Macroeconomic research often relies on structural vector autoregressions, (S)VARs, to uncover empirical regularities. Critics argue the method goes awry due to lag truncation: short lag-lengths imply a poor approximation to important data-generating processes (e.g. DSGE-models). Empirically,...

Transcripts from the US Federal Open Markets Committee provide, albeit with a lag, valuable information on the monetary policymaking process at the Federal Reserve Bank. We use the data compiled by Chappell et al. (2005b) on preferred interest rates (not votes) of individual FOMC members....

Over the last decade, a significant concern in the energy market has been the decline in the USA's oil imports and fall in energy prices. In this context, we investigate the impact of the shale revolution in the USA on oil prices and examine its potential replicability in other shale-rich...

This study investigates the lead-lag relationships and volatility dynamics among four major cryptocurrencies - Bitcoin, Ethereum, Solana, and Polygon - during the turbulent year of 2022. We address three primary research questions: (1) To what extent do lead-lag relationships exist among major...

In multivariate time series systems, key insights can be obtained by discovering lead-lagrelationships inherent in the data, which refer to the dependence between two timeseries shifted in time relative to one another, and which can be leveraged for thepurposes of control, forecasting or...

When using vector autoregressive (VAR) models for approximating time series, a key step is the selection of the lag length. Often this is performed using information criteria, even if a theoretical justification is lacking in some cases. For stationary processes, the asymptotic properties of the...