Labor-Market Heterogeneity, Aggregation, and Policy (In)variance of Dsge Model Parameters

Chang, Yongsung; Kim, Sun Bin; Schorfheide, Frank

doi:10.1111/j.1542-4774.2012.01098.x

Cited by 41 publications

(38 citation statements)

References 74 publications

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“…The method of approximating the expected utility of households through the weighted average of the variances of selected variables has a long tradition in the macroeconomic literature. 15 This method has also been used by policymakers as a means to communicate policy objectives to the general public (Evans, 2011). One outstanding characteristic of the Equation (7) is that it includes interest rate smoothing as one of the objectives.…”

Section: Conventional Monetary Policy Rule ("Taylor Rule")mentioning

confidence: 99%

How costly is a misspecified credit channel DSGE model in monetary policymaking?

Yagihashi

2018

Economic Modelling

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In general, misspecified models can cause harm through suggesting policies that would in reality lead to destabilization of the economy as well as through limiting policy options and thus preventing the policymaker from achieving the first-best outcome. This paper specifically examines whether the choice of credit market frictions in new-Keynesian DSGE models matters in the context of monetary policymaking. We conduct a simulation exercise in which a financial crisis occurs and the monetary policymaker shifts its policy to combat the crisis. In choosing the new policy, the policymaker uses a credit channel model that suffers from a misspecification in the type of credit market friction. We find that the policymaker would choose a policy that not only destabilizes the economy but also performs considerably worse relative to an alternative policy that would have been chosen under a correctly specified model. We further demonstrate that allowing the use of public financial intermediation as a policy option plays a key role in the above result. Finally, we show that using a more flexible policy rule specification or reassigning stabilization objectives can mitigate the problem of model misspecification with varying success.

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Section: Conventional Monetary Policy Rule ("Taylor Rule")mentioning

confidence: 99%

How costly is a misspecified credit channel DSGE model in monetary policymaking?

Yagihashi

2018

Economic Modelling

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“…Dueker et al (2007), Fernandez-Villaverde and Rubio-Ramirez (2007), Canova (2009), Liu et al (2011), Vavra (2008), Dew-Backer (2014), Meier and Sprengler (2015), Seoane (2016), Castelnuovo and Pellegrino (2018), among others, have shown that the parameters of dynamic stochastic general equilibrium (DSGE) models are not time invariant and that variations are small but persistent. Parameter variations do not necessarily imply that DSGE models are not structural (see, e.g., Cogley and Yagihashi, 2010, Chang et al, 2013, Schmitt-Grohe and Uribe, 2003, Hansen and Sargent, 2010, and Cogley et al, 2015, but they create concerns about the economic interpretation of the results.…”

Section: Introductionmentioning

confidence: 99%

Detecting and Analyzing the Effects of Time‐varying Parameters in Dsge Models

Canova

Ferroni

Matthes

2020

Int Economic Review

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We study how structural parameter variations affect the decision rules and economic inference. We provide diagnostics to detect parameter variations and to ascertain whether they are exogenous or endogenous. A constant parameter model poorly approximates a time‐varying data generating process (DGP), except in a handful of relevant cases. Linear approximations do not produce time‐varying decision rules; higher‐order approximations can do this only if parameter disturbances are treated as decision rule coefficients. Structural responses are time invariant regardless of order of approximation. Adding endogenous variations to the parameter controlling leverage in Gertler and Karadi's model substantially improves the fit of the model.

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“…This approach provides intuition on a plethora of phenomena, and is the basis of the most elaborate computational Dynamic Stochastic General Equilibrium (DSGE) models that are used in practice for policy analysis [3]. Yet, its conceptual [4] and practical [5] shortcomings have been repeatedly pointed out.…”

Section: Introductionmentioning

confidence: 99%

Statistical mechanics of complex economies

2017

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Abstract. In the pursuit of ever increasing efficiency and growth, our economies have evolved to remarkable degrees of complexity, with nested production processes feeding each other in order to create products of greater sophistication from less sophisticated ones, down to raw materials. The engine of such an expansion have been competitive markets that, according to General Equilibrium Theory (GET), achieve efficient allocations under specific conditions. We study large random economies within the GET framework, as templates of complex economies, and we find that a non-trivial phase transition occurs: the economy freezes in a state where all production processes collapse when either the number of primary goods or the number of available technologies fall below a critical threshold. As in other examples of phase transitions in large random systems, this is an unintended consequence of the growth in complexity. Our findings suggest that the Industrial Revolution can be regarded as a sharp transition between different phases, but also imply that well developed economies can collapse if too many intermediate goods are introduced.

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Labor-Market Heterogeneity, Aggregation, and Policy (In)variance of Dsge Model Parameters

Cited by 41 publications

References 74 publications

How costly is a misspecified credit channel DSGE model in monetary policymaking?

How costly is a misspecified credit channel DSGE model in monetary policymaking?

Detecting and Analyzing the Effects of Time‐varying Parameters in Dsge Models

Statistical mechanics of complex economies

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