1 Introduction
In the mid 70’s people started to doubt the validity of macroeconomic models as they were not able to forecast the worldwide recession due to the oil-price shock. These models needed an a priori seperation into endogenous or exogenous variables. This need for seperation was criticized by Sims (1980), who proposed as solution for this problem a Vector Autoregressive model (VAR). ”A VAR is an n-equation, n-variable linear model in which each variable is in turn explained by its own lagged values, plus current and past values of the remaining n-1 variables.”1 This offers the possibility that these variables influence each other mutually, which makes each of them endogenous.2
Let us put some economic background to these definitions. As we focus on monetary policy, we might be interested in the mutual relation and behaviour of the interest rate (r) and inflation (). For simplicity we just take these two variables with one lag into account. According to our setup we yield the following equation system:
(Die Formeln sind nur in der Download-Version verfügbar)
The equation system of (1) and (2) is called the primitive system, where (Ert) and (E(pi)t) are uncorrelated white noise disturbances.
2 Building a recursive VAR
In this section we will build a recursive VAR step by step. In the first section the primitive system will be transformed into the reduced form VAR. Building on that, we will focus on the identification of the primitive system, which finally will yield us the recursive VAR. Finally we abandon the exogenous assumption about the lag-length which we set for simplicity to one. This will let us end up with the methodology, which will enable us to apply the VAR toolkit to a real world situation.
Table of Contents
1 Introduction
2 Building a recursive VAR
2.1 From the primitive system to the reduced form VAR
2.2 Identification and the recursiveness aproach
2.3 Lag-lenght selection criteria
3 Application of the recursive VAR toolkit
3.1 Data description
3.2 The transmission mechanism of monetary policy
3.3 The price puzzle
3.4 Estimation of the model, determination of monetary shocks and impulse response functions
4 Conclusion
5 Appendix
Objectives and Topics
The primary objective of this seminar paper is to determine and analyze the impact of monetary policy shocks using a recursive vector autoregressive (VAR) model. By extending existing datasets and applying the VAR toolkit to US macroeconomic data, the work aims to identify past monetary shocks and examine how variables such as output, inflation, and commodity prices react to changes in the federal funds rate.
- Theoretical derivation and technical development of recursive VAR models.
- Implementation of quantitative lag-length selection criteria (AIC and SIC).
- Empirical analysis of the monetary policy transmission mechanism.
- Investigation and mitigation of the "price puzzle" through the inclusion of commodity prices.
- Estimation of impulse response functions to evaluate macroeconomic reactions to interest rate shocks.
Excerpt from the Book
3.3 The price puzzle
One might by now wonder, why we include commodity prices in the model, as we have not thought about any reason or theoretical foundation for this yet. Fact is that the inclusion of commodity prices lacks a theoretical rationale. However, it has become common practice to solve the so called price puzzle by including commodity prices in the VAR.
The price puzzle itself is defined as ”the positive response of the price level to a monetary tightening”. To limit the extent of the puzzle, which used to emerge in early VARs, which did not take forward looking variables into account, Sims (1992) proposed the inclusion of commodity prices. The rationale behind this is as follows: ”In the real world, central banks take in particular leading indicators into consideration, when setting the interest rate. Simple VARs omit variables that could be used to predict future inflation.” If this is the case, the central bank’s information set is modelled incorrectly, which has substantial consequences for the VAR parameters and its forecasts. By including commodity prices we extent the central bank’s information set by a leading indicator of inflation.
Summary of Chapters
1 Introduction: Discusses the historical context of macroeconomic model limitations and introduces Vector Autoregressive (VAR) models as a solution for endogenous variable relationships.
2 Building a recursive VAR: Details the mathematical transformation from a primitive system to a reduced form VAR and establishes identification restrictions and lag-length selection methods.
3 Application of the recursive VAR toolkit: Applies the VAR methodology to US economic data, models the monetary policy transmission mechanism, and utilizes commodity prices to address the price puzzle.
4 Conclusion: Summarizes the effectiveness of the VAR approach and confirms that results remain consistent even when extending the observation period.
5 Appendix: Provides supplemental visual documentation regarding Boivin and Giannoni’s impulse response functions.
Keywords
Monetary Policy, Recursive VAR, Vector Autoregressive Model, Policy Shocks, Price Puzzle, Federal Funds Rate, Macroeconomic Aggregates, Impulse Response Functions, Identification, Lag-length Selection, Inflation, Commodity Prices, Central Bank, Monetary Transmission, Econometrics
Frequently Asked Questions
What is the core focus of this research paper?
The paper focuses on identifying and assessing the effects of monetary policy shocks using a recursive vector autoregressive (VAR) model.
What are the central thematic areas covered?
The paper covers the technical construction of VAR models, the transmission mechanism of monetary policy, the identification of policy shocks, and the empirical application to US economic data.
What is the primary research goal?
The goal is to determine the impact of monetary policy shocks on macroeconomic aggregates and to test the robustness of these models by extending existing datasets.
Which scientific methodology is employed?
The author uses a recursive Vector Autoregressive (VAR) methodology, including Choleski decomposition for identification and standard information criteria for lag selection.
What topics are discussed in the main section?
The main section deals with model building, the rationale for including commodity prices to solve the price puzzle, and the estimation of impulse response functions.
Which keywords best characterize this work?
Key terms include Recursive VAR, Monetary Policy Shocks, Price Puzzle, Federal Funds Rate, and Impulse Response Functions.
Why is the inclusion of commodity prices necessary in this model?
Commodity prices are included to serve as a leading indicator of inflation, which helps mitigate the "price puzzle"—an empirical anomaly where inflation appears to rise following a monetary tightening.
How do the results of this paper compare to existing literature?
The author concludes that their results are qualitatively and quantitatively consistent with the findings of Boivin and Giannoni (2005), confirming the validity of the approach even with a more recent dataset.
- Quote paper
- Felix Miebs (Author), 2006, Which are the Effects of Monetary Policy? Identifying Policy Shocks in recursive VARs, Munich, GRIN Verlag, https://www.grin.com/document/71446
