Evaluating the Transmission Mechanism of Monetary Policy in Jamaica:

A Factor-Augmented Vector Autoregressive (FAVAR) Approach with Time Varying

Coefficients

Authors: Wayne Robinson

Carey-Anne Williams

Presenter: Carey-Anne Williams

Research Services Department

Bank of Jamaica

1

Outline of the Presentation

• Motivation

• Introduction & Conceptual Framework

• Results

• Conclusion and Policy Implications

• Appendix

2

Motivation

3

Motivation

• Assess whether there has been any substantial change in the transmission mechanism in light of the major economic developments since 2010.

• Extend previous studies for Jamaica which utilized a limited information set in a VAR framework.

• Objectives:

• Assess the impact of monetary policy shocks on macroeconomic variables in Jamaica using TV-FAVAR model

• Determine whether the transmission mechanism has evolved

4

Introduction & Conceptual Framework

5

What is the Transmission Mechanism?

• The monetary transmission mechanism is the process through which monetary policy actions affect the economy in general and inflation in particular.

• Its precise definition varies according to the structure of the economy and across business cycles.

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7

Exchange Rate Anchor

Currency Board* ECCU

Anguilla

Antigua & Barbuda

Dominica

Grenada

Montserrat

St. Kitts & Nevis

St. Lucia

St. Vincent & the Grenadines

Conventional Peg* Aruba

The Bahamas

Barbados

Belize

Curacao and Sint Maarten

Stabilized Arrangement* Suriname

Trinidad and Tobago

Managed Float Jamaica

Monetary Policy Frameworks across the Caribbean

Features 0f the Transmission Mechanism

Domestic interest rates respond to external policy shocks

*Source: Annual Report on Exchange Arrangements and Exchange Restrictions, IMF 2014

Domestic interest rates respond somewhat to external policy shocks

Domestic interest rates respond to external policy shocks

Nominal prices adjust to absorb shocks

High degree of price convergence across member states

Monetary policy independence

Limited monetary policy independence

Nominal prices adjust more frequently to absorb shocks

How Do Changes in the Policy Rate Typically

Impact Inflation?

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• The two channels that are typically significant in small open economies are the exchange rate and credit channels.

• In Jamaica the dominant channel is the exchange rate channel.

What are we trying to explain?

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Modelling Changes in the Transmission Mechanism

• Three main approaches: • Estimating the model over subsamples (Clarida et al (2000))

• Modelling the change in the coefficients in the VAR using structural breaks or a Markov switching regime (Stock & Watson (1996), Sims & Zha (2006)).

• Estimating a time-varying VAR (TV-VAR) in which the coefficient change follows a random walk (Primiceri (2005), Canova et al (2007), Baumeister & Benati (2010), Bianchi et Al (2009), Mumtaz & Sunder-Plassmann (2010)).

• Korobolis (2009), Moussa (2010) and Mumtaz (2010), among others, extended the TV-VAR models to include factor analysis.

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Conceptual and Theoretical Framework

• This paper extends the work of Allen and Robinson (2004) by assessing the impact of monetary policy shocks on macroeconomic variables in Jamaica using TV-FAVAR model.

• The model includes a large data set of variables typically monitored by the Central Bank. The data set therefore includes indicators for economic activity, inflation, money, credit and equity prices.

• Common factors are extracted from 65 quarterly Jamaican macroeconomic indicators spanning 1998Q1 - 2013Q1 using principal component analysis.

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Results

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Impulse Response Analysis

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Conclusion and Policy Implications

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Conclusion The transmission mechanism has evolved over the last two decades. The results suggest that:

• Volatility in inflation has declined since 2010.

• Monetary policy is expected to have the largest impact on inflation 5 to 8

qtrs. following the shock relative to the 2 to 3 qtrs. in the early 2000’s.

• The impact of the interest rate adjustment on the exchange rate is larger and longer-lasting in the ‘low’/single digit period.

• The impact on growth is larger and longer-lasting in the ‘low’/single digit interest rate environment.

• The impact on credit is less long lasting in the ‘low’/single digit period.

This may imply that there is a need to strengthen the credit channel of monetary policy prior to the country’s transition to full-fledged inflation targeting.

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Further Work

Calculate the time-varying forecast error variance decomposition to assess the relative importance of each channel (e.g. the exchange rate) in the transmission process.

