Deciphering The Message in Japanese De‡ation Dynamics
Paul D. McNelis and Naoyuki Yoshinoy
January 2004

Abstract
This paper attempts to "decipher", with linear and nonlinear neural network regime switching (NNRS)
models , the message in Japanese de‡ation dynamics.

The NNRS model is superior to the linear

model in terms of in-sample speci…cation tests as well as out-of-sample forecasting accuracy. The mostimportant variables a¤ecting in‡ation are interest rates and the output gap.

Given the zero lower

bound on interest rates, our analysis suggests that policies aimed the reversing the output gap throughstimulating investment are the most important escape channels from the de‡ationary spiral.
JEL Classi…cation: E0, E3, E5
Keywords: de‡ation, neural networks, regime-switching models
Department of Economics, Georgetown University, Washington, D.C. Email: McNelisp@georgetown.edu
Email:yoshino@econ.keio.ac.jp.

y Department of Economics, Keio University, Tokyo, Japan.

11Introduction

Japan has been in de‡ation for over a decade, as Figure 1 shows:
Figure 1

Krugman (1998) comments on this experience of Japan:
Sixty years after Keynes, a great nation - a country with a stable and e¤ective government, a massive
net creditor, subject to none of the constraints that lesser economies face - is operating far below its productive
capacity, simply because its consumers and investors do not spend enough. That should not happen; in allowing
it to happen, and to continue year after year, Japan’s economic o¢ cials have subtracted value from their nationand the world as a whole on a truly heroic scale. [Krugman (1998): Introduction].

Krugman recommends expansionary monetary and …scal policy to create in‡ation. However, Yoshino
and Sakakibara have taken issue with Krugman’s remedies. They counter Krugman in the following way:
Japan has reached the limits of conventional macroeconomic policies. Lowering interest rates
will not stimulate the economy, because widespread excess capacity has made private investment
insensitive to interest rate changes. Increasing government expenditure in the usual way will havesmall e¤ects because it will take the form of unproductive investment in the rural areas. Cutting taxes
will not increase consumption because workers are concerned about job security and future pension
and medical bene…ts [Yoshino and Sakakibara (2002): p.110].

Besides telling us what will not work, Yoshino and Sakakibara o¤er alternative longer-term policy prescriptions, involving …nancial reform, competition policy, and the reallocation of public investment:
In order for sustained economic recovery to occur in Japan, the government must change themakeup and regional allocation of public investment, resolve the problem of nonperforming loans in thebanking system, improve the corporate governance and operations of the banks, and strengthen theinternational competitiveness of domestically oriented companies in the agriculture, construction andservice industries [Yoshino and Sakakibara (2002): p.110].

Both Krugman and Yoshino and Skakibara base their analysis and policy recommendations on analyticallysimple models, with reference to key stylized facts observed in macro-economic data.
Of course, there is no scarcity of proposed "remedies" for the Japanese de‡ation problem within academic

2

discussion and policy analysis. . Svensson (2003) has recently reviewed many of these and has put forward
his own "foolproof" way. His "foolproof" remedy has three key ingredients: …rst, a upward-sloping pricelevel target path set by the central bank, secondly, an initial depreciation followed by a "crawling peg",
and third, an exit strategy with abandonment of the peg in favor or in‡ation or price-level targeting when
the price-level target path has been reached [Svensson (2003): p. 15].

Other remedies include a tax onmoney holding, proposed by Goodfriend (2002) and Buiter and Panigirtzoglou (1999) as well as targeting
the interest rate on long-term government bonds, proposed by Clouse et al (2003), and Meltzer (2001).
The growth of low-priced imports from China has also been proposed as a possible cause of de‡ation in
Japan (as in Hong Kong).

McKibbin (2002) has argued that monetary policy would be e¤ective in Japanthrough yen depreciation. He argues for a combination of a …scal contraction with a monetary expansion
based on depreciation:
Combining a credible …scal contraction that is phased in over three years with an in‡ation target
would be likely to provide a powerful macroeconomic stimulus to the Japanese economy, through aweaker exchange rate and lower long term real interest rates, and would sustain higher growth in Japanfor a decade [McKibbin (2002), p. 133].

