Search

High-frequency changes in interest rates around FOMC announcements are an important tool for identifying the effects of monetary policy on asset prices and the macroeconomy. However, some recent studies have questioned both the exogeneity and the relevance of these monetary policy surprises as instruments, especially for estimating the macroeconomic effects of monetary policy shocks. For example, monetary policy surprises are correlated with macroeconomic and financial data that is publicly available prior to the FOMC announcement. The authors address these concerns in two ways: First, they expand the set of monetary policy announcements to include speeches by the Fed Chair, which essentially doubles the number and importance of announcements in our dataset. Second, they explain the predictability of the monetary policy surprises in terms of the “Fed response to news” channel of Bauer and Swanson (2021) and account for it by orthogonalizing the surprises with respect to macroeconomic and financial data. Their subsequent reassessment of the effects of monetary policy yields two key results: First, estimates of the high-frequency effects on financial markets are largely unchanged. Second, estimates of the macroeconomic effects of monetary policy are substantially larger and more significant than what most previous empirical studies have found.

This paper investigates how monetary policy shock affects the stock market of the United States (US) conditional on states of investor sentiment. In this regard, we use a recently developed estimator that uses high-frequency surprises as a proxy for the structural monetary policy shocks, which in turn is achieved by integrating the current short-term rate surprises, which are least affected by an information effect, into a vector autoregressive (VAR) model as an exogenous variable. When allowing for time-varying model parameters, we find that, compared to the low investor sentiment regime, the negative reaction of stock returns to contractionary monetary policy shocks is stronger in the state associated with relatively higher investor sentiment. Our results are robust to alternative sample period (which excludes the zero lower bound) and model specification and also have important implications for academicians, investors, and policymakers.

An SVAR in US federal spending, federal revenue, and GDP is a standard setting for the study of the impact of fiscal shocks. An appealing feature of identifying a fiscal shock with an external instrument is that one can find the effects of that shock without fully identifying the SVAR. But we show that fully or almost fully instrumenting the SVAR allows one to overidentify the model by restricting the shock covariances to be zero. In this application the overidentifying restrictions are not rejected. Compared to the unrestricted case the restricted SVAR yields (a) greater precision in estimating impulse response functions and multipliers and (b) smaller estimated effects of government spending shocks on output growth.

The recent financial crisis led central banks to lower their interest rates in order to stimulate the economy until they hit the zero lower bound. How should one identify monetary policy shocks in unconventional times? Are unconventional monetary policies as effective as conventional ones? And has the monetary policy transmission mechanism changed in the zero lower bound era? This article aims at providing an overview of the econometric challenges and solutions to the identi cation of monetary policy shocks in unconventional times as well as a survey of their empirical effcts on the economy.

In empirical macroeconomics, proxy structural vector autoregressive models (SVARs) have become a prominent path towards detecting monetary policy (MP) shocks. However, in practice, the merits of proxy SVARs depend on the relevance and exogeneity of the instrumental information employed. Our Monte Carlo analysis sheds light on the performance of proxy SVARs under realistic scenarios of low relative signal strength attached to MP shocks and alternative assumptions on instrument accuracy. In an empirical application with US data we argue in favor of the specific informational content of instruments based on the dynamic stochastic general equilibrium model of Smets andWouters (2007). A joint assessment of the benchmark proxy SVAR and the outcomes of a structural covariance change model imply that from 1973 until 1979 monetary policy contributed on average between 2.2 and 2.4 units of inflation in the GDP deflator. For the so-called Volcker disinflation starting in 1979Q4, the benchmark structural model shows that the Fed's policy measures effectively reduced the GDP deflator within three years (i.e. by -3.06 units until 1982Q3). While the empirical analysis largely conditions ona small-dimensional trinity SVAR, the benchmark proxy SVAR shocks remain remarkably robust within a six-dimensional factor-augmented model comprising rich information from Michael McCracken's database (FRED-QD).

