Before you begin doing econometric analysis, make sure you’re familiar with your data and how to view it in the popular STATA software. After all, you don’t want to estimate an econometric model with data that’s mostly incomplete or full of errors.
In version 12.1 of STATA, the default setting allows you to open a dataset as large as 64 megabytes (MB) and containing up to 5,000 variables. If your dataset is larger than 64MB, you need to increase the memory allocated to STATA by typing “set memory #m” on the command line, where # is the size of your dataset in MB.
Similarly, if your dataset contains more than 5,000 variables, you need to type “set maxvar #” on the command line, with # being the number of variables in your dataset.
4.1.1 preparing the model 57 4.1.2 estimation 57 4.1.3 solving and testing over the past. 57 4.1.4 solving and testing over the future 58 4.1.5 using the model for forecasts and policy studies 58 4.2 the choice of software error! Bookmark not defined. 4.3 how to organize the development of the model 58 5 chapter 5: preparing the model 61. Econometrics#2: Econometrics Modeling and Analysis in EViews. This is the Second part and will cover Multivariate Modeling, Autocorrelation Techniques, VAR Modeling, Stationarity and Unit Root Testing, CoIntegration Testing and Volatility & ARCH Modeling. This course aims to provide basic to intermediate skills on implementing Econometrics.
The Data tab in the menu bar contains most of the elements you need in order to get acquainted with your data. After opening a STATA dataset, you’ll regularly use the following commands:
In the figure, “describe” and “summarize” commands are used to view the fundamental characteristics of the dataset.
The Data tab or “describe” and “summarize” commands provide the basic information you use for your econometric analysis. Examine the tables containing the descriptive information and make sure that all the values are sensible. In other words, make sure that the minimum, maximum, and mean values are feasible for each variable in your dataset.
You can also use the “list” command on occasion, but be careful with it because it displays the value for every variable and every observation. In other words, it displays the entire dataset. With a large dataset (thousands of observations and dozens of variables), this list isn’t likely to help you find errors unless you refine the list to a specific observation using an “if” statement or by subscripting.
Keep in mind that the results section of STATA, by default, displays approximately one page of output. STATA then prompts you with the “-more-” message. Hitting the return key allows you to see an additional line of output, and hitting the spacebar shows another page of output. If you don’t want STATA to pause for “-more-” messages, type “set more off” on the command line. Subsequent output is then displayed in its entirety.
Please note that, We have divided the 'Econometrics' course in to TWO parts as follows:
This is the Second part and will cover Multivariate Modeling, Autocorrelation Techniques, VAR Modeling, Stationarity and Unit Root Testing, CoIntegration Testing and Volatility & ARCH Modeling.
This course aims to provide basic to intermediate skills on implementing Econometrics/Predictive modelling concepts using Eviews software. Whilst its important to develop understanding of econometrics/quantitative modelling concepts, its equally important to be able to implement it using suitable software packages. This course fills the gap between understanding the concepts and implementing them practically. The course works across multiple software packages such as Eviews, MS Office, PDF writers, and Paint. Econometric modeling course aims to provide quantitative/econometric modelling skills typically/specifically in Finance sector. Quantitative methods and predictive modelling concepts could be extensively used in understanding the financial markets movements, and studying tests and effects. The course picks theoretical and practical datasets for econometrics/quantitative/predictive analysis. Implementations are done using Eviews software. Observations, interpretations, predictions and conclusions are explained then and there on the examples as we proceed through the training. The course also emphasizes on the regression models, and AIMS to also cover Auto-Correlation, Co-Integration and ARCH (Auto Regressive Conditional Heteroscedasticity) models.
Structural Econometric Modeling
Essential skillsets – Prior knowledge of Quantitative methods and MS Office, Paint
Desired skillsets — Understanding of Data Analysis and VBA toolpack in MS Excel will be useful
Econometric Modeling Definition
The course works across multiple software packages such as Eviews, MS Office, PDF writers, and Paint.
Examples Of Econometric Models
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