Book
Share
Chapter

6 Identifying the Monetary Transmission Mechanism in Sub-Saharan Africa

Author(s):
Andrew Berg, and Rafael Portillo
Published Date:
April 2018
Share
  • ShareShare
Show Summary Details
Author(s)
Bin Grace Li, Christopher Adam, Andrew Berg, Peter Montiel and Stephen O’Connell 

1 Introduction

Central banks and researchers in low-income countries often use structural vector auto-regression (SVAR) models estimated on aggregate macroeconomic data to develop a reliable understanding of the monetary transmission mechanism (MTM) linking a central bank’s policy instruments and the outcomes it is seeking to influence—typically, aggregate demand and inflation.1 The task of generating robust estimates of the speed, direction, and relative strength of the MTM is demanding in all economies, but particularly so in countries such as those of sub-Saharan Africa where financial markets are thin, the economy is undergoing rapid structural change, and policy regimes are seeking to adjust to this structural change. This task is made even harder by the relatively poor quality of macro-economic data in many low-income countries.

Most studies of the monetary transmission mechanism in LICs, the majority of which tend to rely on conventional SVAR or models, would appear to confirm the extent of this challenge. Mishra, Montiel, and Spilimbergo (2012) and Mishra and Montiel (2013) survey a large literature on the effectiveness of the monetary transmission mechanism (MTM) in low-income countries. They find that stand ard empirical methods, in the form of vector auto-regressions (VARs) applied to macroeconomic data, imply that MTMs are weaker and less reliable in low-income countries than in high-income and emerging economies. By weaker, they mean that monetary policy instruments tend to have small estimated effects on aggregate demand. By less reliable, they mean that these estimated impacts are not precisely estimated, leaving considerable statistical uncertainty about the true MTM. Mishra et al. (2012) suggest two broad possible explanations for these findings:

  • Facts on the ground. Formal financial markets are small and poorly arbitraged in these countries, and many low-income countries (LICs) maintain fixed or heavily managed exchange rates; as a consequence, the link between the short-term interest rates that central banks can control and the variables that matter for aggregate demand (e.g., longer-term interest rates, the exchange rate) may be weak or absent. Even the bank lending channel may tend to be weak when the formal financial sector is small, financial frictions are severe, and the banking industry is characterized by imperfect competition.
  • Limitations of the method. The MTM is not in fact weak, but the data-intensive, atheoretic methods typically used to evaluate the MTM empirically are not capable of measuring its strength accurately in the research environ ment characteristic of LICs. If this explanation is correct, then it is the VAR evidence in LICs that is weak and unreliable, not the MTM itself.

Our aim is to discriminate between the ‘facts on the ground’ and ‘limitations of the method’ interpretations of the missing MTM, focusing on the extent to which the characteristics of a LIC research environment are hostile to VAR-based approaches to identification. The stakes here seem high. If the ‘facts on the ground’ explanation is correct, the empirical literature suggests challenges to successful monetary policies in low-income countries. Along with other features of the LIC environment, such as frequent large supply shocks, weak and uncertain transmission may make it more difficult for policymakers to keep inflation within narrow bounds and to stabilize activity in the face of demand shocks. On the other hand, if the missing MTM mainly reflects methodological limitations, then the results of the VAR-based literature should be suitably discounted and researchers evaluating the strength and reliability of the MTM should seek to compliment VAR-based analyses with approaches that are more robust to the peculiar weaknesses of these methods in LIC-like environments. For example, the use of bank-level or even loan-level data to investigate the strength of the bank lending channel is an obvious candidate (e.g., Mbowe, 2012; Abuka et al., 2015).

In this chapter we attempt to assess the limitations of the method’ interpretation of the missing MTM, focusing on whether specific characteristics of a LIC research environment are particularly hostile to VAR-based approaches to identification. Specifically, if a strong MTM is present, can standard VAR methods uncover it in a LIC-like research environment? We address this question by applying VAR-based methods to a world in which a strong MTM exists but the research environment has some of the features that are characteristic of LICs. A list of these features might include, for example, poorly understood economic structure and non-transparent central banks; short data samples due to missing data or recent major structural changes or policy reforms; large measurement errors; and a high volatility of macroeconomic shocks (especially the prevalence of large temporary supply shocks). Here, we focus on the former set of structural concerns, rather than issues of data and measurement. These are dealt with in detail in Li et al. (2016).

To implement this programme we set up a Monte Carlo experiment to assess the statistical properties of the kind of SVAR models typically deployed on low-income country data. Underlying the data-generating process is a small dynamic stochastic general equilibrium (DSGE) model of a small open economy that embodies a well-defined MTM with an interest-rate channel and an exchange-rate channel. We use the solution to this DSGE to generate multiple independent runs of data, and then within each of these runs, mimic the process of an empirical researcher using SVAR-based methods to infer the nature of the MTM. In particular, we examine the properties of the impulse response functions (IRFs) that she would produce. We compare her median estimated IRFs to the true one, study the spread of estimated IRFs across simulations, and examine the power of conventional significance tests against the hypothesis of a zero response.

Section 2 introduces our DSGE model in four macroeconomic variables, the GDP gap, the inflation rate, the real exchange rate, and the nominal interest rate, while in Section 3 we discuss the relationship between DSGEs and structural VARs that can be identified using restrictions on the contemporaneous inter actions between the variables. Section 4 begins by documenting the empirical success of the VAR-based approach when the researcher has chosen a valid identification scheme and is operating in a favourable research environment (as may be found in a mature open economy such as Canada, for example). Section 5 quantifies the effects on inference of various sorts of weak transmission, some of which are plausibly related to the structure of LIC economies and others to the characteristics of monetary policy regimes themselves.

