Johansen’s Approach

Background

Johansen’s approach is a pivotal methodology within econometrics used to estimate vector error correction models (VECM) in the presence of endogenous, nonstationary, and stationary variables. Developed by Søren Johansen in the late 1980s, this approach has become an essential tool for examining multiple cointegration relationships among integrated variables.

Historical Context

The development of Johansen’s approach came at a time when economists were increasingly focused on understanding the long-run equilibrium relationships between multiple time series data sets that exhibited nonstationarity. Prior analyses often failed to accommodate cases with multiple cointegration vectors, leading to the emergence of a more refined maximum likelihood estimation technique encapsulated in Johansen’s research.

Definitions and Concepts

Johansen’s approach: A maximum likelihood-based methodology to estimate a vector error correction model (VECM) that accounts for relationships among numerous endogenous variables, including both nonstationary and stationary aspects. It specializes in testing for and estimating the cointegration vectors that indicate long-term equilibrium relationships among these variables.

Key Terms:

1. Vector Error Correction Model (VECM): An econometric model incorporating both short-term adjustments and long-term equilibrium relationships among variables.
2. Endogenous Variables: Variables whose values are determined within the model.
3. Nonstationary Variables: Variables whose statistical properties change over time.
4. Stationary Variables: Variables whose statistical properties remain constant over time.
5. Cointegrating Vectors: Vectors that represent the equilibrium relationships among nonstationary variables.

Major Analytical Frameworks

Classical Economics

Johansen’s approach is not traditionally part of Classical Economics, which focuses more on microeconomic principles and doesn’t heavily involve econometric modeling techniques.

Neoclassical Economics

Neoclassical economics potentially uses Johansen’s approach to model long-term relationships within economic data effectively.

Keynesian Economics

Keynesian models, often dealing with aggregate demand and macroeconomic variables, may incorporate Johansen’s approach to assess long-term economic equilibrium.

Marxian Economics

While Marxian economics focuses on capitalist structures and class relations, applying Johansen’s approach could illuminate long-term dynamics in economic inequality and capital flows.

Institutional Economics

Institutional economists might use Johansen’s approach to examine how different institutional variables interact over time.

Behavioral Economics

Though not typically quantitative, behavioral economics could potentially benefit from Johansen’s methodology to analyze how nonstationary behavioral financial indicators co-integrate over the long term.

Post-Keynesian Economics

Johansen’s approach is compatible with Post-Keynesian econometrics to model complex, real-world economic interactions beyond simple equilibrium models.

Austrian Economics

Austrian Economics, which emphasizes qualitative over quantitative models, may find limited but useful applications for Johansen’s approach in modeling nonstationary economic process.

Development Economics

Development economics can employ Johansen’s approach to understand long-term structural changes in developing economies by identifying cointegration among key economic indicators.

Monetarism

Johansen’s approach could be utilized in monetarist models to investigate how money supply impacts long-term price levels and economic output variables.

Comparative Analysis

Compared to other cointegration methods, Johansen’s approach is sophisticated in handling systems with multiple cointegrating relationships, providing clearer insights into the long-term equilibrium dynamics within economic data.

Case Studies

Empirical research often leverages Johansen’s approach to decipher long-run equilibrium relationships in topics like:

• GDP and investment trends
• Global financial integration
• Economic indicators within regional trade blocs

Suggested Books for Further Studies

1. “Likelihood-Based Inference in Cointegrated Vector Autoregressive Models” by Søren Johansen
2. “Time Series Analysis with Applications in R” by Jonathan D. Cryer and Kung-Sik Chan
3. “Econometric Analysis of Nonstationary Data” edited by Niels Haldrup, Mika Meitz, and Pentti Saikkonen

Related Terms with Definitions

1. Augmented Dickey-Fuller (ADF) Test: A statistical test used to determine whether a unit root is present in an autoregressive model.
2. Phillips-Perron Test: An alternative to the ADF test for checking the stationarity of a time series.
3. Granger Causality: A hypothesis test to ascertain whether one time series can predict another.
4. Cointegration Test: Tests that determine whether long-run equilibrium relationships exist between time series variables.
5. Stationarity: The property of a time series whereby its statistical characteristics remain constant over time.

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