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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.
This book presents research that applies contemporary monetary theory and state-of-the-art econometric methods to the analysis of the monetary and financial aspects of the Indian economy and the impact of monetary policy on economic performance. Indian monetary policy has attracted significant attention from Indian and international macroeconomists over the last several years. Interest in how monetary policy influences economic performance and how monetary policy is conducted in India is growing. The prospects for further financial sector reform and ongoing inflation in India have sparked new interest in the role of money and monetary policy in India among economists, policy makers and students alike. The book should also interest economists outside India because it studies monetary economics in a major emerging market economy and makes advances in the analysis of how financial market imperfections and structural constraints influence the effects of monetary policy.
Do changes in monetary policy affect inflation and output in the East African Community (EAC)? We find that (i) Monetary Transmission Mechanism (MTM) tends to be generally weak when using standard statistical inferences, but somewhat strong when using non-standard inference methods; (ii) when MTM is present, the precise transmission channels and their importance differ across countries; and (iii) reserve money and the policy rate, two frequently used instruments of monetary policy, sometimes move in directions that exert offsetting expansionary and contractionary effects on inflation—posing challenges to harmonization of monetary policies across the EAC and transition to a future East African Monetary Union. The paper offers some suggestions for strengthening the MTM in the EAC.
Bayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. This handbook is a single source for researchers and policymakers wanting to learn about Bayesian methods in specialized fields, and for graduate students seeking to make the final step from textbook learning to the research frontier. It contains contributions by leading Bayesians on the latest developments in their specific fields of expertise. The volume provides broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing. It reviews the state of the art in Bayesian econometric methodology, with chapters on posterior simulation and Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized tools used by Bayesian time series econometricians such as state space models and particle filtering. It also includes chapters on Bayesian principles and methodology.
Time series econometrics is a rapidly evolving field. Particularly, the cointegration revolution has had a substantial impact on applied analysis. Hence, no textbook has managed to cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume. The methods are sketched out, reminding the reader of the ideas underlying them and giving sufficient background for empirical work. The treatment can also be used as a textbook for a course on applied time series econometrics. Topics include: unit root and cointegration analysis, structural vector autoregressions, conditional heteroskedasticity and nonlinear and nonparametric time series models. Crucial to empirical work is the software that is available for analysis. New methodology is typically only gradually incorporated into existing software packages. Therefore a flexible Java interface has been created, allowing readers to replicate the applications and conduct their own analyses.
The past several years of recession and slow recovery have raised much interest on the effect of fiscal stimulus on economic activity, even as high public debts in many countries would call for fiscal consolidation. To evaluate the delicate balance between stimulus and consolidation requires measuring the size of fiscal multipliers, which often depends on having quarterly data so that exogenous fiscal policy shocks can be identified. We estimate fiscal multipliers using a novel methodology for identifying fiscal shocks within a structural vector autoregressive approach using annual data while controling for debt feedback effects. The estimation focuses on regions with scarce quarterly data (mostly low-income countries), and uses results for advanced economies, emerging market countries, and other broad groupings for which alternative estimates are available to validate the methodology. Differently from advanced and emerging market economies, fiscal consolidation in low-income countries has only a small temporary negative effect on growth while raising medium-term output. Shifting the composition of public spending toward capital expenditure further supports long-run growth.
This valuable text provides a comprehensive introduction to VAR modelling and how it can be applied. In particular, the author focuses on the properties of the Cointegrated VAR model and its implications for macroeconomic inference when data are non-stationary. The text provides a number of insights into the links between statistical econometric modelling and economic theory and gives a thorough treatment of identification of the long-run and short-run structure as well as of the common stochastic trends and the impulse response functions, providing in each case illustrations of applicability. This book presents the main ingredients of the Copenhagen School of Time-Series Econometrics in a transparent and coherent framework. The distinguishing feature of this school is that econometric theory and applications have been developed in close cooperation. The guiding principle is that good econometric work should take econometrics, institutions, and economics seriously. The author uses a single data set throughout most of the book to guide the reader through the econometric theory while also revealing the full implications for the underlying economic model. To test ensure full understanding the book concludes with the introduction of two new data sets to combine readers understanding of econometric theory and economic models, with economic reality.
This volume focuses on recent developments in the use of structural econometric models in empirical economics. The first part looks at recent developments in the estimation of dynamic discrete choice models. The second part looks at recent advances in the area empirical matching models.
This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields.
This book is designed for self study. The reader can apply the theoretical concepts directly within R by following the examples.