In contrast to Krugman and Yoshino and Skakibara, McKibbin based his analysis and policy recommendations on simulation of the calibrated "G-Cubed" (Asia Paci…c) dynamic general equilibrium model,
outlined in McKibbin and Wilcoxen (1998).
Sorting out the relative importance of monetary policy, stimulus packages which a¤ect overall demand(measured by the output gap), as well as the contributions of unit labor costs, falling imported goods prices,and …nancial-sector factors coming from the collapse of bank lending and asset-price de‡ation (measured
by the negative growth rates of share price and land price indices) is no easy task. These variables display
considerable volatility and the response of in‡ation to these variables is likely to be asymmetric.
Most studies of in‡ation have relied on linear extensions and econometric implementation of the Phillips
curve or New Keynesian Phillips curve. While such linear applications are commonly used and have beensuccessful for many economies, we show in this paper that a nonlinear smooth-transition neural network
regime-switching method outperforms the linear model on the basis of in-sample diagnostics and out-ofsample forecasting accuracy.
Regime switching models have been widely applied to macroeconomic analysis of business cycles, initially with "linear" regimes switching between periods of recession and recovery [see Hamilton (1989, 1990)].
Similarly, there have been many studies examining nonlinearities in business cycles, which focus on thewell-observed asymmetric adjustments in times of recession and recovery [see Teräsvirta, and Anderson(1992)]. This paper follows in this tradition by applying a nonlinear regime switching model to the in‡ationary/de‡ationary experience of Hong Kong.
The next section dicusses the key variables we use to analyze the dynamics of in‡ation and de‡ation.
Section 3 contains our model speci…cation while Section 4 discusses the estimation method.

3

Section 5 isanalysis of our key empirical results and the last section concludes.

2

Key Variables for Assessing In‡ation

In this section we examine the interest rate, the output gap, the rates of growth of import prices and unit
labor costs, two …nancial sector indicators, the rates of growth of the Hang Seng index and residential
property prices, and the growth of bank lending.
The interest rate, of course, represents the key channel of monetary policy. We use the current-period
short-term Gensaki rate as a measure or "indicator" of the stance of monetary policy.

The output gap,

which measures the "slack" in the economy, is de…ned as the di¤erence between the logarithm of actual
output less the logarithm of "potential" output, y p ; while potential output is given by the Hodrick-Prescott…lter. Figures 2 and 3 picture these variables. We see that the interest rate e¤ectively reached its positive"lower bound" by 1995, while measures of the output gap show that the economy has been well "below
potential" (as Krugman point out) by factors of three or four percent in recent years.
Figure 2

Figure 3

The behavior of imported goods prices and unit labor costs, both important for understanding the supplyside or costs factors of in‡ationary movements, show considerably di¤erent patterns of volatility. Figure 4
pictures the rate of growth of imported goods prices while Figure 5 shows the corresponding movement inlabor costs.

4

Figure 4

Figure 5

Figure 6 pictures the …nancial sector variables , the rates of growth of bank lending, the share priceindex (the Nikkei index), and the land price index. Bank lending of often cited as the "credit channel" ofmonetary policy, while asset price "in‡ation" or "de‡ation" factors are used as …nancial sector "pressures"a¤ective overall demand and in‡ation.

Not surprisingly, the share price index shows much more volatility

than the growth of bank lending and the land price index.
Figure 6

Table I contains a statistical summary of the data we use in our analysis We use quarterly observations
from 1970.1 until 2001.1. Table I lists the means, standard deviations, and contemporaneous correlationsof annualized rates of in‡ation, the Gensaki rate, the output gap, and changes in import prices, unit laborcosts, the land price index, the share price index, and overall bank lending.
5

Table I

The highest volatility rates (measured by the standard deviations of the annualized quarterly data) arefor the rates of growth of the share market and import price indices.
Table I further shows that the highest correlation of in‡ation is with the Gensaki rate, but that it ispositive rather than negative. This is another example of the well known "price puzzle", recently analyzed
by Giordani (2001). This "puzzle" is also a common …nding of linear vector-autoregressive (VAR) models,
which show that an increase in the interest rate has positive, rather than negative, e¤ects on the price level in
impulse-response analysis. Sims (1992) proposed that the "cause" of the prize puzzle may be "unobservable"
contemporaneous supply shocks. The policy makers observe the shock and think it will have positive e¤ects
on in‡ation, so they raise the interest rates in anticipation of countering higher future in‡ation. Sims foundthat this puzzle disappears in US data when we include a commodity price index in a more extensive VAR
model.
Table I also shows that the second and third highest correlations of in‡ation are with unit labor costs andbank lending, followed by import price growth.