Section 6 offers a cautionary detour. It is well-understood that correct identification is critical. When the environment in which the central bank operates and its mode of operation are poorly understood—as is perhaps particularly the case in most LICs, both because of the opacity of the regimes and the scarcity of research—identification is especially challenging, and estimates of the MTM can go badly wrong. Section 7 drills deeper into misidentification by analysing the implication of non-transparent central bank policy frameworks, in such as the hybrid money targeting regimes, which characterize many LICs. We conclude that misidentification related to hybrid monetary policy reaction functions is a plausible and difficult challenge. Section 8 concludes with a summary of findings and with a discussion of possible extensions and policy implications.

2 DSGES as a Data-Generating Process

The MTM is about the ability of monetary policy to exert a temporary effect on aggregate demand.2 To focus on these effects we ignore stochastic trends in the data, implicitly assuming that these can be estimated with reasonable statistical confidence so that the stationary part of the data is cleanly isolated. Our DSGE models will therefore generate a stationary vector xt=[y˜t,πt,e˜t,it] of quarterly values for the GDP gap y˜t, defined as the gap between actual GDP and unobservable potential GDP), the inflation rate (πt), the real exchange rate (e˜t, with an increase being a real appreciation), and the annualized nominal interest rate (it). In this chapter we treat the model-generated GDP gap as observable, although in Li et al. (2016) we introduce an underlying trend in GDP to examine how difficulties in inferring the GDP gap from observed measures of output affect inference.

The four endogenous variables in the model will in turn be functions of a vector ϵt=[ϵty,ϵtπ,ϵte,ϵti] of structural shocks that are not directly observable by the researcher. The objects of interest to the researcher are the responses of xt+j to a one-time unit-value shock to monetary policy (Δϵti=1). To estimate these, the researcher starts by estimating a reduced-form VAR of the form

where A(L) contains enough lags to render the reduced-form innovations ut approximately white noise. In the absence of measurement error or inappropriate truncation, this produces consistent estimates of the lag parameters in A(L) and the covariance matrix Ω of the reduced-form innovations.3 The researcher then imposes enough restrictions on the reduced form to identify the structural shocks to monetary policy. In our case, these take the form of zero restrictions on elements of the square and invertible matrix B in

Conditional on identification, the impulse responses (IRs) can then be calculated as nonlinear functions of the estimated lag parameters and reduced-form shock covariances. The researcher computes these estimated IRs and, in a final step, bootstraps their standard errors and calculates t ratios for each impulse-response step. When a ‘true’ MTM is present in the data-generating process, the researcher should see impulse responses that are appropriately signed and shaped, of roughly correct magnitude, and of reasonable statistical significance. We loop over multiple simulated datasets in order to study the population distribution of estimated impulse responses, the associated t-ratios, and the power of the t-ratio test in a wide variety of specific environments.

2.1 The DSGE Model

The model we employ for our experiments is a canonical New Keynesian open-economy model that combines an IS curve, a New Keynesian Phillips curve, an interest-parity condition, and a Taylor Rule for monetary policy (e.g., Berg, Karam, and Laxton, 2006). There is no empirical consensus on the appropriate parameterization of such a model for LICs, but in choosing parameters we can draw on recent research that develops partly calibrated and partly estimated DSGEs for low-income countries in Africa. We rely particularly on Chapter 8, which develops DSGEs with similar four-equation structure for Kenya, Tanzania, and Uganda. Our basic model, complete with parameters, is:

IS equation:

New Keynesian Phillips curve:

Uncovered interest parity equation:

Taylor-type rule for monetary policy:

Structural shocks:

Here Et denotes an expectation conditional on information available at time t. As explained below, we allow information sets to vary across equations, reflecting differences in the information available to agents. Note also that equation (7) departs from the bulk of the DSGE literature by assuming i.i.d. shocks, in preference to the standard AR(1) structure: our version allows for distributed lag responses similar to those in the literature, but these are governed completely by the lags within the behavioural equations. By eliminating purely exogenous dynamics, we substantially simplify the task of solving the DSGE and representing its solution as a structural VAR, although in principle the VAR could be conditioned on strongly exogenous variables such as world oil prices.

The model we are employing was of course not developed for LICs, and in characterizing the MTM it makes no effort to capture the financial architecture or other ‘facts on the ground’ that may differentiate LICs from the advanced countries for which these models were developed. For most of the analysis, monetary policy follows a Taylor-style rule, even though many LICs use the monetary base and other measures rather than a policy interest rate as the main operational instrument. In Section 7 we briefly consider the case where the authorities’ true reaction function gives weight to deviations in money aggregates from target, but where this reaction function may not be correctly identified by the econometrician. We also omit a banking sector from the model, even though the nature of the credit channel may differ in LICs as compared to more advanced countries. Finally, we simplify by assuming that the structural shocks are mutually uncorrelated. These simplifications reflect our focus on aspects of the research environment that are largely model-independent. Our Monte Carlo approach can of course be applied to any structural model, a topic to which we return in the concluding section.

