Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.     

Weiter behandeln oder nicht?

Ein Fallbeispiel - Frau Schneider erkrankte im Alter von 73 Jahren an einer akuten myeloischen Leuk?mie (AML). Die eingeleiteten Therapien griffen nicht. Schlie?lich stimmte sie der Therapiebegrenzung zu. Vor welchen klinischen und normativen Herausforderungen stand das Behandlungsteam? Welche L?sungsans?tze für das praktische Vorgehen k?nnen aus diesem Fall geschlussfolgert werden?     

On Conditional Density Estimation

With the aim of mitigating the possible problem of negativity in the estimation of the conditional density function, we introduce a so-called re-weighted Nadaraya-Watson (RNW) estimator. The proposed RNW estimator is constructed by a slight modification of the well-known Nadaraya-Watson smoother. With a detailed asymptotic analysis, we demonstrate that the RNW smoother preserves the superior large-sample bias property of the local linear smoother of the conditional density recently proposed in the literature. As a matter of independent statistical interest, the limit distribution of the RNW estimator is also derived.     

Do Immigrants Affect Firm‐Specific Wages?*

We propose and test a novel effect of immigration on wages. Existing studies have focused on the wage effects that result from changes in the aggregate labour supply in a competitive labour market. We argue that if labour markets are not fully competitive, immigrants might also affect wage formation at the most disaggregate level – the workplace. Using linked employer?employee data, we find that an increased use of low‐skilled immigrant workers has a significantly negative effect on the wages of native workers at the workplace – also when controlling for potential endogeneity of the immigrant share using both fixed effects and instrumental variables.     

The impact of referendums on the centralisation of public goods provision: a political economy approach

The paper compares decision-making on the centralisation of public goods provision in the presence of regional externalities under representative and direct democratic institutions. A model with two regions, two public goods and regional spillovers is developed in which uncertainty over the true preferences of candidates makes strategic delegation impossible. The political economy argument against centralisation of Besley and Coate (J Public Econ 87:2611–2637, 2003) does therefore not apply. Instead, it is shown that the existence of rent extraction by delegates alone suffices to make cooperative centralisation more likely through representative democracy under reasonable assumptions. In the case of non-cooperative centralisation, the more extensive possibilities for institutional design under representative democracy increase the likelihood of centralisation. Direct democracy may thus be interpreted as a federalism-preserving institution.     

Forecasting Housing Approvals in Australia: Do Forecasters Herd?

Price trends in housing markets may reflect herding of market participants. A natural question is whether such herding, to the extent that it occurred, reflects herding in forecasts of professional forecasters. Using more than 6,000 forecasts of housing approvals for Australia, we did not find evidence of forecaster herding. On the contrary, forecasters anti‐herd and, thereby, tend to intentionally scatter their forecasts around the consensus forecast. The extent of anti‐herding seems to vary over time. We also found that more pronounced anti‐herding leads to less accurate forecasts.     

Venture Capital Valuation,Partial Adjustment,and Underpricing: Behavioral Bias or Information Production?

Using a sample of venture capital (VC)‐backed initial public offerings (IPOs), we analyze the role played by perceived valuation changes on IPO underpricing. We find that perceived valuation change from the last pre‐IPO VC round to the IPO affects IPO underpricing in a nonlinear way. Further analysis indicates that information‐based theories, not behavioral biases, explain this nonlinearity. We also find that the previously documented partial adjustment effect and its nonlinear impact on IPO underpricing are related to the trajectory of the perceived valuation changes, which stands in stark contrast to prior evidence of the importance of behavioral biases.