The correlations of in‡ation with the share-price growthrate and the output gap are negative but insigni…cant.
Finally, what is most interesting from the information given in Table I is the very high correlation between
the growth rate of bank lending and the growth rate of the land price index, not with the growth rate of theshare price index. It is not clear which way the causality runs: does the collapse of land prices lead to afall in bank lending, or does the collapse of bank lending lead to a fall in land prices?
Gerlach and Peng (2003) have examined the interaction between banking credit and property prices inHong Kong. They found that property prices are "weakly exogenous" and determine bank lending, whilebank lending does not appear to in‡uence property prices [Gerlach and Peng (2003): p. 11]. They argue
that changes in bank lending rates cannot be regarded as the source of the boom and bust cycle in HongKong. They hypothesize that "changing beliefs about future economic prospects led to shifts in the demandfor property and investments". With a higher inelastic supply schedule, this caused price swings, and withrising demand for loans, "bank lending naturally responded" [ [Gerlach and Peng (2003): p. 11].
In Japan, the story is di¤erent: banking credit and land prices show bidirectional causality or feedback.
The collapse of land prices reduces bank lending, but the collapse of bank lending also leads to a fall in land6

prices. Table II gives the joint-F statistics and the corresponding P-values for a Granger test of causality.
We see that the results are somewhat stronger for a causal e¤ect from land prices to loan growth. However,the p-value for causality from loan growth to land price growth is only very slightly above …ve percent.
These results indicate that both variables have independent in‡uences and should be included as …nancial
factors for assessing the behavior of in‡ation.
Table II

We acknowledge that econometric estimation for policy analysis, of course, is subject to the Lucas (1976)
critique. Key parameter values would could hardly be expected to remain constant when monetary policy"regimes" change, so we do not argue that policy prescriptions should be based on econometric model estimation alone. Clearly, good policy should come from a combination of clear analytical thinking, exempli…edby the diagnoses of Krugman, and Yoshino and Sakahibara, from rigorously constructed and calibrated dynamic general equilibrium models, as well as econometric estimation. The role of econometrically-estimatedmodels is to provide a "secondary model" for capturing in a more precise fashion the "stylized facts" which
the analytical and dynamic general equilibrium models are attempting to account for or explain.

3

Speci…cation

3.1

Phillips Curve ModelWe draw upon the standard Phillips Curve framework used by Stock and Watson (1999) for forecastingin‡ation in the United States. They de…ne the in‡ation as an h-period ahead forecast. For our quarterly
data set, we set h=4 for an annual in‡ation forecast:t+h = ln(pt+h )ln(pt )

(1)

We thus forecast in‡ation as an annual forecast (over the next four quarters), rather than as a one-period
forecast. We do so because policy makers are typically interested in the in‡ation prospects over a longer
horizaon than one quarter. For the most part, in‡ation over the next quarter is already "in process" andchanges in current variables will not have much e¤ect at so short a horizon.
In this model, in‡ation depends on a set of current variables xt , including current in‡ationof in‡ation, and a disturbance termt:t , and lags

This term incorporates a moving average process with innovations

t , normally distributed with mean zero and variance

7

2

:

= f (xt ) +

t

(2)

t

= ln(pt )ln(pt h )

(3)

t

=

t+h

t

where

(L) are lag operators.

t+(L) t 1

(4)

N (0;

2

(5)

)

Besides current and lagged values of in‡ation,t ; ::: t k ,the variables

contained in xt include measures of the interest rate, it ,the output gap, ytgap , de…ned as the di¤erence between
actual output yt and potential output ytpot , the (logarithmic) price gap with mainland China pgap
t ,the rate of
growth of unit labor costs (ulc) and the rate of growth of import prices (imp). The vector xt also includes
two …nancial-sector variables: changes in the share price index (spi), the residential property price index(rpi), and bank lending, (b) :
xt

=

[ t;

t 1;

h ulct ;The operator

gapt k ; it ; yt ; :::t 2 ; :::;
h impt ;h spit ;h rpit ;h bt ]h for a variable zt represents simply the di¤erence over h periods.