Our data-generating process will not be the DSGE model itself, but rather its solution in terms of the endogenous state variables and the shock vector εt. This introduces a set of technical issues that are well understood in the DSGE and VAR literatures but that appear here in combination. First, for VAR-based methods to have a chance of uncovering the features of the MTM, the solution to the DSGE must be representable, at least approximately, as a finite-order VAR in observable variables. As discussed in detail in Li et al. (2016) some of our model solutions have exact representations as finite-order VARs, while others are well approximated by VARs with short lags (we use four-quarterly lags). Second, the monetary policy shocks must be identifiable through the imposition of conventional structural-VAR restrictions on this representation. We focus on short-run restrictions, because these remain the dominant approach to identification in the applied literature reviewed by Mishra et al. (2012). As discussed below, such restrictions work by limiting the contemporaneous interactions between the variables in the VAR.

3 Motivating CEE-Recursive Structure

The restrictions we impose at the estimation stage are typically motivated in the structural VAR literature by appealing to a structural simultaneous equations model of the form

The shocks εt are i.i.d. and mutually uncorrelated variables that can be normalized without loss of generality to have unit variances (E[ϵtϵt]=I).4 As long as B0 is invertible, equation (8) implies the reduced-form VAR representation in equation (1), with A(L)=B01B(L). The relationship between the structural and reduced-form innovations is then given by equation (2), with B=B01.

Within the class of short-run restrictions, the most common are those that impose a recursive structure on B0. Cholesky decompositions assume that the model is fully recursive, so that B0 is lower triangular. As Christiano, Eichenbaum, and Evans (1999) have shown, however, if the focus is on the impulse responses just to monetary policy shocks, these can be recovered from the reduced form VAR under the considerably weaker condition that the system be contemporaneously block-lower-triangular, with the interest rate occupying its own diagonal block. We refer to any system that can be ordered into two or more block-recursive segments, with the interest rate occupying its own diagonal block, as ‘CEE-recursive’. When a structural VAR model is CEE-recursive, the impulse responses to monetary policy shocks can be recovered from the reduced-form VAR even if the remaining impulse responses cannot.5

A glance at equations (3)-(7) confirms that the solution to our DSGE will not exhibit the block-recursiveness property under full information. Instead, it will tend to be highly simultaneous. This is in part because monetary policy is assumed to affect all endogenous variables contemporaneously, so the interest rate does not occupy its own diagonal block. However, it also reflects the role of expectation variables, since any endogenous variable that is in the information set of a particular class of agents will contemporaneously affect all of the endogenous variables that are influenced by the forecasts or ‘now-casts’ formulated by those agents. Since all of the equations in our model contain such expectation variables, under full information all of the model’s endogenous variables would tend to appear in every equation.

We therefore have to impose additional restrictions on our DSGE in order to produce a data-generating process that is identifiable via short-run restrictions. We retain the simultaneity of the structural model and obtain exclusion restrictions through assumptions about the information sets available to the private sector and the central bank. For most of the chapter, we place the interest rate first in the CEE block-recursive ordering so that it affects all other variables contemporaneously. What this means is that the private sector has full information, but the central bank can only observe the endogenous variables with a lag.

This structure can be rationalized as follows: the central bank (strictly its monetary policy committee) sets the systematic part of the policy interest rate at the beginning of the period, before any shocks arrive. Shocks then hit the system and are observed by the private sector and the monetary policy committee. The private sector can react immediately, but the central bank cannot do so until the beginning of the next period (i.e. the next MPC meeting). The central bank is, therefore, setting the systematic part of its policy on the basis of t–1 information, while the private sector is behaving on the basis of time-f information. We suggest this structure may have greater plausibility in a LIC context, where the central bank has less access to timely information on the state of the economy, than in higher-income countries. To denote the informational ad vantage of the private sector, we refer to this block-recursive identification strategy as ‘CEE-PS’.

For robustness, we also examine an alternative strategy in which the informational advantage accrues to the central bank. It sets the interest rate with full information, but the interest rate does not affect the model’s other endogenous variables contemporaneously, not because of a behavioural lag, but because the private sector does not observe the shock to monetary policy contemporaneously, so it reacts to its forecast of the time-t interest rate based on information dated at time t—1. This structure, which corresponds to a block-recursive structure in which the interest rate block comes last, follows Christiano, Eichenbaum, and Evans (2005) and reflects the common practice in the advanced-country VAR literature of attributing an information advantage to the central bank. We refer to this identification strategy as ‘CEE-CB’.

These identification strategies still fall foul of the serious challenge to recursively identified VARs in an open-economy context posed by Kim and Roubini (2000). As they point out, the interest rate cannot occupy its own diagonal block unless the central bank does not respond contemporaneously to the current exchange rate (in our preferred CEE-PS formulation) or if the exchange rate does not respond to the current interest rate (in the CEE-CB formulation). Absent either of these two conditions, the interest rate and exchange rate are simultaneously determined regardless of the recursive structure of the remainder of the model. This is addressed within the structural VAR literature by appealing to non-recursive short-run restrictions, sometimes in combination with theoretically motivated long-run restrictions (e.g., that monetary policy has no long-run impact on real variables).

4 Strong var Performance under Baseline Conditions

Figure 6.1 sets the stage for the subsequent discussion. Here we report the performance of a validly identified CEE-recursive VAR using forty years of quarterly data. The experiment assumes equal variances for the four structural shocks, and the information structure is CEE-PS. The researcher estimates the VAR with four lags.6 To focus on parameters of interest we report only the impulse response functions for the monetary policy shock. Since the components of Figures 6.1a and 6.1b will appear throughout the chapter, we begin by describing their content.

