Login or Register Log in with. Forums FAQ. Search in titles only. Posts Latest Activity.VAR Model. Model Three. Part 1 of 2. STATA
Page of 1. Filtered by:. Gianluca Cafiso. Dear Statalisters, this is about the estimation of a panel VAR. I have read the previous posts about this topic, then this should not be a repetition. Given the dimension of my panel long T, short NNickell's bias should be negligible. As we know, there are no built-in commands to estimate panel-VAR in Stata. Although, there are two user-written routines available on the internet: 1 pvar, 2 xtvar.
However, I would like to enjoy the flexibility of the "var" built-in command available in Stata, and I come to my question now. Nonetheless, I made it work by disregarding the panel structure and by considering the data as long time series.
Then, to perform the VAR estimation with the Least Squares Dummy Variables estimator, I use the "var" command and insert dummies as exogenous variables for each longitudinal unit: xi: var y1 y2 y3, exog i.
Or, do you believe that this possibly generates some kind of error in the estimation? Any suggestion, warning, advice is very much appreciated. Gianluca PS The other option is, first, to apply the within-transformation to the data, then, to use the "var" command as above but without exog.
The effects of dynamic feedbacks on LS and MM estimator accuracy in panel data models. Journal of Econometrics. Lof, M. Does sovereign debt weaken economic growth?
Methods for applied macroeconomic research Princeton University Press,VAR Model. Regression Model Three Residual Analysis. Stability of regression model Model One M odel One.
Regression Model One M odel building. Unit Root Testing. Conversion of a variable into stationary Click here Data in Eviews. What is F statistics? F statistics Data in Eviews. What is R square in Eviews? R square Data in Eviews. Data conversion to Normal.
Model One Click here Data in Eviews. General to Specific Model. Johansen long run co-integration equation. Growth Model. Import and Open Economy using dummy variable. Why digital economy is important? Residual from Johansen Cointegartion. Lag selection. Cointegration Test. Granger causality. Impulse response function.
Stata: Data Analysis and Statistical Software
General to specific model. Transmission Channel among variables. Single equation regression model. Lag variable development. Transmission channel among variables.
Serial Correlation Detection. Removal of Heteroscedasticity. Relationship between R square and F Statistics. Serial Correlation in Autoregressive Model. Conversion of a variable into stationary. Removal of Serial Correlation.
Panel Data. Fixed and Random Effect. Data mangement.They are not guaranteed to be complete or free of errors. Comments are welcome. Class slides on univariate stationary time series models. Class slides on Box-Jenkins methodology. Updated April 5, Powerpoint examples.
Typed notes on e stimation of ARMA models by maximum likelihood. Updated April 11, Typed n otes on forecasting covariance stationary modelsand comparing forecasts using the Diebold-Mariano statistic.
Class slides on forecasting. Updated April 10, Typed notes on state space models and the Kalman filter. Updated April 12, Class slides on state space models and the Kalman filter. Updated April 17, Updated April 9, Forecasting forecast ing. State Space Models and the Kalman Filter statespacemodels.
Updated April 18, Updated April 23, Updated April 19, Unit Root and Stationarity Tests unitrootlecture. Class slides on unit root tests. Asymptotic distribution Theory asymptoticsNonstationarySlides. Class slides on asymptotics for nonstationary processes. Updated May 3, Class slides on multivariate time series and VAR models. Updated May 10, Updated May 8, Structural VAR Models svarslides.
Class slides on structural VAR models. Updated May 22, Login or Register Log in with. Forums FAQ. Search in titles only.
Stata: Data Analysis and Statistical Software
Posts Latest Activity. Page of 2. Filtered by:. Justine Bulkaert. Panel Threshold Model xthreg - problems balanced panel 07 Aug Good evening, I am studying the relation between debt and growth. I would like to find an endogenous debt-threshold using Hansen's technique. I have found the command xthreg to do so. However, when I try to run the regression I get the following message:.
Maybe I am misusing the option rx I do not understand why it does not accept my data as balanced. When I started my analysis I got the confirmation that it is strongly balanced:.
