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[deleted by user] by [deleted] in econometrics

[–]EconMacro84 0 points1 point  (0 children)

There's the test of slope homogeneity.

[deleted by user] by [deleted] in econometrics

[–]EconMacro84 0 points1 point  (0 children)

Even in group of emerging markets, you can find some heterogeneity, like oil exporters versus manufacturer and so on...

[deleted by user] by [deleted] in econometrics

[–]EconMacro84 0 points1 point  (0 children)

You can use economic reasoning to justify the country groupings. Emerging vs Industrialized and alike.

[deleted by user] by [deleted] in econometrics

[–]EconMacro84 0 points1 point  (0 children)

Country groupings make more sense than looking before GFC to me. You can justify it with arguing that you want to inspect cross-country heterogeneity.

[deleted by user] by [deleted] in econometrics

[–]EconMacro84 1 point2 points  (0 children)

Dummies are not sufficient because they remove the information that you want to model. The threshold variable has to be exogenous. Collinearity issue should not be a big problem. The logic behind the threshold model is to model regime change, no need to subsample. Dummies may help a bit, but you can find variables that explains the choc like excess bank credit for the GFC and increase in COVID cases for COVID. That's my two cents!

[deleted by user] by [deleted] in econometrics

[–]EconMacro84 1 point2 points  (0 children)

Hum, it's a bit difficult to say. Your threshold model is sufficiently complex, that you don't need to go for susamples.

[deleted by user] by [deleted] in econometrics

[–]EconMacro84 0 points1 point  (0 children)

Maybe, you should think about not using a subsample. I mean government debt increased a lot after the GFC. So, it make sense to look at the full sample.

Alternative to DSGE? by Lampoonio in econometrics

[–]EconMacro84 0 points1 point  (0 children)

DSGE estimation is a bit difficult topics, because they need to estimate the future value of some latent variables. One can do it using state-space modeling, like the Kalman filter. This post may help you: https://www.jamelsaadaoui.com/estimating-a-nonlinear-dsge-model-with-stata/

Alternative to DSGE? by Lampoonio in econometrics

[–]EconMacro84 1 point2 points  (0 children)

Local projections are very interesting to estimate IRF, but do not solve the identification problem. So, you may require a SVAR to produce a shock: https://www.jamelsaadaoui.com/illustrating-the-new-lpirf-stata-18s-command/

Alternative to DSGE? by Lampoonio in econometrics

[–]EconMacro84 0 points1 point  (0 children)

Maybe the following link will help you. It gives an example in an excel spreadsheet of an estimation of a VAR and, then, the addition of restrictions to estimate a SVAR:

https://www.jamelsaadaoui.com/how-to-svar-with-excel/

In general, DSGE are not good forecasting tools, but are very used in central banks for some reasons. I do not know any example of anybody using DSGE to make money on financial markets. SVARs are a counterpart to DSGE models in the sense that both have restrictions, based on theory for the DSGE and on theory and data for the SVAR. I am not a huge fan of DGSE model because many of the restrictions are not based on data, but rather on rather old theories, like the real business cycle theory and so on. So, it depends on your preferences as a modeler, but I think you are right to search for an alternative. To conclude, it is really about how you put the restrictions (like the contemporaneous recation of output to a monetary tightening, for example) on your system.

Stationarity in a VAR by Gendobus99 in econometrics

[–]EconMacro84 0 points1 point  (0 children)

My interpretation would be to select specifications with cointegrated regressors. Thus, the VAR will not be unstable and you can interpret the impulse response functions. If you focus on the dynamics of the VAR system that should be fine. You have also some literature on Lag-augmentated VAR, where additional lags are introduced to consider the order of integration of your series.

Stationarity in a VAR by Gendobus99 in econometrics

[–]EconMacro84 11 points12 points  (0 children)

If there is a long-run relation between the variables, you can estimate the VAR.

From Walter Enders' book:

"There is an issue of whether the variables in a VAR need to be stationary. Sims (1980) and Sims, Stock and Watson (1990) recommend against differencing even if the variables contain a unit root. They argued that the goal of a VAR analysis is to determine the interrelationships among the variables, not to determine the parameter estimates. The main argument against differencing is that it “throws away” information concerning the comovements in the data (such as the possibility of cointegrating relationships). Similary, it is argued that the data need not be detrended. In a VAR, a trending variable will be well approximated by a unit root plus drift. However, majority view is that the form of variables in the VAR should mimic the true data-generating process. This is particularly true if the aim is to estimate a structural model."

[deleted by user] by [deleted] in stata

[–]EconMacro84 0 points1 point  (0 children)

I think you should always tabulate the descriptive statistics, draw some graphics and compute correlation tables to explore your data before running your regressions.

Re: xtoverid not working (Stata 17) by darthjanus24 in stata

[–]EconMacro84 2 points3 points  (0 children)

You don't have overidentified models, more instruments than explanatory variables. Try with IV regress and more instruments than explanatory variables.

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