Figure 6.1.aImpulse Responses to Monetary Policy Shock: Baseline

Figure 6.1.bPower Functions for Monetary Policy Shock

Figure 6.1.cT-stat for Monetary Policy Shocks

The researcher is trying to uncover the true, model-based impulse responses, which appear as the bold lines identified by dots in Figure 6.1a. As shown in the figure, these IRFs display the conventional hump-shaped responses of the real exchange rate, inflation, and output to a monetary contraction. On impact, a 100 basis point increase in the interest rate leads to a 1 percentage point contraction in inflation and a reduction in the output gap by around 0.7 per cent of GDP, values that are broadly in line with Christiano et al. (2005). To examine whether VAR methods can uncover these responses, we generate 1,000 data samples from our model, based on independent simulations of the DSGE solution, each generated by 40 quarters of independent draws on the shock vector εt. For each data sample, a researcher estimates a VAR and constructs IRs by imposing the CEE-PS identifying restrictions. The empirical performance of these IRs is summarized by the three dashed lines in Figure 6.1a. These lines show the fifth, fiftieth, and ninety-fifth percentiles of the population distribution of simulated point estimates for the impulse responses (with percentiles computed separately for each impulse-response step).

For each of the 1,000 simulations, the researcher computes the VAR coefficients and standard errors using conventional Bayesian estimation methods. The figure shows the probability of rejecting the null hypothesis of a zero impulse-response coefficient at each step. We assume that the researcher treats the t ratios as asymptotically normal and applies the relatively undemanding hurdle of 10 per cent significance.

Figures 6.1a and 6.1b establish that with appropriate identification and in the presence of ample and high-quality data, the VAR methodology does very well at uncovering strong monetary transmission when it is present. The estimated impulse responses for output and inflation show only a trivial degree of small-sample attenuation at the median, and for the first few quarters fully 90 per cent or more of the point estimates lie on the correct side of zero.

The researcher’s own inference will of course frequently be less confident than suggested by Figure 6.1b, because the researcher has only one data sample. Figure 6.1c reflects this by showing the full distribution of t ratios across the 1,000 runs. The structure of the exercise suggests that the width of boot strapped confidence intervals for the IR coefficients will not be far from that implied by the population distribution of impulse responses, and the comparison of Figures 6.1a and 6.1c bears this out. When one end of the population distribution of IRs is close to zero in Figure 6.1a, roughly half of the t statistics reported in Figure 6.1c fail to reject the null.7

5 Low Power to Detect Weak Transmission

Unfortunately, the power of the SVAR method to reject the null of no transmission deteriorates significantly when true transmission is present, but weak. In this section we illustrate this property under several alternative sources of weak transmission. Figure 6.2 shows the results of the alternative CEE-CB experiment, where the central bank has the information advantage and the model solution places the interest rate last. To keep the presentation of results manageable we reproduce, on the top row, the impulse response plots for output and inflation only and the corresponding power plots on the bottom row of the figure. The model parameters are identical in the two cases, but there is a substantial difference in the true impulse responses, with the MTM being much weaker in this case (compare the top row of Figure 6.2 with Figure 6.1a, noting the difference in the vertical scales). The difference is driven by the effect of lags in diluting the impact of a monetary policy change in the CEE-CB case. The SVAR results are somewhat weaker in this case: though the median estimated IRF continues to track the true IRs generated by the model very closely, the fifth and ninety-fifth percentiles of the estimated IRFs are now more widely dispersed relative to the median, and there is a correspondingly substantial loss of power. The deterioration in the inference environment relative to Figures 6.1a and 6.1b reflects the unfavourable effect on inference of smaller true effect sizes.

Figure 6.2.Impulse Responses and Power Functions: CEE-CB Baseline

The weaker MTM just described arose from an alternative information structure, with unchanged model parameters. To explore further the implications for inference of a weak MTM, we now consider the impact on VAR-based inference of small true effects driven by model parameters, rather than by the information environment. To do so, we return to the CEE-PS information structure as the baseline. Even within a tightly parameterized DSGE, there are many parameters that may differ substantially between LIC and higher-income applications. The private-sector block incorporates both an interest-rate channel that operates though the IS curve and an exchange-rate channel that branches off from the interest parity condition to the IS and Phillips curves, while the monetary policy rule incorporates feedback from both inflation and the GDP gap along with a parameter that governs the degree of interest-rate smoothing. Based on Mishra et al. (2012) and Mishra and Montiel (2013), we focus here on two simple experiments. In Figure 6.3, we scale down the transmission elasticities in the IS and Phillips curves by a uniform 75 per cent relative to the baseline model, and in Figure 6.4 we leave the transmission elasticities untouched but reduce the lag parameter in the monetary policy rule by 75 per cent.