(6)
Hence

h zt = zt

zt h :The rates of growth of unit labor costs, the import price index, the share price index, and the residential
property price index thus represent annualized rates of growth for h = 4 in our analysis.
for consistency with our in‡ation forecast, which is a forecast over four quarters.

We do thisIn addition, taking log

di¤erences over four quarters helps to reduce the in‡uence of seasonal factors in the in‡ation process.
The disturbance termt consists of a current period shockt in addition to lagged values of this shock.

We explicitly model serial dependence, since it is well known that when the "forecasting interval" h exceeds
the sampling interval (in this case we are forecasting for one year but we sample with quarterly observations),
temporal dependence is induced in the disturbance term.

3.2

Linear and Neural Network Regime Switching Speci…cation

To make the model operational for estimation, we specify the following linear and neural network regimeswitching (NNRS) alternatives.
The linear model has the following speci…cation:
t+h

=

xt +

t

=

t+

(L) t 1

(8)

N (0;

2

(9)

t

t

)

We nest this linear speci…cation within a more general NNRS model:

8

(7)

t+h

t
t

=

=

xt + f[ ( t 1 ; ; c)]G(xt ; ) +[1( t 1 ; ; c)]H(xt ; )g +t+

(L) t 1

(11)

N (0;

2

(12)

t

)

(10)

The NNRS model is similar to the smooth-transition autoregressive model discussed in Frances and van
Dijk (2000), originally developed by Teräsvirta (1994), and more generally discussed in van Dijk, Teräsvirta,
and Franses (2000). The function( t 1 ; ; c) is the transition function for two alternative nonlinear ap-

proximating functions G(xt ; ) and H(xt ; ):The transition function depends on the value of lagged in‡ationand threshold c. We use a logistic or logsigmod speci…cation for( t 1 ; ; c) =1 + exp[t 1 as well as the parameter vector( t 1 ; ; c) :

1
( t 1

(13)

c)]

For simplicity we set the threshold parameter c = 0, so that the regimes divide into periods of in‡ation andde‡ation.

As Frances and van Dyck (2000) point out, the parameter

determines the smoothness of thechange in the value of this function, and thus the transition from the in‡ation to de‡ation regime.
The functions G(xt ; ) and H(xt ; ) are also logsigmoid and have the following representations:G(xt ; ) =H(xt ; )=1
1 + exp[
1
1 + exp[

xt ]
xt ]

(14)
(15)

The in‡ation model in equation (11) has a "core" linear component, including autoregressive terms, amoving average component, and a nonlinear component incorporating "switching regime" e¤ects, which isweighted by the parameter :For values of( t 1 ; ; c) strictly less than 1 and strictly greater than zero, the nonlinear regime-switchingcomponent resembles a familiar "feedforward" or multiperceptron neural network of two neurons with ajump connection in one hidden layer. Figure 7 pictures the "architecture" of such our NNRS model as a"neural network" architecture.

9

Figure 7

Figure 7 shows that components of input vector x [x1; x2; x3], directly a¤ect the "output" [y] via alinear direct connectors, pictured by the "straight line" at the bottom of the chart. However, the nonlinear
system works through the new "neurons", G and H, in the single hidden layer. This neurons transform the
input variables [x1; x2; x3]. The functions G and H in turn a¤ect the …nal output variable Y through theweighting function.

As van Dijk, Teräsvirta, and Franses (2000) point out, the advantage of incorporating a neural network
comes from the fact that such a network, with a …nite number of hidden units, can "approximate any
continuous function to any desired degree of accuracy" [see Hornik, Stinchcombe, and White (1989, 1990)].

4

Estimation Method

We estimate the models given by equations by maximum likelihood methods. We initially set the autoregressive lag structure and moving average order to four, given our quarterly observations. Since we are interested
in both in-sample explanatory power, out-of-sample forecast accuracy (as well as economic insight), and wish
to avoid "data snooping", we …x the lag structure for the AR or MA components at a reasonably liberal
length of four for both.1
The linear model is rather straightforward.

