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| Developer(s) | The JMulTi Team |
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| Stable release | 4.23 / July 23, 2008 |
| Operating system | Linux, Windows |
| Platform | Java |
| Type | econometrics software |
| License | GPL |
| Website | www.jmulti.com |
JMulTi is an open-source interactive software for econometric analysis, specialised in univariate and multivariate time series analysis. It has a Java graphical user interface.
Open-source software (OSS) is a type of computer software in which source code is released under a license in which the copyright holder grants users the rights to study, change, and distribute the software to anyone and for any purpose. Open-source software may be developed in a collaborative public manner. Open-source software is a prominent example of open collaboration.
Computer software, or simply software, is a collection of data or computer instructions that tell the computer how to work. This is in contrast to physical hardware, from which the system is built and actually performs the work. In computer science and software engineering, computer software is all information processed by computer systems, programs and data. Computer software includes computer programs, libraries and related non-executable data, such as online documentation or digital media. Computer hardware and software require each other and neither can be realistically used on its own.
A time series is a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.
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The motivation for its designed was to provide the means by which some time-series econometric procedures that were difficult or unavailable in other packages could be undertaken. Such procedures include Impulse Response Analysis with bootstrapped confidence intervals for VAR/VEC modelling. [1]
Vector autoregression (VAR) is a stochastic process model used to capture the linear interdependencies among multiple time series. VAR models generalize the univariate autoregressive model by allowing for more than one evolving variable. All variables in a VAR enter the model in the same way: each variable has an equation explaining its evolution based on its own lagged values, the lagged values of the other model variables, and an error term. VAR modeling does not require as much knowledge about the forces influencing a variable as do structural models with simultaneous equations: The only prior knowledge required is a list of variables which can be hypothesized to affect each other intertemporally.