Figure 6.3.Weak Transmission (elasticities in IS and PC scaled down by 75%)

Figure 6.4.Smoothing Parameter (scaled down by 75%)

Figure 6.3 shows the impact of uniformly low interest-rate and exchange-rate elasticities in the private-sector block. The (new) true IRs in this case are shown by the heavy solid line. As before, the dashed lines show the fifth, fiftieth, and ninety-fifth percentiles of the population distribution of simulated point estimates. For reference, the true IRs with the original model parameters are retained in the figure in the form of the heavy line with dots. As expected, the change in the parameters weakens the effect of monetary policy on aggregate demand, which shows up in the form of smaller impacts on the GDP gap and the inflation rate. The true MTM is particularly weakened with respect to its effects on real activity. Notably, however, the estimated impulse responses continue to show very strong fidelity at the median, with the median IRs corresponding very closely to the true ones. Comparing Figures 6.3 and 6.1a, there is also no discernible impact on the spread of estimated impulse responses. To a first approximation, therefore, the impact of weak transmission elasticities operates exclusively through the impact of small true effect sizes on the power of t-ratio tests against the null hypothesis of no effect. That impact is substantial, however, with the scope for confident inference cut roughly in half (bottom row of Figure 6.3).8

Finally, in New Keynesian models such as (3)-(6), the strength of transmission depends not only on spending elasticities, but also on a transmission channel that may differ sharply between LICs and higher-income countries. Mishra, Montiel, and Spilimbergo (2012) find that the correlation between short-term interest rates and lending rates tends to become progressively weaker at lower levels of development. While equations (3)-(6) do not directly incorporate a lending channel, the IS curve and interest-parity condition can be solved forward to express the levels of the current GDP gap and real exchange rate gap as functions of current and expected future short-term interest rates. As emphasized by Woodford (2001), monetary policy shocks affect the ‘tilt’ of the spending and real exchange rate gaps via the short-term interest rate, but they alter the equilibrium level of these variables only to the degree that they change current long-term rates. The pass-through of short rates to long rates is in turn governed both by the parameters of the private sector block and, very importantly, by the degree of interest-rate smoothing implemented by the central bank.9 To investigate the role of the latter, in Figure 6.4, we leave the transmission elasticities unchanged and reduce the smoothing parameter in the monetary policy rule by 75 per cent. Monetary policy shocks now pass through much more weakly into long rates and spending.

These results underscore the leverage of interest-rate smoothing in the New Keynesian model. The true impulse responses (shown by the solid line in bold) decline slightly more sharply than when the transmission elasticities are reduced by the same proportion, but the overall shapes are virtually identical. Consistent with our previous results, there is minimal evidence of bias in the impulse responses: the very weak true IRs are faithfully reproduced by the median estimated IRs. Not surprisingly, there is now much less scope to reject the null hypothesis of zero monetary policy effects.

Overall, Figures 6.1 to 6.4 are consistent with a ‘facts on the ground’ interpretation of the ‘missing MTM’ puzzle. Given a valid block-recursive identification scheme and sufficient data, structural VARs identified through short-run restrictions do well at uncovering the true MTM—whether it is strong or weak—in the strict sense that the median estimated IRs track very closely those generated by the true MTM. At the same time, however, we have found that the population dispersion in estimated IRs is not substantially affected by the strength of the underlying ‘true’ MTM. Therefore, the weaker the true MTM, the harder it is for the data to reject the null of no response—i.e., the weaker the power of tests of the null hypothesis that the MTM is entirely missing. Even where a (plausibly weak) MTM exists, the researcher armed with only one dataset, even a pristine one that is 40 years long, may well conclude that it is missing.

6 Identification Through Behavioural Lags and Information Sets

Up to this point, we have maintained the assumption of correct identification. The perils of incorrect SVAR identification are of course neither surprising nor LIC-specific. However, there tends to be no consensus on the nature of behavioural lags and information sets in the LIC environment. Thus, identification of monetary policy shocks is likely to prove far more difficult in the LIC environment than in the more familiar environment of high-income countries.

In this section, we examine the case in which the researcher places the interest rate too late in the recursive structure of the model. If our CEE-PS ordering has any special plausibility in low-income applications, this error might be a natural one for a researcher trained in the advanced-country literature. The researcher in Figure 6.5 assumes a CEE-CB information structure when the true structure is in fact CEE-PS. The solid line with dots in bold represents the true CEE-PS impulse responses (reproduced directly from Figure 6.1) while the dashed lines once again represent the fifth, fiftieth, and ninety-fifth percentiles of the estimated IRFs when the data are generated by CEE-PS but the researcher mistakenly imposes CEE-CB identification.

Figure 6.5.CEE-PS Wrong Identification

The result of this error is sufficient to produce impulse responses that are ‘weak and unreliable’ in the extreme: they are essentially zero, both economically and statistically. At the median, they closely approximate the relatively weak shapes of the impulse responses that would have been generated by a CEE-CB structure, even though the true responses are the much stronger ones generated by CEE-PS. However, the dispersion of the estimated IRFs is dramatically wider than was observed when CEE-CB was in fact the correct identification (compare Figure 6.5 with Figure 6.2). Not surprisingly, statistical tests based on bootstrapped standard errors for the estimated IRs will have essentially no power to reject the null of zero monetary policy effects in this case (Figure 6.5), even though the true effects are in fact extremely powerful.

7 Central Bank Transparency

Central banks in LICs tend in general to be far less transparent than those in high-income countries, making the nature of the monetary policy rule less evident.10 To examine the pitfalls posed for estimation of the MTM by misspecification of the monetary policy rule, we consider a case where the authorities optimally update their policy interest rate on the basis of the growth of money aggregates relative to target, but where this additional information on money growth is not exploited by the researcher. This setting is described in Chapter 8, where money aggregates, which are essentially observed in real time, are systematically related to expected output and inflation through the private sector’s demand for money. Ex ante, there is therefore an exact equivalence between any given interest rate rule and a corresponding money target. This equivalence does not hold when the economy is subject to shocks, including to money demand, so that the authorities’ optimal policy rule entails giving weight to both deviations of the interest rate from target and money from its target. How much weight is placed on money in the policy rule will depend on the volatility of money demand shocks relative to real shocks and on the interest elasticity of the demand money. When money demand is highly volatile and the interest elasticity is high, the optimal weight placed on money should be low, and vice versa.