For the NNRS model, we have two problems:a larger

1 For a forecast horizon of four quarters, with quarterly data, we expect at a minimum a third-order moving average error

process.

10

number of parameters to estimate, and the very high possibility, given the nonlinear functional forms, that
we will obtain coe¢ cients which are local, rather than global, optima.

This is a well-known problem innonlinear optimization in general, and there is no "silver bullet" to overcome it.
To increase our chances of …nding coe¢ cients close to a global optima, we …rst estimate the coe¢ cients ofthe model with a "evolutionary stochastic" search, called the genetic algorithm. The algorithm starts witha population of p initial guesses, [01 ;02 :::

0p ], for the coe¢ cient set f; ; ; ; ; g. It then updates

the population of guesses by genetic selection, breeding and mutation, for many generations, until the bestcoe¢ cient vector is found among the last-generation "population", which maximizes the likelihood function.
The genetic algorithm does not involve taking gradients or second derivatives, and thus avoids the problem of "blowing up" or "crashing" during an estimation process.

It is a global and evolutionary search

process. We “score” the variously randomly-generated coe¢ cient vectors by the objective maximum likelihood function, which does not have to be smooth and continuous with respect to the coe¢ cient set. De

Falco (1998) applied the genetic algorithm to nonlinear neural network estimation, and found that his results"proved the e¤ectiveness" of such algorithms for neural network estimation.
The main drawback of the genetic algorithm is that it is slow. For even a reasonable size or dimensionof the coe¢ cient vector, the various combinations and permutations of elements ofwhich the genetic

search may …nd “optimal”or close to optimal, at various generations, may become very large. This is anotherexample of the well-known “curse of dimensionality” in non-linear optimization. Thus, one needs to let thegenetic algorithm “run”over a large number of generations— perhaps several hundred— in order to arrive atresults which approximate unique and global optima.
Since most nonlinear estimation methods rely on an arbitrary initialization of
global search with a Quasi-Newton gradient estimation.

, we follow-up the genetic

This estimation is known as a hybrid approach.

We run the genetic algorithm for a reasonable number of generations, and then use the …nal weight vector
as the initialization vector for the gradient-descent or simulated annealing optimization. We repeat thisprocess several times, and choose the coe¢ cient estimates from the …nal set of estimates which optimize thelikelihood function.

4.1

In-Sample Evaluation

We estimate the model initially for the entire data set.

We use the following diagnostics:

the sum-of squared errors [SSE], the multiple correlation coe¢ cient [R2 ]; the Hannan-Quinn (1979) information
criterion [HQIF ], the marginal signi…cance of the Ljung-Box (1978) [LB] Q-statistic for serial dependence
in the residuals, as well as that of the MacLeod-Li (1983) [M L] Q-statistic for serial dependence in thesquared residuals, the Engle-Ng (1993) [EN ] test for symmetry of residuals, the Jarque-Bera (1980) [JB]
test of normality of residuals and the Brock-Deckert-Scheinkman (1987) [BDS] test of nonlinearity in theresiduals Finally, the Lee-White-Granger (1992) [LW G] test gives the number of signi…cant regressions of

11

the residuals against 1000 randomly generated nonlinear combinations of regressors.2

4.2

Forecasting Accuracy

For evaluating the out-of-sample forecasting performance of the two competing models, we use the "realtime" forecasting approach of Stock and Watson (1999).
1990.1.

We …rst estimate the model from 1980.1 until

We forecast the dependent variable (year-on-year in‡ation) for the second quarter of 1990, andobtain the …rst "forecast error" of our exercise.

Then we incorporate this observation, and estimate themodel from 1980.1 until 1990.3, and forecast the dependent variable for the fourth quarter of 1990, andobtain the second forecast error of our exercise. We continue this method of rolling one period forecasting
until we exhaust our sample.
Needless to say, with nonlinear estimation, this method takes time.

