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Recurrent conditional heteroskedasticity (replication data)
We propose a new class of financial volatility models, called the REcurrent Conditional Heteroskedastic (RECH) models, to improve both in-sample analysis and out-of-sample... -
Is euro area lowflation here to stay? Insights from a time‐varying parameter ...
We build a time-varying parameter model that jointly explains the dynamics of euro area inflation and inflation expectations. Our goal is to explain the weak inflation during... -
Focused Bayesian prediction (replication data)
We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is... -
Unobserved components with stochastic volatility: Simulation‐based estimation...
The unobserved components time series model with stochastic volatility has gained much interest in econometrics, especially for the purpose of modelling and forecasting... -
Multivariate fractional integration tests allowing for conditional heterosked...
We introduce a new joint test for the order of fractional integration of a multivariate fractionally integrated vector autoregressive (FIVAR) time series based on applying the... -
Forecasting stock returns with model uncertainty and parameter instability (r...
We compare several representative sophisticated model averaging and variable selection techniques of forecasting stock returns. When estimated traditionally, our results confirm... -
Endogeneity and non‐response bias in treatment evaluation – nonparametric ide...
This paper proposes a nonparametric method for evaluating treatment effects in the presence of both treatment endogeneity and attrition/non-response bias, based on two... -
CCE in fixed‐T panels (replication data)
The presence of unobserved heterogeneity and its likely detrimental effect on inference has recently motivated the use of factor-augmented panel regression models. The workhorse... -
To pool or not to pool: What is a good strategy for parameter estimation and ...
This paper considers estimating the slope parameters and forecasting in potentially heterogeneous panel data regressions with a long time dimension. We propose a novel optimal... -
Telling tales from the tails: High‐dimensional tail interdependence (replicat...
We propose a simple and flexible framework that allows for a comprehensive analysis of tail interdependence in high dimensions. We use co-exceedances to capture the structure of... -
A factor‐augmented vector autoregressive (FAVAR) approach for monetary policy...
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Expected market returns: SVIX, realized volatility, and the role of dividends...
This note provides a replication of Martin's (Quarterly Journal of Economics, 2017, 132(1), 367-433) finding that the implied volatility measure SVIX predicts US stock market... -
Bayesian parametric and semiparametric factor models for large realized covar...
This paper introduces a new factor structure suitable for modeling large realized covariance matrices with full likelihood-based estimation. Parametric and nonparametric... -
Should I stay or should I go? A latent threshold approach to large‐scale mixt...
We propose a straightforward algorithm to estimate large Bayesian time-varying parameter vector autoregressions with mixture innovation components for each coefficient in the... -
Comovements and asymmetric tail dependence in state housing prices in the USA...
We reexamine the methods used in estimating comovements among US regional home prices and find that there are insufficient moments to ensure a normal limit necessary for... -
Estimating the U.S. output gap with state‐level data (replication data)
This paper develops a method to estimate the U.S. output gap by exploiting the cross-sectional variation of state-level output and unemployment rate data. The model assumes that... -
Bubbles and crises: Replicating the Anundsen et al. (2016) results (replicati...
This paper both narrowly and widely replicates the results of Anundsen et al. (Journal of Applied Econometrics, 2016, 31(7), 1291-1311). I am able to reproduce the same results... -
How the baby boomers' retirement wave distorts model‐based output gap estimat...
This paper illustrates, based on an example, the importance of consistency between empirical measurement and the concept of variables in estimated macroeconomic models. Since... -
Exploiting tail shape biases to discriminate between stable and student t alt...
The nonnormal stable laws and Student t distributions are used to model the unconditional distribution of financial asset returns, as both models display heavy tails. The... -
Ancestry and development: New evidence (replication data)
We revisit the relationship between ancestral distance and barriers to the diffusion of development by replicating previous results with a new genomic dataset on human...