This paper develops a new method for estimating production-function parameters that can be applied in differentiated-product industries with endogenous quality and variety choice. We take advantage of data on physical quantities of outputs and inputs from the Colombian manufacturing survey, focusing on producers of rubber and plastic products. Assuming constant elasticities of substitution of outputs and inputs within firms, we aggregate from the firm-product to the firm level and show how quality and variety choices may bias standard estimators. Using real exchange rates and variation in the "bite" of the national minimum wage, we construct external instruments for materials and labor choices. We implement a simple two-step instrumental-variables method, first estimating a difference equation to recover the materials and labor coefficients and then estimating a levels equation to recover the capital coefficient. Under the assumption that the instruments are uncorrelated with firms' quality and variety choices, this method yields consistent estimates, free of the quality and variety biases we have identified. Our point estimates differ from those of existing methods and changes in our preferred productivity estimator perform relatively well in predicting future export growth.

We set up a model in which the residents of two neighboring municipalities use the services provided by public infrastructures located in both jurisdictions. The outcome is that municipalities strategically interact when investing in infrastructures, with the small municipality reacting more to the expenditure of its neighbor than the big one. This theoretical prediction is tested by estimating the determinants of the stock of public infrastructures of the municipalities belonging to the Autonomous Province of Trento in Italy. By introducing the classical spatial lag-error component, we find that municipalities positively react to an increase in infrastructures by their neighbors, but the effect vanishes above a given population threshold. Such a result is confirmed when we exploit the exogenous variation in the neighbors’ stock of infrastructures induced by a strong flood that occurred in the Province of Trento in 2000.

Structural VAR models require two ingredients: (i) Informational sufficiency, and (ii) a valid identification strategy. These conditions are unlikely to be met by small-scale recursively identified VAR models. I propose a Bayesian Proxy Factor-Augmented VAR (BP-FAVAR) to combine a large information set with an identification scheme based on an external instrument. In an application to monetary policy shocks I find that augmenting a standard small-scale Proxy VAR by factors from a large set of financial variables changes the model dynamics and delivers price responses which are more in line with economic theory. A second application shows that an exogenous increase in uncertainty affects disaggregated investment series more negatively than consumption series.

Bayesian empirical macroeconomic models are excellent tools for prediction and structural analysis. The use of a prior distribution facilitates model averaging, allows for structural identification of multiple time series models and makes estimation of high-dimensional models feasible. However, prior distributions need to be chosen carefully in order to accurately reflect the researcher's beliefs before looking at the data. I exemplify how to do so in this thesis by employing model averaging techniques in Bayesian spirit, by developing tools to express priors for structural vector-autoregressive models, and by showing a new approach to identify the impact of variations in uncertainty in a data-intensive environment. In the first chapter, which is based on joint work with Tigran Poghosyan, we use an Early Warning System (EWS) to recover leading indicators of fiscal distress events. In particular, we use Extreme Bounds Analysis (EBA), a model averaging approach introduced by Leamer (1985) and popularised by Sala-i-Martin (1997), to assess the robustness of leading indicators for fiscal distress across different models. We find that both fiscal and non-fiscal leading indicators are robust predictors of fiscal distress events. In a second step we assess the forecasting performance of an EWS based on the most robust leading indicators. We find that it offers a gain in predictive power compared to a baseline model which is based on fiscal leading indicators only. Lastly, we assess the robustness of these results across various model specifications, subsamples and estimation strategies. In the second chapter, which is based on joint work with Michele Piffer, we propose a new approach to express prior beliefs on the impulse responses of structural vector auto-regressive (SVAR) models. This approach does not restrict the family of prior distributions to a set that is technically convenient. Rather, it combines extensive flexibility in the choice of priors with an efficient importance sampler to explore the posterior distribution. We compare our new posterior sampler to a computationally more demanding generic sampler and confirm that we recover the shape of the posterior. We illustrate the approach using artificial data and in an application of sign restrictions to identify oil market shocks. We show that posterior inference is sharpened compared to the traditional approach of imposing sign restrictions and that oil supply shocks play a major role in driving oil price dynamics. In the third chapter I investigate the effects of uncertainty shocks in the spirit of Bloom (2009) using a newly developed Bayesian Proxy Factor-augmented VAR (BP-FAVAR) model. This model combines a large information set with an identification scheme based on external instruments, thereby jointly addressing informational deficiency issues and non-credible identification assumptions. I propose a new sampling algorithm exploiting the state-space representation of the model. I find that uncertainty shocks have adverse effects on the real economy and are deflationary in the short run. To recover the dynamic causal effects, the identification scheme is crucial.