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THE END

“The details of reality can disguise essential truths that are best revealed through simple fictions. Aesop called them fables….economists call them models”-

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Technical Appendix

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Conceptual and Theoretical Framework • The TV-FAVAR takes the following form:

𝑦𝑡 = 𝜆0𝑡 + 𝜆1𝑡𝑦𝑡−1 +⋯+ 𝜆𝑝𝑡𝑦𝑡−𝑝 + 𝜀𝑖𝑡 (1)

Where 𝑦𝑡′ = 𝑓𝑡

′, 𝑖𝑡 such that the VAR (State/Transition) equation is:

𝑓𝑡𝑖𝑡= ∅1

𝑓𝑡−1𝑖𝑡−1

+⋯+ ∅𝑝 𝑓𝑡−𝑝𝑖𝑡−𝑝

+ 𝜀𝑡𝑓

(2)

• The factor (Observation) equation, with drifting coefficients, is given as:

𝑥𝑡 = 𝛾𝑡𝑓𝑓𝑡 + 𝛾𝑡

𝑖𝑖𝑡 + 𝜇𝑡 (3)

Where, 𝜇𝑡~𝑁 0, 𝑅𝑡 and

𝛾𝑡𝑓′𝛾𝑡𝑖/𝑁 = 𝐼

𝑥𝑡 is a vector of economic time series,𝛾𝑡𝑖 and 𝛾𝑡

𝑓 are (N × K) and(N × M)

matrices of factor loadings and 𝜇𝑡 is an (N × 1) vector of error terms.

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Conceptual and Theoretical Framework

• To identify the structural model, we use a triangular reduction of the VAR (State) error covariance. Korobolis (2009), Primiceri (2005) where:

𝐴𝑡𝐻𝑡𝐴𝑡′ = Σ𝑡Σ𝑡

′ (4)

Or

𝐻𝑡 = 𝐴𝑡−1Σ𝑡Σ𝑡

′(𝐴𝑡−1)′ (5)

To simplify the notation, assume Σ𝑡Σ𝑡′ = Ω𝑡

• In this regard, time variation is assumed for the coefficients as well as the variance covariance matrix.

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Conceptual and Theoretical Framework

• All time-varying parameters follow a random walk process with the

innovation specification of Giordani and Kohn (2008).

𝛾𝑖,𝑡 = 𝛾𝑖,𝑡−1 + 𝐽𝑖,𝑡𝛾𝜂𝑡𝛾 Factor Loadings

𝑟𝑖,𝑡 = 𝑟𝑖,𝑡−1 + 𝐽𝑖,𝑡𝑟 𝜂𝑡𝑟 Variance of the Factor/Obs Equation

𝜙𝑖,𝑡 = 𝜙𝑖,𝑡−1 + 𝐽𝑖,𝑡𝜙𝜂𝑡𝜙

FAVAR Coefficients of the State Equation

𝑎𝑖,𝑡 = 𝑎𝑖,𝑡−1 + 𝐽𝑖,𝑡𝑎 𝜂𝑡𝑎 Off-diagonal elements of Covariance of State Eq

𝑙𝑛Ω𝑖,𝑡= 𝑙𝑛Ω𝑖,𝑡−1 + 𝐽𝑖,𝑡Ω 𝜂𝑡Ω Diagonal elements of Covariance of State Eq

where 𝜂𝑡𝜚~𝑁 0, 𝑄𝜚 for 𝜚 ∈ (𝛾, 𝑟, 𝜙, 𝑎, 𝑙𝑛 Ω). 23

Conceptual and Theoretical Framework (Estimation Strategy) • Given initial estimates for the factors, the Gibbs algorithm can be

summarized as follows:

1. Initialize the TV-FAVAR parameters (∅1 ) and the hyper parameters Simulate the TV-FAVAR coefficients (∅1 ) using the Carter and Kohn (1994) algorithm.

2. Draw the elements of 𝑄𝑟 using the inverse gamma distribution and the remaining 𝑄𝜚 (i.e. the hyperparameters) using an inverse Wishart (IW) distribution.

3. Draw factor loadings (𝛾𝑡𝑓) and covariance matrix (𝑅𝑡) given initialized

factors. 4. Given all other parameters simulate factors as in Bernanke et al

(2005). 5. Go back to step two

With regard to the priors for the hyperparemeters, we set 𝐾𝑞= 0.01,𝐾𝑤 = 0.1 and 𝐾𝑠= 0.01 (Primiceri 2005)

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Inflation: Selected Caribbean Countries

25 Source: International Financial Statistics