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Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.
Dynamic factor models and external instrument identification are two recent advances in the empirical macroeconomic literature. This paper combines the two approaches in order to study the effects of monetary policy shocks. I use this novel framework to re-examine the effects found by Forni and Gambetti (2010, JME) in a recursively-identified DFM. Considering the fundamental differences between the identifying assumptions, the results are overall strikingly similar. Importantly, this finding stands in stark contrast to traditional VAR models, which yield decisively different results in the two identification schemes. This highlights the importance of using extended information sets to properly identify monetary policy shocks.
Research Paper (undergraduate) from the year 2017 in the subject Mathematics - Applied Mathematics, grade: 8.5, , course: Empirical Econometrics II, language: English, abstract: This paper investigates the effects of monetary policy in the US by comparing a system of equations – estimated from a VECM (vector error correction model) – to a SVAR (structural autoregressive) model. Vector error-correction models are used when there exists long-run equilibrium relation-ships between non-stationary data integrated of the same order. Those models imply that the stationary transformations of the variables adapt to disequilibria between the non-stationary variables in the model. In contrast, SVAR models focus on the contemporaneous interdependence between the variables. The authors apply these two methods on a model with a contractionary monetary policy which affects the short-term interest rate. Following Sims and Zha the authors use a shock to the Treasury Bill rate instead of a shock to the Federal Funds rate. The paper continues as follows. First, a description of the data is given. Secondly, it presents a system of equations built from the LSE approach, aiming at macroeconomic simulations. Thirdly, it compares results obtained from the previous part to those obtained using SVAR impulse response functions (IRFs) identified with sign restrictions. The paper focuses on the impact of the simulated policies or monetary shocks on GDP and its growth rate.
In this book leading German econometricians in different fields present survey articles of the most important new methods in econometrics. The book gives an overview of the field and it shows progress made in recent years and remaining problems.
We show how to study time-varying dynamic causal effects of structural shocks using external instruments in a generalized Factor-Augmented-VAR(FAVAR) model with time-varying parameters and stochastic volatility. Specifically, we employ the Bayesian MCMC estimation methodology and focus on global factors of outputs, inflation rates, interest rates, and exchange rates in five representative advanced economies, namely the United States, Canada, Germany, Japan, and the United Kingdom, to study time-varying impacts of an exogenous U.S. monetary policy shock on these open economies. We find uniformly strong evidence over time in support of Dornbusch's theoretical prediction of the exchange rate overshooting in response to an exogenous monetary policy shock. The "delayed overshooting puzzle" commonly documented in the literature disappears, likely thanks to the better identification of the exogenous U.S. monetary policy shock via external instruments. We also find that the U.S. monetary policy shock has significant contributions to the dynamics of the exchange rates overall, and the contributions were particularly large during the period of global financial crisis in 2007-2008. We document a great deal of time variations of impacts of the U.S. monetary policy shock on global factors of all other economic variables including interest rates, inflation rates, and outputs. These empirical results lend strong support to the extension of the baseline model to the more general model with time-varying parameters and stochastic volatility.
News - or foresight - about future economic fundamentals can create rational expectations equilibria with non-fundamental representations that pose substantial challenges to econometric efforts to recover the structural shocks to which economic agents react. Using tax policies as a leading example of foresight, simple theory makes transparent the economic behavior and information structures that generate non-fundamental equilibria. Econometric analyses that fail to model foresight will obtain biased estimates of output multipliers for taxes; biases are quantitatively important when two canonical theoretical models are taken as data generating processes. Both the nature of equilibria and the inferences about the effects of anticipated tax changes hinge critically on hypothesized information flows. Different methods for extracting or hypothesizing the information flows are discussed and shown to be alternative techniques for resolving a non-uniqueness problem endemic to moving average representations.