This paper extends the Bayesian proxy SVAR model (BP-SVAR) of Caldara and Herbst (2019) to examine changes in the transmission of structural shocks in the presence of regime shifts in an economy. I provide a Metropolis-within-Gibbs sampling algorithm to approximate the posterior distribution of model parameters. The model is then used to examine the role of credit spreads on the transmission of monetary policy shocks in the United States between 1994-2007, where identification is achieved using a proxy constructed from high-frequency financial data. The main finding is that the effect of credit spreads differs across regime. Credit spreads significantly change the transmission of monetary policy shocks from 2000-2007 supporting Caldara and Herbst (2019), although, their inclusion appears to only alter the response of industrial production in the short-term with no other significant changes to the rest of the economy during the mid to late 1990s. This result highlights the empirical relevance of accounting for regime changes when assessing the impact of economic shocks.

In this paper we discuss general identification results for Structural Vector Autoregressions (SVARs) with external instruments, considering the case in which r valid instruments are used to identify g Ï 1 structural shocks, where r Ï g. We endow the SVAR with an auxiliary statistical model for the external instruments which is a system of reduced form equations. The SVAR and the auxiliary model for the external instruments jointly form a "larger" SVAR characterized by a particularly restricted parametric structure, and are connected by the covariance matrix of their disturbances which incorporates the "relevance" and "exogeneity" conditions. We discuss identification results and likelihood-based estimation methods both in the "multiple shocks" approach, where all structural shocks are of interest, and in the "partial shock" approach, where only a subset of the structural shocks is of interest. Overidentified SVARs with external instruments can be easily tested in our setup. The suggested method is applied to investigate empirically whether commonly employed measures of macroeconomic and financial uncertainty respond on-impact, other than with lags, to business cycle fluctuations in the U.S. in the period after the Global Financial Crisis. To do so, we employ two external instruments to identify the real economic activity shock in a partial shock approach.

Motivated by the increasing use of external instruments to identify structural vector autoregressions (SVARs), we develop algorithms for exact finite sample inference in this class of time series models, commonly known as proxy-SVARs. Our algorithms make independent draws from the normal-generalized-normal family of conjugate posterior distributions over the structural parameterization of a proxy-SVAR. Importantly, our techniques can handle the case of set identification and hence they can be used to relax the additional exclusion restrictions unrelated to the external instruments often imposed to facilitate inference when more than one instrument are used to identify more than one equation, as in Mertens and Montiel-Olea (2018).

How should one identify monetary policy shocks in unconventional times? Are unconventional monetary policies as e§ective as conventional ones? And has the transmission mechanism of monetary policy changed in the zerolower bound era? The recent Önancial crisis led Central banks to lower their interest rates in order to stimulate the economy, and interest rates in many advanced economies hit the zero lower bound. As a consequence, the traditional approach to the identiÖcation and the estimation of monetary policy faces new econometric challenges in unconventional times. This article aims at providing a broad overview of the recent literature on the identiÖcation of unconventional monetary policy shocks and the estimation of their e§ects on both Önancial as well as macroeconomic variables. Given that the prospects of slow recoveries and long periods of very low interest rates are becoming the norm, many economists believe that we are likely to face unconventional monetary policy measures often in the future. Hence, these are potentially very important issues in practice.

