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Bayesian Collapsed Gibbs Sampling for a Stochastic Volatility Model with a Di...
This dataset has no description
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Deep Distributional Time Series Models and the Probabilistic Forecasting of I...
This dataset has no description
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Identifying and Interpreting the Factors in Factor models via Sparsity: Diffe...
This dataset has no description
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General Bayesian time‐varying parameter vector autoregressions for modeling g...
US yield curve dynamics are subject to time-variation, but there is ambiguity about its precise form. This paper develops a vector autoregressive (VAR) model with time-varying... -
Bayesian estimation of multivariate panel probits with higher‐order network i...
This paper proposes a Bayesian estimation framework for panel data sets with binary dependent variables where a large number of cross-sectional units are observed over a short... -
Do words hurt more than actions? The impact of trade tensions on financial ma...
We use machine learning techniques to quantify trade tensions between the United States and China. Our measure matches well-known events in the US-China trade dispute and is... -
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... -
A regularization approach to common correlated effects estimation (replicatio...
Cross-section average-augmented panel regressions introduced by Pesaran (2006) have been a popular empirical tool to estimate panel data models with common factors. However, the... -
The impact of product and labour market reform on growth: Evidence for OECD c...
We examine the impact of labour and product market reforms on economic growth in 25 OECD countries between 1985 and 2013, and tests whether this impact is conditioned by the... -
Does model complexity add value to asset allocation? Evidence from machine le...
This study evaluates the benefits of integrating return forecasts from a variety of machine learning and forecast combination methods into an out-of-sample asset allocation... -
(Un)expected monetary policy shocks and term premia (replication data)
The term structure of interest rates is crucial for the transmission of monetary policy to financial markets and the macroeconomy. Disentangling the impact of monetary policy on... -
Common factors of commodity prices (replication data)
In this paper, we extract latent factors from a large cross-section of commodity prices, including fuel and non-fuel commodities. We decompose each commodity price series into a... -
Individual forecaster perceptions of the persistence of shocks to GDP (replic...
We analyse individual professional forecasters' beliefs concerning the persistence of GDP shocks. Despite substantial apparent heterogeneity in perceptions, with around one half... -
Information gains from using short‐dated options for measuring and forecastin...
We study the gains from using short-dated options for volatility measurement and forecasting. Using option portfolios, we estimate nonparametrically spot volatility under weak... -
Dynamic evaluation of job search assistance (replication data)
This paper evaluates a job search assistance program for unemployed teachers where the assignment to the program is dynamic. We discuss the methodology of estimating dynamic... -
Commodity prices and inflation risk (replication data)
This paper investigates the role of commodity price information when evaluating inflation risk. Using a model averaging approach, we provide strong evidence of in-sample and... -
Encompassing measures of international consumption risk sharing and their lin...
We investigate international consumption risk sharing in a panel of 15 industrial economies over the historical period 1875-2016. By considering a rich empirical...