To operationalize this idea, we augment our baseline model by introducing the nominal money target into the Taylor rule defined in equation (6), recalling that the inflation and nominal money growth targets are both zero. First, consider a pure money-targeting rule, in which the authorities allow interest rates to move so as to achieve the desired growth rate of money, assumed to be 0 for simplicity. We can start with a conventional money demand equation for the change in money, Δmt:

The associated interest rate that sets money growth to the target, itM, is then:

Following Chapter 16, we can define a hybrid rule as follows:

where itT is the interest rate target defined in equation (6) (excluding the monetary policy shock in that equation, which is now explicit in equation (11)). The parameter λ defines the weight placed on the interest rate rule. When λ = 1, the result is a standard Taylor-type rule as we discuss before; when λ = 0, policy is defined in terms of a target (0 in this case) for the growth rate of money, and the interest rate is a residual in that it follows from the authorities’ efforts to hit their money target. In many ‘money targeting’ countries, it is plausible to think that λ lies somewhere in the middle.11

Solving equation (11) for the interest rate yields a hybrid rule:

Replacing itT with equation (6) and itM with equation (10), we recover an implicit interest rate rule of the form:

where, critically, etm is a composite of the money supply and money demand shocks, and the appearance of yt and πt implies that all shocks that matter for these variables, notably contemporaneous aggregate demand and supply shocks, also affect the interest rate contemporaneously.

With the true model now defined by equations (3)-(5) and (7) and (9) plus (6), (10), and (12) (or (13)), the researcher armed with the four-variable vector of data xt=[ii,e˜t,yt,πt], will only correctly identify the monetary policy shock if λ = 1. For any λ < 1, however, where money growth provides the central bank with information on the evolution of inflation and the output gap, the researcher is unable to decompose the composite error term in (13) so as to cleanly identify the monetary policy shock.

Figure 6.6 shows the outcome of applying our standard four-variable VAR with the same recursive identification to data produced by a model with hybrid monetary policy, where λ = 0.95, i.e. where in the model the authorities place only a small weight on money deviations from target. As the figure shows, even this seemingly minor deviation strongly attenuates the median estimated impulse responses and greatly reduces power relative to the baseline.12

Figure 6.6.CEE-PS Money Target Model (λ = 0.95)

In both the case of the alternative information structure (implying a different recursive ordering) and the case of hybrid policy rules involving money, the nature of the impulse response functions might lead the researcher to re-think their estimation strategy (rather than conclude that transmission is weak, for example). In the first case, experimenting with alternative recursive orderings would help. In the second case, however, it would not. Even with the full five-variable vector, neither recursive identification strategy can isolate the monetary policy shock in its own block.

Imposing more structural identification schemes such as SVARs might help in the hybrid rule case.13 However, this solution is far from trivial. First, our conventional specification of money demand is very limited. In the DSGE tradition, money demand may also depend on expected inflation and other variables.14 In this situation, no contemporaneous zero restrictions in the money demand equation are available to identify the SVAR. More generally, getting SVAR zero restrictions right would require being precise about the details of a quite complex policy framework. If our money-targeting example is indicative, there is not likely to be a one-size-fits-all solution to the proper identification of monetary shocks in more complex settings.

Taken together, the results in this and the last sections suggest that incorrect identification of monetary policy shocks is itself a prime suspect in the case of the missing MTM.

8 Conclusions

In an effort to come to grips with the ‘missing MTM’ in the empirical literature on LICs, we have reversed the standard dialogue between DSGEs and VARs. In the standard dialogue, VAR-based impulse responses provide an empirical standard against which DSGEs or other theory-based models can be evaluated. We have instead used DSGEs as a data-generating process, in order to ask a question about the validity of VAR-based impulse responses. If a strong MTM is present in the data, can standard VAR methods uncover it?

No parametric method will do very well if it mis-specifies the data-generating process. This is the basis of Sims’ critique of structural econometric modelling, and as long as the data are generated by a stable but unknown data-generating process, this critique favours the use of as few structural restrictions as possible to identify the MTM. VARs identified via short-run restrictions are very widely used in the literature on LICs, and perhaps even more intensively there than elsewhere, given the relative dearth of structural modelling in these countries. Within this class, we have focused on CEE-recursive VARs, which impose just enough recursive structure to identify the monetary policy impulse responses, while leaving the other responses potentially unidentified. Our off-the-shelf DSGE will not generate a solution with this property, but we have demonstrated that an otherwise-canonical DSGE is capable of doing so under mixed-information assumptions of the type often seen in the structural VAR literature.

When the VAR researcher imposes a valid identification scheme and has access to ample and high-quality data, the virtues of the VAR approach come through strongly. The LIC environment nonetheless poses a set of well-defined challenges to a strategy that ‘lets the data speak’: we investigate the effects on statistical power of weak transmission arising from various sources as well as inappropriate identification of monetary policy shocks due to incorrect assumptions about behavioural lags and information asymmetries or to opaque monetary policy rules.