Despite years of research, there is still uncertainty around the effects of monetary policy shocks. We reassess the empirical evidence by combining a new identification that accounts for informational rigidities, with a flexible econometric method robust to misspecifications that bridges between VARs and Local Projections. We show that most of the lack of robustness of the results in the extant literature is due to compounding unrealistic assumptions of full information with the use of severely misspecified models. Using our novel methodology, we find that a monetary tightening is unequivocally contractionary, with no evidence of either price or output puzzles.

The toolkit of identification strategies for structural vector autoregressive (SVAR) models has been constantly expanded since their introduction by Sims (1980). Recently, the literature has introduced methods of achieving identification by combining zero and sign restrictions (Mountford and Uhlig, 2009), using the heteroscedasticity properties of the structural shocks (Rigobon, 2003; Lanne and Lütkepohl, 2008) and exploiting information contained in external instruments (Stock and Watson,2012; Mertens and Ravn, 2013). This thesis deploys those newly established methods of identification to address a variety of research questions that have arisen in the aftermath of the recent economic and financial crisis. The research questions, which fall within the domain of credit, risk and uncertainty, are addressed in four independent chapters. The first chapter, based on joint work with Christoph Große Steffen, analyzes the relationship between macroeconomic uncertainty and risk aversion, together with their role for the pricing of sovereign debt. A theoretical model of sovereign default is used to separate the effects of risk aversion and uncertainty for bond prices. We find that investors’ risk aversion is positively affected by an increase in uncertainty, pointing toward uncertainty constituting a root cause for changes in risk attitudes. Building a structural VAR identified via heteroscedasticity, we decompose credit default swaps (CDS) for Spain and Italy into three shocks: fundamental risk, risk aversion, and uncertainty. We find that shocks to macroeconomic uncertainty (1) significantly increase international investors’ risk aversion, in line with the predictions of the theoretical model; (2) have a significant and economically relevant impact on sovereign financing premia; (3) account for a share in sovereign CDS of up to 25 basis points at the onset of the European sovereign debt crisis, quantitatively comparable to the effect of increased risk aversion during this period. The second chapter, based on joint work with Anton Velinov, analyzes the impact of changes in sovereign bond holdings in the banking sector on the risk position of the sovereign. The theoretical literature is inconclusive on whether changes in bank exposure towards the domestic sovereign have an adverse effect on the sovereign risk position via a diabolic loop in the sovereign-bank nexus or reduce perceived default risk by acting as a disciplinary device for the sovereign. We empirically analyze the impact of exogenous changes in bank exposure on the risk position of the sovereign within a Markov switching structural vector autoregressive in heteroscedasticity (MSH-SVAR) framework for a set of EMU countries. We add to the methodological literature by allowing for regime dependent shock transmissions according to the volatility state of the financial system. Finding support for both, a stabilizing and a destabilizing effect, we document a clear clustering among the country sample: Rising bank exposure increased default risk for the EMU periphery, but decreased credit risk for the core EMU countries during times of financial stress. The third chapter analyzes the drivers of current account imbalances in the European Monetary Union. Against the backdrop that current account imbalances have been a decisive feature of the European banking and sovereign debt crisis, this chapter investigates the drivers of euro area current accounts — their divergence and subsequent rebalancing — within a structural model that accommodates potential