Understanding Major US Recessions Through the Lens of a DSGE Model ∗

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Understanding Major US Recessions Through the Lens of a DSGE Model ∗ Understanding Major US Recessions through the Lens of a DSGE Model ∗ Luis Herreraa;b Jesús Vázquezb February 9, 2021 Abstract We assess the ability of a standard medium-scale DSGE model augmented with financial fric- tions to describe US recessions. We first estimate the DSGE model using a Bayesian approach for three alternative periods, each containing a major US recession: the Great Depression, the Stagfla- tion and the Great Recession. Then, we assess the stability of structural parameters, and analyze what frictions were particularly important and what shocks were the main drivers of aggregate fluctuations in each historical period. We find that the estimated DSGE model is able to provide a sound explanation of all three recessions that is in line with a narrative interpretation of them, and that both estimated shocks and frictions are linked to well known economic events. Our estimation results strongly support the use of medium-scale DSGE models for assessing (ex-post) economic analysis of aggregate fluctuations. JEL classification: E30, E44 Keywords: Medium-scale DSGE model, Great Depression, Stagflation, Great Recession a IRES, Université catholique de Louvain b Universidad del País Vasco (UPV/EHU). Corresponding author: [email protected] ∗The first author acknowledges financial support from the Spanish Ministry of Science, Innovation and Universities under scholarship grant FPU17/06331. The second author’s research was supported by the Spanish Ministry of Economy and Competition and the Basque Government under grant numbers ECO2016-78749P and IT-1336-19, respectively. 1 A Model-Based Comparison of the Three Major Recessions in the US 1 Introduction New Keynesian DSGE models are currently widely used to explain macroeconomic dynamics and assessing economic policy. They are often considered to be the workhorses of modern macroeco- nomics. Nevertheless, the adequacy of DSGE models has been regarded with skepticism in recent times. For instance, Romer (2016) has recently set out a frontal critique of macroeconomics based on these models. One of his main criticisms is the explanation of aggregate fluctuations driven by exogenous shocks, which he calls “imaginary causal forces”. This term can arguably be viewed as a description of what shocks really are: The black boxes of DSGE models. All events that affect macroeconomic variables that are not explicitly modeled are captured by shocks in these models. In principle, this can be a problem when it comes to explaining what shocks are really capturing. However, macroeconomists do not currently know any parsimonious approach for introducing these specific events into models since their inclusion would at the very least make the model intractable. Does this critique mean that we should discard the use of DSGE models? Our answer is a definite no, but further efforts are needed to understand what estimated shocks from DSGE models are truly capturing: Structural shocks (e.g. oil shocks, legislation changes, monetary policy shifts) that match recognizable economic events rather than imaginary forces that allow the model to fit the data. The main contribution of this paper is to provide a sound matching between shock realizations and their dynamics effects with well-known historical events. The proven ability of a standard DSGE model with financial frictions to explain alternative and very concrete economic events serve as a defense of the role of these kind of models not only to characterize the features of the business cycle, but also as a tool to identify the importance of these events at very specific points in time. Thus, the identification of the nominal and real rigidities in addition to the identification of shock realizations for each quarter allows us to derive a historical variance decomposition of real output that turns out to be rather precise in explaining the fluctuations of the selected periods featuring major recessions. The aim is not to suggest a model that best fit each period, but provide evidence of the strength of a standard DSGE model in capturing recognizable economic events, which helps to shed light on the “black boxes” described as structural shocks in DSGE models. This paper studies the periods surrounding three major US recessions using the medium-scale DSGE model of Smets and Wouters (2007) augmented with the financial accelerator of Bernanke, Gertler and Gilchrist (1999).1;2 We consider this specification since we want to keep the model as standard as possible and versions of this model have been extensively used in the related litera- ture (see, among others, Christiano, Motto and Rostagno, 2003, 2014; De Graeve, 2008; Villa 2013; Rychalowska, 2016). 3 Thus, we consider the Great Depression, whose cause has usually been associ- 1Del Negro and Schorfheide (2013) show that this DSGE model with financial frictions would have done a much better job forecasting the dynamics of real GDP growth and inflation during the Great Recession than the DSGE model without financial frictions. 