Advances in Using Vector Autoregressions to Estimate Structural Magnitudes
Christiane Baumeister, James D. Hamilton
Econometric Theory,
im Erscheinen
Abstract
This paper surveys recent advances in drawing structural conclusions from vector autoregressions (VARs), providing a unified perspective on the role of prior knowledge. We describe the traditional approach to identification as a claim to have exact prior information about the structural model and propose Bayesian inference as a way to acknowledge that prior information is imperfect or subject to error. We raise concerns from both a frequentist and a Bayesian perspective about the way that results are typically reported for VARs that are set-identified using sign and other restrictions. We call attention to a common but previously unrecognized error in estimating structural elasticities and show how to correctly estimate elasticities even in the case when one only knows the effects of a single structural shock.
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Financial Technologies and the Effectiveness of Monetary Policy Transmission
Iftekhar Hasan, Boreum Kwak, Xiang Li
European Economic Review,
January
2024
Abstract
This study investigates whether and how financial technologies (FinTech) influence the effectiveness of monetary policy transmission. We use an interacted panel vector autoregression model to explore how the effects of monetary policy shocks change with regional-level FinTech adoption. Results indicate that FinTech adoption generally mitigates the transmission of monetary policy to real GDP, consumer prices, bank loans, and housing prices, with the most significant impact observed in the weakened transmission to bank loan growth. The relaxed financial constraints, regulatory arbitrage, and intensified competition are the possible mechanisms underlying the mitigated transmission.
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Fiscal Policy under the Eyes of Wary Bondholders
Ruben Staffa, Gregor von Schweinitz
IWH Discussion Papers,
Nr. 26,
2023
Abstract
This paper studies the interaction between fiscal policy and bondholders against the backdrop of high sovereign debt levels. For our analysis, we investigate the case of Italy, a country that has dealt with high public debt levels for a long time, using a Bayesian structural VAR model. We extend a canonical three variable macro mode to include a bond market, consisting of a fiscal rule and a bond demand schedule for long-term government bonds. To identify the model in the presence of political uncertainty and forward-looking investors, we derive an external instrument for bond demand shocks from a novel news ticker data set. Our main results are threefold. First, the interaction between fiscal policy and bondholders’ expectations is critical for the evolution of prices. Fiscal policy reinforces contractionary monetary policy through sustained increases in primary surpluses and investors provide incentives for “passive” fiscal policy. Second, investors’ expectations matter for inflation, and we document a Fisherian response of inflation across all maturities in response to a bond demand shock. Third, domestic politics is critical in the determination of bondholders’ expectations and an increase in the perceived riskiness of sovereign debt increases inflation and thus complicates the task of controlling price growth.
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The Importance of Credit Demand for Business Cycle Dynamics
Gregor von Schweinitz
IWH Discussion Papers,
Nr. 21,
2023
Abstract
This paper contributes to a better understanding of the important role that credit demand plays for credit markets and aggregate macroeconomic developments as both a source and transmitter of economic shocks. I am the first to identify a structural credit demand equation together with credit supply, aggregate supply, demand and monetary policy in a Bayesian structural VAR. The model combines informative priors on structural coefficients and multiple external instruments to achieve identification. In order to improve identification of the credit demand shocks, I construct a new granular instrument from regional mortgage origination.
I find that credit demand is quite elastic with respect to contemporaneous macroeconomic conditions, while credit supply is relatively inelastic. I show that credit supply and demand shocks matter for aggregate fluctuations, albeit at different times: credit demand shocks mostly drove the boom prior to the financial crisis, while credit supply shocks were responsible during and after the crisis itself. In an out-of-sample exercise, I find that the Covid pandemic induced a large expansion of credit demand in 2020Q2, which pushed the US economy towards a sustained recovery and helped to avoid a stagflationary scenario in 2022.
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Advances in Using Vector Autoregressions to Estimate Structural Magnitudes
Christiane Baumeister, James D. Hamilton
Abstract
This paper discusses drawing structural conclusions from vector autoregressions. We call attention to a common error in estimating structural elasticities and show how to correctly estimate elasticities even in the case when one knows only the effects of a single structural shock and the covariance matrix of the reduced-form residuals. We describe the traditional approach to identification as a claim to have exact prior information about the structural model and propose Bayesian inference as a way to acknowledge that prior information is imperfect or subject to error. We raise concerns about the way that results are typically reported for VARs that are set-identified using sign and other restrictions.
Artikel Lesen
Financial Technologies and the Effectiveness of Monetary Policy Transmission
Iftekhar Hasan, Boreum Kwak, Xiang Li
Abstract
This study investigates whether and how financial technologies (FinTech) influence the effectiveness of monetary policy transmission. We use an interacted panel vector autoregression model to explore how the effects of monetary policy shocks change with regional-level FinTech adoption. Results indicate that FinTech adoption generally mitigates the transmission of monetary policy to real GDP, consumer prices, bank loans, and housing prices, with the most significant impact observed in the weakened transmission to bank loan growth. The relaxed financial constraints, regulatory arbitrage, and intensified competition are the possible mechanisms underlying the mitigated transmission.
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Should Forecasters Use Real‐time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence
Katja Heinisch, Rolf Scheufele
German Economic Review,
Nr. 4,
2019
Abstract
In this paper, we investigate whether differences exist among forecasts using real‐time or latest‐available data to predict gross domestic product (GDP). We employ mixed‐frequency models and real‐time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real‐time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.
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U.S. Monetary-Fiscal Regime Changes in the Presence of Endogenous Feedback in Policy Rules
Yoosoon Chang, Boreum Kwak
Abstract
We investigate U.S. monetary and fiscal policy regime interactions in a model, where regimes are determined by latent autoregressive policy factors with endogenous feedback. Policy regimes interact strongly: Shocks that switch one policy from active to passive tend to induce the other policy to switch from passive to active, consistently with existence of a unique equilibrium, though both policies are active and government debt grows rapidly in some periods. We observe relatively strong interactions between monetary and fiscal policy regimes after the recent financial crisis. Finally, latent policy regime factors exhibit patterns of correlation with macroeconomic time series, suggesting that policy regime change is endogenous.
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Should Forecasters Use Real-time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence
Katja Heinisch, Rolf Scheufele
Abstract
In this paper we investigate whether differences exist among forecasts using real-time or latest-available data to predict gross domestic product (GDP). We employ mixed-frequency models and real-time data to reassess the role of survey data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real-time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.
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Monetary-fiscal Policy Interaction and Fiscal Inflation: A Tale of Three Countries
Martin Kliem, Alexander Kriwoluzky, Samad Sarferaz
European Economic Review,
2016
Abstract
We study the impact of the interaction between fiscal and monetary policy on the low-frequency relationship between the fiscal stance and inflation using cross-country data from 1965 to 1999. In a first step, we contrast the monetary–fiscal narrative for Germany, the U.S., and Italy with evidence obtained from simple regression models and a time-varying VAR. We find that the low-frequency relationship between the fiscal stance and inflation is low during periods of an independent central bank and responsible fiscal policy and more pronounced in times of non-responsible fiscal policy and accommodative monetary authorities. In a second step, we use an estimated DSGE model to interpret the low-frequency measure structurally and to illustrate the mechanisms through which fiscal actions affect inflation in the long run. The findings from the DSGE model suggest that switches in the monetary–fiscal policy interaction and accompanying variations in the propagation of structural shocks can well account for changes in the low-frequency relationship between the fiscal stance and inflation.
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