regime changes upon the introduction of the common currency and the onset of the financial crisis. It is found that domestic demand shocks account for a substantial fraction in the current account deficit of EMU periphery countries in the run-up to the crisis — the mirror image of Germany’s foreign demand driven surplus. While supply side factors also explain part of the current account deficits in Italy, Spain and Portugal in the years before the crisis, shocks to price competitiveness or foreign demand played a minor role for those economies. The adjustment subsequent to the financial crisis was borne partly by a contraction in demand in the economies running deficits, but is also due to adverse supply shocks implying lower growth perspectives. The fourth chapter, based on joint work with Michele Piffer, proposes a new instrument to identify the impact of uncertainty shocks in a SVAR model with external instruments. We construct the instrument for uncertainty shocks by exploiting variations in the price of gold around selected events. The events capture periods of changes in uncertainty unrelated to other macroeconomic shocks. The variations in the price of gold around such events provide a measure correlated with the underlying uncertainty shocks, due to the perception of gold as a safe haven asset. The proposed approach improves upon the recursive identification of uncertainty shocks by not restricting only one structural shock to potentially affect all variables in the system. Replicating Bloom (2009), we find that the recursive approach underestimates the effects of uncertainty shocks and their role in driving monetary policy., Das Instrumentarium zur Identifikation von strukturellen Vektorautoregressiven Modellen ist seit deren Einführung durch Sims (1980) stetig gewachsen und zuletzt um neue Ansätze erweitert worden. Zu diesen neuen Identifikationsstrategien gehört die Kombination von Ausschluss- und Vorzeichenrestriktionen (Mountford and Uhlig, 2009), die Verwendung der Heteroskedastizitätseigenschaften in den zugrundeliegenden Daten (Rigobon, 2003; Lanne and Lütkepohl, 2008), sowie die Nutzung von externen Instrumenten (Stock and Watson, 2012; Mertens and Ravn, 2013).Diese Dissertation verwendet diese neuen Identifikationsstrategien, um in vier unabhängigen Aufsätzen Forschungsfragen zu untersuchen, die im Zuge der jüngsten Finanz- und Wirtschaftskrise auf die Forschungsagenda gerückt sind. Der erste Aufsatz basiert auf einem Fachartikel mit Christoph Große Steffen und analysiert den Zusammenhang zwischen makroökonomischer Unsicherheit und Risikoaversion sowie deren Rolle für die Bepreisung von Staatsanleihen. Im Rahmen eines theoretischen Modells zeigen wir, dass sowohl steigende Risikoaversion als auch zunehmende ökonomische Unsicherheit die Finanzierungsprämie des Staates erhöhen. Dabei steigt die Finanzierungsprämie mit der ökonomischen Unsicherheit aufgrund eines direkten Effekts, der Unsicherheitsprämie, und eines indirekten Effekts, da auch die Risikoaversion mit zunehmender Unsicherheit steigt. In einer empirischen Analyse auf der Grundlage eines strukturellen Vektorautoregressiven Modells, das über die Heteroskedastizität in den Daten identifiziert wird, zerlegen wir die Prämien von Kreditausfallversicherungen für Spanien und Italien in drei strukturelle Schocks: Fundamentales Ausfallrisiko, Risikoaversion und makroökonomische Unsicherheit. Wir zeigen, dass Unsicherheitsschocks (1) die Risikoaversion internationaler Investoren signifikant erhöhen, (2) die Finanzierungsprämien in einem ökonomisch relevanten Maßsteigern und (3) zu Beginn der Europäischen Staatsschuldenkrise zu einem Finanzierungsaufschlag von bis zu 25 Basispunkten — vergleichbar mit dem Effekt der Veränderung in der Risikoaversion in diesem Zeitraum — auf die Kreditausfallversicherung führen. Der zweite Aufsatz, der auf einem Fachartikel mit Anton Velinov basiert, untersucht empirisch den Effekt von Veränderungen in den Volumina von Staatsanleihen, die im heimischen Bankensektor gehalten werden, auf die Risikoposition des Staates. Aus der theoretischen Literatur leiten sich widersprüchliche Aussagen über den erwarteten Effekt ab: Eine zunehmende Exponiertheit des