We find that a weaker—but otherwise standard—MTM would be hard for a typical VAR to detect, even with forty years of pristine data and proper identification. Point estimates of the impulse responses to monetary shocks are at most only mildly attenuated when the MTM is unusually weak. However, the power of the VARs to reject the null that the MTM is ‘missing’ is very low. Among the potential sources of weak transmission, we have emphasized three: lags in private sector responses, small interest rate and exchange rate elasticities in the goods markets, possibly caused by the small size of the formal financial system and the structure of external trade in LICs, and a limited degree of interest-rate smoothing that reduces the signalling power of a given interest rate change. Some of these features would presumably be highly persistent features of the economies. Those that depend on the policy regime, notably low smoothing, could change quickly.

We also find that, unsurprisingly, improper identification can easily produce estimates suggesting a weak MTM, even when a strong MTM is present. Because the identification challenge is likely more severe in the LIC context than in the better-understood context of high-income countries, deficiencies in identification strategy are another prime suspect in the ‘case of the missing MTM’. Such challenges can arise from poorly understood behavioural and information lags, and/or from opaque central bank policy rules. In some cases, econometric due diligence may rescue matters, as experimentation with alternative block-recursive orderings would do in one of the examples of the previous section. The possibility that more serious mis-specifications are obscuring the MTM, however, requires a willingness to re-think the SVAR identification strategy and, in some cases, to explore different approaches to identification.

There is a paucity of information on which to base standard block-recursive identification of monetary policy shocks in the LIC context, and the role of exchange rates and perhaps money aggregates as well as interest rates, complicates the identification challenge and renders exclusion restrictions particularly difficult to justify. Unless a strong case for a specific set of short-run restrictions can be made in specific applications, other approaches to identification may prove more fruitful. A natural extension of the approach of this chapter would be to acknow ledge the reality that agents in the economy (central bank and private sector alike) can observe both the exchange rate and the interest rate in real time. When these two variables form a simultaneous block, the monetary policy shock will not be identifiable by imposing a block-recursive structure.15 Another approach to identification of monetary policy shocks makes use of long-run restrictions, as in Mishra, Montiel, Pedroni, and Spilimbergo (2014).

Altogether, all these VAR based methods seem worth pursuing. Further country-specific analyses that carefully tailor the approach to the particular country-specific institutional framework for monetary policy may permit progress. Even in the US, with its unusually long, stable data series and policy regimes, economists experimented for many years before reliably generating acceptable results, only eventually solving the ‘liquidity puzzle’ that interest rates tended to rise in response to an increase in the money supply and the ‘price puzzle’ that inflation seemed to rise after a shock that tightened monetary policy.16

However, this chapter does suggest that the challenges to achieving similar success in LICs will be severe. Moreover, in Li et al. (2016) we note a further set of LIC-specific challenges, using the same analytic framework. Three are particularly important. First, the relevant time series data for LICs are very rarely longer than ten years, rather than the forty assumed here, especially considering breaks associated with monetary policy regime shifts. Second, measurement error seems to be much larger in LICs than in middle-income and advanced economies. And finally, the prevalence of supply shocks in LICs complicates the estimation of the output gap. Each of these problems sharply reduces the power of VAR-based methods to uncover even a strong and properly identified MTM, and together they are devastating. Other empirical methods may thus be helpful. The case study approach adopted in Chapter 5 examines the implications of a large monetary policy shock identified through a narrative approach. Abuka et al. (2015) use loan-level data to assess the bank-lending channel in Uganda. Perhaps more radically, the imposition of more economic structure and use of Bayesian techniques is a natural way to make use of available data while accepting its scarcity.

It seems clear, despite the possible ways forward, that uncertainty about the MTM is likely to continue to face LIC central banks. Moreover, while we have shown that inappropriate identification can lead to downward biased coefficients, while various features of the data environment can produce low t-statistics and substantial variation in estimates of the MTM, we have also shown that weak transmission is also a possible reason for poor VAR results.

What broader implications follow from these conclusions? One view is that, in the face of this uncertainty, monetary policy should be passive. In particular, frameworks that do not require confident knowledge of the MTM may be more appropriate, notably fixed exchange rate regimes.

Chapter 1 and indeed the rest of this book present another perspective that recognizes that this uncertainty is hardly unique to LICs and that learning-by-doing is a necessary counterpart to the reform of monetary policy regimes. However, clearly further empirical work is a critical component of this learning process. We hope that the results of this chapter will help to guide the agenda.

References

    AbukaC.AlindaR. K.MinoiuC.PeydroJ. L. andPresbiteroA. F. (2015). Monetary Policy in a Developing Country: Loan Applications and Real Effects. IMF Working Paper 15/270. Washington, DC: International Monetary Fund.

    • Search Google Scholar
    • Export Citation

    BergA.KaramP. D.LaxtonD. (2006). A Practical Model-Based Approach to Monetary Policy Analysis. IMF Working Paper WP/06/80. Washington, DC: International Monetary Fund.

    • Search Google Scholar
    • Export Citation

    BoughtonJ. M. andTavlasG. S. (1991). What Have We Learned about Estimating the Demand for Money? A Multicountry Evaluation of Some New Approaches. IMF Working Papers 91/16. Washington, DC: International Monetary Fund.

    • Search Google Scholar
    • Export Citation

    ChristianoL.EichenbaumJ. M. andEvansC. L. (1999). Monetary Policy Shocks: What Have We Learned? In J. B.Taylor andM.Woodford (Eds.) Handbook of Macroeconomics1A. Amsterdam: North Holland.

    • Search Google Scholar
    • Export Citation

    ChristianoL.EichenbaumM. andEvansC. L. (2005February). Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy. Journal of Political Economy113 (1) 145.

