency, which has been introduced by Dierker et al. (2005) in a two-period model, to derive a natural extension of the Drèze rule. This is possible because minimal efficiency relies on a planner who can choose the production plan but who cannot interfere with future consumption otherwise.

We study the determinants of trust in the ECB as measured by the European Commission’s Eurobarometer survey in particular during the global financial crisis and the European sovereign debt crisis. We find that the fall in trust in the ECB in crisis times can be rather well explained based on the precrisis determinants, and show that the fall in trust reflected the macroeconomic deterioration, a more generalised fall in the trust in European institutions in the wake of the crisis as well as the severity of the banking sector’s problems, to which the ECB was associated in the public opinion.

A parsimonious search and matching model of the labor market with endogenous separation is embedded in a North-North intermediate goods trade framework. International product market integration leads to redistribution of market shares from 'weak' to 'strong' firms within an industry, implying chances and threats for them, as firms are ex-ante unaware of their relative advantage over the competitor. Opening the economy will therefore increase the dispersion of potential revenues and consequently lead to higher labor market turnover, higher welfare and increased wage inequality, while the effect on employment is ambiguous. Ceteris paribus, the effects are qualitatively similar to decreasing employment protection in form of costly firing restrictions which prevent the economy from reaching a first best allocation. The positive welfare effects of opening to trade are decreasing in the level of firing costs. This can therefore lead to a substantial failure in reaping the benefits from economic integration.

We investigate the causal effect of market structure on market performance in the consumer electronics. We combine data from Austria’s largest online site for price comparisons with retail data on wholesale prices provided by a major hardware producer for consumer electronics. We observe input prices of firms, and all their moves in the entry and the pricing game over the whole product lifecycle. Using this information for 70 digital cameras, we generate instrumental variables for the number of firms in the market based on the shops’ entry decisions on other product markets in the past. We find that instrumenting is particularly important for estimating the effect of competition on the markup of the price leader.