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# Code

- R code for the AWB trend inference method developed in the paper “Autoregressive Wild Bootstrap Inference for Nonparametric Trends”.
- R toolbox containing code, help file and dataset, for the SPECS method developed in the paper “An automated approach towards sparse single-equation cointegration modelling”. The code and data in the toolbox allow for replication of the empirical study in the paper.
- MATLAB toolbox for the WIMP method developed in the paper “Inference for Impulse Responses under Model Uncertainty”. The code and data in the toolbox allow for replication of the WIMP confidence bands for all the identification schemes used in the empirical study in the paper.
- GAUSS code and R code for the modified wild bootstrap tests developed in the paper “A Multivariate Invariance Principle for Modified Wild Bootstrap Methods with an Application to Unit Root Testing”. The code includes the lag length selection methods developed in the paper “Lag Length Selection for Unit Root Tests in the Presence of Nonstationary Volatility”.
- GAUSS code and R code for the bootstrap panel predictability tests developed in the paper “Robust Block Bootstrap Panel Predictability Tests”.
- GAUSS code for the bootstrap sequential quantile tests developed in the paper “Bootstrap Sequential Tests to Determine the Order of Integration of Individual Units in a Time Series Panel”.
- GAUSS code for the bootstrap union tests developed in the paper “Bootstrap Union Tests for Unit Roots in the Presence of Nonstationary Volatility”, with A. M. Robert Taylor.
- GAUSS code for the bootstrap panel unit root tests developed in the paper “Cross-Sectional Dependence Robust Block Bootstrap Panel Unit Root Tests”, with Franz C. Palm and Jean-Pierre Urbain.
- GAUSS code for the bootstrap ECM cointegration tests developed in the paper “A Sieve Bootstrap Test for Cointegration in a Conditional Error Correction Model”, with Franz C. Palm and Jean-Pierre Urbain.

Notes

- The codes are provided freely for non-commercial use only. The codes are fully functional but provided without any warranty whatsoever. The R code has been tested to give (almost) identical output to the Gauss output using a limited number of datasets. Questions on how to use the codes, or comments and suggestions on typos, improvements, etc. are welcome by e-mail. If you use the code in your research, an acknowledgement in the form of a reference to this website and a citation of the relevant papers is appreciated.
- The zip-files with Gauss codes contain two files: a ‘.gss’ file with the original Gauss code, and a ‘.src’ file with modifications of that code such that it can be run in OxGauss. Typically the modifications, such as changing the random number generator, do not affect the functionality of the code; the exception is the MWB.src file which has a minor loss of functionality (see the file itself for more information).
- OxGauss is part of Ox; the Ox Console version is freely available for academic use and can be downloaded here.
- R is available for free download here.