However, the advantage of thismethod of evaluating out-of-sample performance of two competing models (the linear and the NNRS models),is that it re‡ects how economists de facto do their forecasting. Economists are always updating coe¢ cientsas new data and new information become available.
For comparing the forecasting performance, we use the root mean squared error [RM SQ] value and theDiebold-Mariano (1995) [DM ] test of relative out-of-sample performance.3

4.3

Evaluation and Signi…canceTo assess the relative importance and statistical signi…cance of the estimation results, we …rst have to obtain
the partial derivatives implied by the neural network coe¢ cient estimates.
Since we use logsigmoid functional forms for the two regimes, we can calculate the partial derivatives
rather easily. The derivative of the logsigmod function G is simply G (1G). Thus, for the linear model,

the partial derivatives of the in‡ation forecast with respect to xj;t is simply

j ; for all observations t.

Thecorresponding neural network partial derivative is given by the following expression:
@ t+h
=
@xj;t

j +

[

t Gt (1

Gt ) j + (1t )Ht (1Ht ) j ](16)

Equation (17) comes from applying the familiar "chain rule" method for taking the partial derivative of
equation (11) with respect to argument xj;t .
Since the nonlinear partial derivatives are "state-dependent", we compute the partial derivatives of theneural network for three di¤erent states: at the beginning of the sample, at the sample mid-point, and at
the end of the sample.4
Of course, any discussion of the relative importance of the determinants of in‡ation and de‡ation has
to consider their "statistical signi…cance".

The di¢ culty of obtaining tests of signi…cance of nonlinear

2 All of these statistical tests are clearly summarized in Franses and van Dijk (2000).
3 These tests are also summarized in Franses and van Dijk (2000).
4 Stefan Gerlach made this suggestion at a seminar at the Hong Kong Institute of Monetary Research.

12

parameter estimates or state-dependnet partial derivative estimates should not be underestimated. All too
often, asymptotic t-statistics based on the inverted Hessian matrices of coe¢ cient estimates simply "blowup" or fail to invert.
As an alternative we use the "bootstrapping" method, due to Efron (1979) and Efron and Tibshirani(1993).

Bootstrapping consists of sampling the original set of "residuals" from the network model, withreplacement. Then we re-set the dependent variable equal to the original forecast plus the "resampled"
residual vector, re-estimate the model, and obtain new coe¢ cients and partial derivatives. We repeat thisprocess 1000 times, and we …nd a distribution of the partial derivatives, from which we calculate probability
values indicating if the initially estimated partial derivatives are signi…cantly di¤erent from zero.5

5Analysis of Results

5.1

In-Sample Evaluation

Table III shows that the NNRS model clearly dominates the linear model on virtually all criteria for model
speci…cation. In terms of explanatory power, even when we adjust for the increased complexity of the NNRS
model relative to the linear model by the Hannan-Quinn criterion, the results strongly favor the selection of
the NNRS model. The Q-statistics for serial independence in the regression residuals and squared-regression
residuals lead to rejection for the linear model. For the NNRS model, the Q-statistic for serial independence
in the residuals cannot be rejected, barely, at the …ve percent level of signi…cance, though it can be rejected
at the ten percent level.

However, the tests for serial independence in the squared residuals cannot berejected.
The in-sample diagnostics indicate that the linear model, with four lags of the dependent variables anda MA(4) error process, is not a well-speci…ed model. However, for the same lag structure of the dependent
variable and the same MA(4) error process, the NNRS model appears to be well-speci…ed. Since we value
parsimony in speci…cation of the lag structure and error process, we proceed with this speci…cation.
5 Mark Taylor suggested this approach for an earlier version of this paper.

13

Table III

5.2

Forecasting Accuracy

The linear model, though failing in-sample speci…cation tests relative to the NNRS model, may still be used
for purposes of forecasting, and compared with the corresponding performance of the NNRS model. The
relative performance of the two models for the sequence of 56 one-period forecast errors appears in Figure
8.

We see that the NNRS model does either better or equally as well as the linear model.

We note inparticular that during sharp up-turns or sharp downturns, the NNRS forecast error is usually lower thanthat of the linear model.

14

Figure 8

Table IV summarizes the out-of-sample forecasting performance of the two models.