Bankensektors könnte entweder über einen Teufelskreis der Risikoansteckung zwischen Staat und Bankensektor zum Anstieg des Ausfallrisikos des Staates führen, oder aber zu einer Reduktion des Ausfallrisikos, da die Exponiertheit des heimischen Bankensektors als disziplinierendes Element auf den Staat wirkt. Zur Untersuchung des empirischen Zusammenhangs entwickeln wir ein Markov- Switching-Modell, das endogen zwischen einem Regime mit niedriger Volatilität und einem Regime mit hoher Volatilität wechselt und dabei Veränderungen in der Transmission von Schocks zwischen den Regimen zulässt. Wir dokumentieren die Existenz von destabilisierenden Effekten, die von einer zunehmenden Exponiertheit des heimischen Bankensektors ausgehen insbesondere in Spanien, Portugal und Italien, aber auch von stabilisierenden Effekten in Frankreich, Deutschland, den Niederlanden und Österreich in Phasen größerer Volatilität auf den Finanzmärkten. Der dritte Aufsatz untersucht die Treiber von Leistungsbilanzungleichgewichten in der Europäischen Währungsunion (EWU), die ein maßgebliches Merkmal der Europäischen Banken- und Staatsschuldenkrise waren. Zur Analyse entwickele ich ein strukturelles Modell für Deutschland, Italien, Spanien und Portugal, das über eine Kombination aus Ausschluss- und Vorzeichenrestriktionen identifiziert wird und den Strukturbrüchen zum Eintritt in die Währungsunion und zum Beginn der Finanz und Wirtschaftskrise von 2008/09 Rechnung trägt. Das Modell verweist darauf, dass Nachfrageschocks zu den wesentlichen Treibern der Leistungsbilanzdefizite von Italien, Spanien und Portugal gehören, während spiegelbildlich die Auslandsnachfrage ursächlich ist für die deutschen Leistungsbilanzüberschüsse in den ersten Jahren der Währungsunion. Während angebotsseitige Schocks ebenfalls eine Rolle spielen, scheinen weder exogene Veränderungen der preislichen Wettbewerbsfähigkeit noch der Auslandsnachfrage die Leistungsbilanzen in den Defizitländern belastet zu haben. Die plötzliche Anpassung der Leistungsbilanzpositionen in der Folge der Krise wurde von einer Kontraktion der Nachfrage in den Defizitländern aber auch von angebotsseitigen Schocks und entsprechend geringeren Wachstumserwartungen getragen. Der vierte Aufsatz basiert auf einem Fachartikel mit Michele Piffer. In dem Aufsatz entwickeln wir ein neues Instrument zur Identifikation von Unsicherheitsschocks in einem strukturellen Vektorautoregressiven Modell auf der Grundlage der Veränderung des Goldpreises zu ausgewählten Ereignissen. Diese Ereignisse sind so gewählt, dass sie in einem Zusammenhang mit Veränderungen ökonomischer Unsicherheit stehen, nicht aber zu anderen makroökonomischen Schocks. Wir argumentieren, dass die Veränderung des Goldpreises zu diesen Ereignissen mit den zugrundeliegenden, unbeobachtbaren Unsicherheitsschocks korreliert, da Gold unter Anlegern als sichere Anlage gilt. Die Identifikation von Unsicherheitsschocks mittels des vorgeschlagenen externen Instruments hat gegenüber dem in der Literatur verbreiteten rekursiven Identifikationsschema den Vorteil, dass sie nicht nur einem einzelnen Schock erlaubt, kontemporär sämtliche Variablen im System zu beeinflussen. Impuls-Antworten und Varianzzerlegungen der Prognosefehler verweisen darauf, dass die Effekte von Unsicherheitsschocks auf die Volkswirtschaft durch das rekursive Identifikationsschema unterschätzt werden.

We use bank-level information on lending practices from the euro area Bank Lending Survey to construct a new indicator of loans’ supply tightening controlling for both macroeconomic and bank-specific factors. Embedding this information as external instrument in a Bayesian vector autoregressive model (BVAR), we find that tighter bank loan supply to non-financial corporations leads to a protracted contraction in credit volumes and higher bank lending spreads. This fosters firms’ incentives to substitute bank loans with market finance, producing a significant increase in debt securities issuance and higher bond spreads. We also show that loans’ tightening shocks explain a large fraction of the contraction in real activity and the widening of credit spreads especially over the recession which followed the euro area sovereign debt crisis.