2We do not consider the current recession driven by Covid-19 as analyzed in recent studies (e.g. Boscá, Doménech and Ferri, 2020; Costa-Junior, Garcia-Cintado and Junior, 2021) because, as emphasized below, our goal is to study samples featuring a major recession, but where the samples also include the period prior to a recession as well as its aftermath. However, the aftermath of the Coronavirus recession will take place hopefully in the near future. 3In order to assess the robustness of the empirical findings, we have also estimated an alternative specification for the financial frictions augmenting the DSGE model. Thus, we have considered the Gertler and Karadi (2011) approach to include financial frictions instead of the Bernanke, Gertler and Gilchrist’s financial accelerator considered in the benchmark DSGE model. Moreover, we have estimated an alternative specification where monetary policy reacts to the money stock and an explicit money demand decision is introduced in the model. Our estimation results from all these alternative specifications 2 A Model-Based Comparison of the Three Major Recessions in the US ated with a mixture of financial shocks and wrongly conducted monetary policy (Christiano, Motto and Rostagno, 2003); the Stagflation, which has been widely interpreted as a consequence of the oil supply quotas introduced by the OPEC cartel and a lax monetary policy as discussed in intermediate macroeconomics textbooks (e.g. Abel and Bernanke, 1998, p. 433) and in the context of DSGE mod- els (Smets and Wouters, 2007);4 and the Great Recession, which is usually viewed as a crisis driven mainly by financial shocks (Christiano, Motto and Rostagno, 2014). We consider these three periods for two main reasons. First, each of these periods includes a major US recession. Second, these three periods have been widely studied and some consensus about the sources causing each recession has been achieved. Therefore, this empirical analysis allows us to assess whether the shocks generating the business cycle during these three specific periods are really capturing actual economic events that took place at the time rather than uninformative black boxes (i.e. the ‘imaginary causal forces’ in Paul Romer’s words). 5 We examine the role played by the structural shocks that affected the economy in each sample period. We appraise our results by studying the historical empirical evidence and other empirical findings reported in the related literature. Indeed, the estimated DSGE model captures well the sources causing each recession period that are in line with other studies. We find that the shocks that mainly caused the Great Depression are (i) the risk premium shock capturing the burst of the finan- cial bubble, (ii) monetary policy shocks representing shifts in (an inappropriate monetary) policy; and (iii) the wage and price markup shocks associated with important changes in legislation which, for instance, allowed firms to reach collusive price agreements (the 1933 NIRA Act), and allowed workers to organize themselves into unions independently from their employers (the 1935 Wagner Act). These results are in line with several studies like White (1990) and Romer (1990) who point out the financial instability as crucial in this huge recession, Christiano, Motto and Rostagno (2003) who argue that a more accommodative monetary policy could have mitigated the Great Depression, and Weinstein (1980) and Cole and Ohanian (2004) who suggest that the legislation changes in the labor and good markets (i.e. New Deal policies) played an important role in the weak recovery during the Great Depression.6 The estimated price markup shock identifies the oil crisis as the main driving force of the Stagfla- tion. Nevertheless, we also find that the monetary policy conducted and the overall increase in wages also played crucial roles in this recession. Thus, the weak response of monetary policy to high infla- tion may have exacerbated the effects of these shocks in the economy. Moreover, there was resilience to the cutting of real wages of unskilled workers through changes in the minimum wage laws. Fur- thermore, there was an increase in the wage gap between skilled and unskilled workers as a result of show the robust ability of the DSGE model to match recognizable economics events. 4This traditional view of the Stagflation has been challenged by Barsky and Killian (2002). They argue that the Stagflation of the 1970s could have been avoided to a large extent, should the Fed not implemented the major monetary expansions in the early 1970’s. The discusssions of Olivier Blanchard and Allan Blinder on Barsky and Killian’s article—which are published in the same volume of the NBER Macroeconomics Annual, 2001— critize Barsky and Killian’s monetary explanation of the sources of the Stagflation on several grounds. See also the comments and remarks raised by a few prominent macroeconomists, which were published in the same volume. 5DSGE models are frequently estimated for long sample periods, therefore, the consideration of shorter samples that in- clude rather large shocks is challenging for the model.
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