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Microfit 5.5
Copyright Bahram Pesaran and M. Hashem Pesaran (2017)

About the Software: Microfit 5.5 is an interactive, menu-driven program with a host of facilities for estimation, hypothesis testing, forecasting, data processing, file management, and graphic display. These features make Microfit 5.5 one of the most powerful menu-driven time-series econometric packages currently available. It is a major advance over Microfit 4 and offers a unique built-in interactive, searchable econometric text. It provides users with technical, functional and tutorial help throughout the package. Another key feature is that it can be used at different levels of technical sophistication.

For experienced users of econometric programmes, it offers a variety of univariate methods, multivariate techniques for cointegration, principal components, canonical correlations and multivariate volatility modelling, and provides a large number of diagnostic and non-nested tests not readily available in other packages. The interaction of excellent graphics and estimation capabilities in Microfit 5.5 allows important econometric research to be carried out in a matter of days rather than weeks.

Download: To download the software, free of charge, visit the Microfit 5.5 webpage.

Global VAR (GVAR) Modelling

About the GVAR Modelling Approach: The Global Vector Autoregressive (GVAR) approach, originally proposed in Pesaran et al. (2004), provides a relatively simple yet effective way of modelling interactions in a complex high-dimensional system such as the global economy. Although GVAR is not the first large global macroeconomic model of the world economy, its methodological contributions lay in dealing with the curse of dimensionality (i.e. the proliferation of parameters as the dimension of the model grows) in a theoretically coherent and statistically consistent manner. Other existing large models are often incomplete and do not present a closed system, which is required for simulation analysis, see Granger and Jeon (2007) for a recent overview of global models.

The GVAR model was developed in the aftermath of the 1997 Asian financial crisis to quantify the effects of macroeconomic developments on the losses of major financial institutions. It was clear then that all major banks are exposed to risk from adverse global or regional shocks, but quantifying these effects required a coherent and simple-to-simulate global macroeconomic model. The GVAR approach provides a useful and practical way of building such a model, and, although developed originally as a tool for credit risk analysis, it soon became apparent that it has numerous other applications. For an extensive survey of the latest developments in GVAR modelling, both the theoretical foundations of the approach and its numerous empirical applications, see Chudik and Pesaran (2016).

The GVAR can be briefly summarized as a two-step procedure. In the first step, small-scale country-specific models are estimated conditional on the rest of the world. These models are represented as augmented VAR models, denoted as VARX* and feature domestic variables and weighted cross-section averages of foreign variables, which are also commonly referred to as ‘star variables’, which are treated as weakly exogenous (or long-run forcing). In the second step, individual country VARX* models are stacked and solved simultaneously as one large global VAR model. The solution can be used for shock scenario analysis and forecasting as is usually done with standard low-dimensional VAR models.

The GVAR approach has been applied to a number of diverse problems. Individual units need not necessarily be countries, but could be regions, industries, goods categories, banks, municipalities or sectors of a given economy, just to mention a few notable examples. Mixed cross-section GVAR models, for instance linking country data with firm-level data, have also been considered in the literature. The GVAR approach is conceptually simple, although it requires some programming skills since it handles large data sets. Fortunately, an accessible and easy-to-use package (the GVAR Toolbox), with no background knowledge of MatLab or Excel required, has been developed by Smith and Galesi (2014). The GVAR Toolbox together with the publicly available GVAR quarterly dataset set, covering the period 1979Q2–2010Q4 for 33 countries, can be used for the application of the GVAR methodology.

Download: To download the latest version of the Global VAR (GVAR) dataset and to obtain an easy-to-use Toolbox designed for the application of the GVAR methodolog visit the Global VAR (GVAR) webpage.