I appreciate your help, Regards, Justine Bulkaert. Tags: None. Richard Williams. I have never used the command but my guess is that if there is missing data the panel will no longer be balanced. My further guess is that you would have to delete all records for a panel id if even only one of its records had missing data, e.
There is probably some clever way to do that via egen commands but off the top of my head I don't know what it is. It might be a nice option for xthreg if it allowed you to drop IDs with records that were not balanced.
Comment Post Cancel. I think something like this can get you a balanced panel with all panel ids with any MD deleted. Just be sure to include all the vars you will be using in the gen command. Thank you for your answer. Could you also help me with respect to the rx. However in some examples I have seen that the variable wich is inside rx. I have some difficulties understaning the rx. Excuse me fore these stupid questions. I am quite new to stata.Hansen, Lars Peter, Bun, Maurice J.
Bun M. David Roodman, Roodman, David, Discussion Papers. Neumann, Todd C. Todd C. Tom Doan, "undated". Sims, Christopher A, Kiviet, Jan F.
Nickell, Stephen J, Granger, C W J, Hirotugu Akaike, Judson, Ruth A. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hai:wpaper See general information about how to correct material in RePEc. For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Web Technician.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here.
This allows to link your profile to this item.Econometric Analysis of Panel Data. Class Notes. Return to course home page.
The range of topics covered in the course will span a large part of econometrics generally, though we are particularly interested in those techniques as they are adapted to the analysis of 'panel' or 'longitudinal' data sets.
Topics to be studied include specification, estimation, and inference in the context of models that include individual firm, person, etc. The asymptotic distribution theory necessary for analysis of generalized linear and nonlinear models will be reviewed or developed as we proceed.
We will then turn to instrumental variables, maximum likelihood, generalized method of moments GMMand two step estimation methods. The linear model will be extended to dynamic models and recently developed GMM and instrumental variables techniques.
The last third of the course will focus on nonlinear models. Theoretical developments will focus on heterogeneity in models including random parameter variation, latent class finite mixture and 'mixed' and hierarchical models. We will consider numerous applications from the literature, including static and dynamic regression models, heterogeneous parameters models e. Notes: The following list points to the class discussion notes for Econometric Analysis of Panel Data.
These are Powerpoint. Introduction to Econometrics; Introduction to the course 2. Endogeneity in the linear model 3. Models with Individual Effects 4. Fixed Effects and Hierarchical Models 4-A. Minimum Distance Estimation 5.
Random Effects Models.
Panel Data Structures 7. Linear Regression and Nonlinear Modeling Discrete Choice Models for Spatial Data Hazard Function and Duration Models Econometrics I. Class Notes. Professor W. Abstract: This is an intermediate level, Ph. Topics to be studied include specification, estimation, and inference in the context of models that include then extend beyond the standard linear multiple regression framework.
After a review of the linear model, we will develop the asymptotic distribution theory necessary for analysis of generalized linear and nonlinear models.
We will then turn to instrumental variables, maximum likelihood, generalized method of moments GMMand two step estimation methods. Inference techniques used in the linear regression framework such as t and F tests will be extended to include Wald, Lagrange multiplier and likelihood ratio and tests for nonnested hypotheses such as the Hausman specification test.
Specific modelling frameworks will include the linear regression model and extensions to models for panel data, multiple equation models, and models for discrete choice.
Notes: The following list points to the class discussion notes for Econometrics I. These are Power Point. Introduction: Paradigm of Econometrics pptx pdf 2. Regression Fit, Restricted Least Squares pptx pdf 6.
Interval Estimation, Prediction, Quantile Regression pptx pdf Instrumental Variables and Treatment Effects pptx pdf The Generalized Regression Model pptx pdf Panel Data Modeling pptx pdf Linear Models for Panel Data, Applications pptx pdf Nonlinear Regression pptx pdf Maximum Likelihood Estimation, Binary Choice pptx pdf Time Series Data pptx pdf Monte Carlo Methods: Bayesian Analysis pptx pdf.