We see that the

NNRS model reduces the root mean squared error by a factor of more than 50 percent. The Diebold-Mariano
statistics show that we can reject at a high degree of con…dence the null hypothesis of zero di¤erences in theout-of-sample forecast errors.
Table IV

5.3

Evaluation and Signi…cance

Table V contains the partial derivative estimates of the NNRS models, as well as the bootstrapped p-values(marginal signi…cance values) at three periods, 1981.1, 1992.1 and 2002.1 The …rst period, 1981.1, is a timeof steady in‡ation, the period of 2002.1 is a time of continued de‡ation, whilte the mid-point period, 1992.1,

15

representing a time of in‡ation volatility, at the end of the …rst Gulf War. Since the diagnostics indicatethat the linear model is suspect on grounds of speci…cation errors, given the parsimonious lag structure andMA(4) error process, we evaluate only NNRS model for assessing de‡ationary dynamics in Japan.
Table V

Table V shows that the Gensaki rate, the output gap, and the in‡ation rate at a two period lag are theonly signi…cant variables. We see that the Gensaki rate is signi…cant and negative in all three periods. The
NNRS model thus evades the familiar "price paradox" problem obtained by estimation of linear models. We
also see that the magnitude of the interest rate e¤ect is about the same in 2002 as in 1981. Of course, theproblem for the Bank of Japan is not the potential e¤ects of interest rate cuts on in‡ation but the feasibilityof making cuts, since the interest rate is bounded below by zero.
We also see that the output gap has the expected positive sign on in‡ation in all three periods, andthat the e¤ect in 2002 is only slightly below its value in 1981. The sharp fall in the output gap after 1997
cannot help but be the major factor driving de‡ation, given the partial deriviatives implied by the coe¢ cientestimates of the model.
What is surprising in the model is that the rate of growth of property prices and unit labor costs not
only are insigni…cant but also have the wrong signs. The rate of growth of import prices, bank lending, andthe Nikkei index, while insigni…cant, have the expected positive signs.
The results of Table V indicate that interest rates and the output gap are the most important andsigni…cant determinants of price changes, both in the "in‡ation" regime and in the "de‡ation" regime.
What about the adjustment of the smooth transition probability? Figure 9 pictures this estimate (inthis case, the probability of being in the de‡ation state), along with the annual in‡ation rates.

This …gureshows that the probability was between .3 and .4 in the early 1980’s, around .5 in the early 90’s and has beenin the neighborhood of .7 since 2000. While it has falled slightly, it has remained well above .5 since 1997.
More importantly, the …gure does not show any noticeable downward trend in recent years, indicating the
likelihood of an "escape" from de‡ationary dynamics.
16

Figure 9

Overall, the NNRS model points to the collapse of overall demand (through the output gap e¤ect) as wellas inability of interest rates to fall below zero, as the main factors behind Japanese de‡ationary dynamics.
While the rate of growth of bank lending is not signi…cant, this does not mean that it is unimportant. Table
VI pictures the results of a Granger causality test between the output gap and the rate of growth of bankleanding in Japan. We see strong evidence, at the …ve percent level of signi…cance, that the rate of growthof bank loans is a causal factor for changes in the output gap. There is also evidence of reverse causality,from the output gap to the rate of growth of bank lending, to be sure. These results indicate that a reversal
in bank leanding will improve the output gap, and such an improvement will call forth more bank leanding,leading, in turn, in a virtuous cycle, to further output-gap improvement.
Table VI

What our econometric analysis suggests is that policies aimed at restructuring the …nancial sector, which
would stimulate both bank lending, would in turn generate new investment spending, reverse the negative
output gap, and help break the de‡ationary spiral.

17

6Conclusion

The results indicate that …ghting Japanese de‡ation is not simply a matter of expansionary monetary policy.
Expansion of bank lending and other …nancial restructuring policies aimed at improving investment and theoverall growth in gdp are also necessary to push the economy out of the de‡ation trap, even with expansionary
low interest-rate policies in place.
These results should not be so surprising. Unlike Hong Kong, Japan is a relatively more closed economy.
Policies aimed at domestic demand are likely to be much more important factors for in‡ation than in a small,
highly open economy such as Hong Kong.

In‡ation or de‡ation in asset prices and import prices do notplay the same role in Japanese in‡ation and de‡ation cycles as they would in a very small but much more
open economy.
The contrast between linear and very simple neural network speci…cations indicates that these approaches
may be very useful for capturing or "deciphering" key stylized facts about de‡ation

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