    • Search Google Scholar
    • Export Citation

    FaustJ. andRogersJ. (2003October). Monetary Policy’s Role in Exchange Rate Behavior. Journal of Monetary Economics50(7).

    KimS. andRoubiniN. (2000). Exchange Rate Anomalies in the Industrial Countries: A Solution with a Structural VAR Approach. Journal of Monetary Economics4556186.

    • Search Google Scholar
    • Export Citation

    LeeperE. M. andGordonD. B. (1992). In Search of the Liquidity Effect. Journal of Monetary Economics29 (3) 34169.

    LiB. G.O’ConnellS.AdamC.BergA. andMontielP. (2016). VAR meets DSGE: Uncovering the Monetary Transmission Mechanism in Low-Income Countries. IMF Working Paper 16/90. Washington, DC: International Monetary Fund.

    • Search Google Scholar
    • Export Citation

    MboweW. (2012August3). The Bank Lending Channel of Monetary Policy Transmission: Dynamic Bank-Level Panel Data Analysis on Tanzania. Bank of Tanzania.

    • Search Google Scholar
    • Export Citation

    MishraP. andMontielP. (2013). How Effective is Monetary Transmission in Developing Countries: A Survey of the Empirical Evidence. Economic Systems37(2) 187216. Elsevier.

    • Search Google Scholar
    • Export Citation

    MishraP.MontielP. andSpilimbergoA. (2012). Monetary Transmission in Low-Income Countries: Effectiveness and Policy Implications. IMF Economic Review60270302.

    • Search Google Scholar
    • Export Citation

    MishraP.MontielP.PedroniP. andSpilimbergoA. (2014). Monetary Policy and Bank Lending Rates in Low-Income Countries: Heterogeneous Panel Estimates. Journal of Development Economics11111731.

    • Search Google Scholar
    • Export Citation

    NelsonE. (2002May). Direct Effects of Base Money on Aggregate Demand: Theory and Evidence. Journal of Monetary Economics49 (4) 687708.

    • Search Google Scholar
    • Export Citation

    SimsC. (1992). Interpreting the Macroeconomic Time Series Facts: The Effects of Monetary Policy. European Economic Review36 (5) 9751000.

    • Search Google Scholar
    • Export Citation

    SimsC. andZhaT. (2006). Does Monetary Policy Generate Recessions?Macroeconomic Dynamics10: 23147.

    WoodfordM. (2001 August 30–September 1). Monetary Policy in the Information Economy. Proceedings from the Federal Reserve Bank of Kansas City Annual Symposium Conference: Economic Policy for the Information Economy1297370. Jackson Hole, Wyoming.

    • Search Google Scholar
    • Export Citation
1This chapter is based on Li et al. (2016).
2Weak and unreliable transmission in the short run is, of course, perfectly consistent with monetary policy providing an effective long-run anchor for inflation.
3The VAR representation of the DSGE solution may be infinite-order; discussed on page 116.
4A one-unit shock to ϵti is then equivalent to one standard deviation of the structural shock to monetary policy.
5For the same reason, the ordering of variables within each of the recursively prior and posterior blocks is irrelevant to obtaining the responses to interest-rate shocks (Christiano, Eichenbaum, and Evans, 1999).
6In the CEE-CB case, the DSGE solution is an exact VAR(1). The true lag length may be infinite in the CEE-PS case, and it would be straightforward to embed a data-driven choice of appropriate lag length. Our baseline VAR(4) estimates differ only trivially, however, from estimates generated by VARs with eight or twelve lags (results available on request). These lag comparisons suggest that any loss of efficiency from suboptimal choice of lag length is not large.
7The inference plots are very consistent with this effect. This suggests that the bootstrapped standard errors calculated by the researcher on each run of data tend to closely approximate the spread of the population distribution.
8The exchange-rate channel is quantitatively important in our model. In simulations not reported here, we show that if only the interest-rate elasticity differs between LIC and non-LIC applications, and not the exchange rate elasticity, the deterioration in the inference about the MTM is mild.
9Our simple model does not incorporate a separate bank lending channel, which would introduce an additional potential source of weak transmission related to imperfect competition and/or high intermediation costs in the banking sector of low-income economies (Mishra et al. 2012, 2013).
10See Chapter 8 for a discussion.
11See Chapter 16 on money targeting in practice and Chapter 8 on reaction functions of this sort.
12It need not be the case that this misspecification strongly attenuates the impulse responses towards zero. Depending on the relative frequency of the different structural shocks and the value of λ (and other parameter values), different outcomes are possible. For example with λ = 0.5, and all structural shocks having equal variances as we have been assuming, the estimated impulse responses are not attenuated towards zero but rather are the opposite of the true ones.
13For example Boughton and Tavlas (1991) used instrumental variables to estimate money demand and money supply shocks via a two-step least squares method. See also Sims and Zha (2006).
14Nelson (2002) argues forcefully that money demand depends on the long interest rate, which would bring all shocks that matter for future interest rates into the money demand equation.
15Kim and Roubini (2000) identify the monetary policy shock by imposing a non-recursive set of behavioral and information restrictions on the B0 matrix. From a DSGE perspective, however, their key exclusion restriction (that countries do not react directly to foreign interest rates) may be hard to justify. See the discussion in Faust and Rogers (2003). Sims and Zha (2006) motivate similar restrictions in a DSGE context, though in a closed-economy context.
16On these points, see Sims (1992), and Leeper and Gordon (1992).

    Other Resources Citing This Publication