by The stability of the solutions of workhorse dynamic stochastic general equilibrium (DGSE) models is a major drawback of these models when trying to explain recessions. This article looks at the estimated linear New Keynesian model that was used in the previous discussion of the estimation of r* -- the Holston-Laubach-Williams (HLW) model. As I have noted a few times previously, more interesting behaviour can be obtained by nonlinear DSGE models. However, this flexibility comes at the cost of increasing the difficulty of fitting the model to data. There is an infinite number of models one can use to tell stories with.Models are general descriptions of possible worlds. Only data can determine how closely the possible world described by a model may be to the real world, and data determining
Topics:
Mike Norman considers the following as important:
This could be interesting, too:
Robert Vienneau writes Austrian Capital Theory And Triple-Switching In The Corn-Tractor Model
Mike Norman writes The Accursed Tariffs — NeilW
Mike Norman writes IRS has agreed to share migrants’ tax information with ICE
Mike Norman writes Trump’s “Liberation Day”: Another PR Gag, or Global Reorientation Turning Point? — Simplicius
The stability of the solutions of workhorse dynamic stochastic general equilibrium (DGSE) models is a major drawback of these models when trying to explain recessions. This article looks at the estimated linear New Keynesian model that was used in the previous discussion of the estimation of r* -- the Holston-Laubach-Williams (HLW) model.
As I have noted a few times previously, more interesting behaviour can be obtained by nonlinear DSGE models. However, this flexibility comes at the cost of increasing the difficulty of fitting the model to data. There is an infinite number of models one can use to tell stories with.
Models are general descriptions of possible worlds. Only data can determine how closely the possible world described by a model may be to the real world, and data determining this correspondence are exogenous to the model. Data gathering, processing and processing into information have their own problems.
An analogy would be a nautical chart that does not have information about reefs and shoals. The chart is fine most of the time, but.…
There is tension between model building and data that can lead to various issues set forth in the literature. One problem is that modelers often disregard this literature, if they are even aware of it, e.g., owing to cognitive-affective bias, ideology, and lack of awareness of priors such as unstated assumptions and lurking presumptions as meta-assumptions.
Why, especially in the face of much criticism and professional critiquing? This question has a variety of answers, many about the modelers, e.g., silos and group think.
Bond EconomicsSide Effects Of Stability Of DSGE Models
Brian Romanchuk