International Review of Economics and Finance 20 (2011) 44–58

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International Review of Economics and Finance

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Crisis and self-fulfilling expectations: The Turkish experience in 1994and 2000–2001

Unay Tamgac⁎Department of Economics, University of California, Santa Cruz, CA 95064, United States

a r t i c l e i n f o

⁎ Corresponding author. Tel.: +1 408 596 1175; faxE-mail address: utamgac@ucsc.edu.

1 For how self-fulfilling expectations play an importand Morris (2004) and Keister (2009).

1059-0560/$ – see front matter © 2010 Published bydoi:10.1016/j.iref.2010.07.005

a b s t r a c t

Available online 23 July 2010

In this paper we analyze the role of fundamentals and self-fulfilling expectations in the crisisepisodes of Turkey in 1994 and 2001. The question is howmuch of the occurrence of a crisis canbe attributed to market expectations and how much to fundamentals. The model is estimatedusing a Markov switching framework in which the devaluation expectations affect crisisprobability via three different specifications. Such a framework which allows for sunspotsperforms better than a purely fundamental-based model. The study shows that besides thefundamentals in the economy, shifts in agents' devaluation expectations have played a crucialrole and that a Markov switching model with constant transition probabilities provides betterestimates for the Turkish currency crises.

© 2010 Published by Elsevier Inc.

JEL classification:F31O52

Keywords:Currency crisesExchange rate regimeTurkeySelf-fulfilling expectations

1. Introduction

Financial crises have and remain to be one of themajor research areas in economics. The recent global financial crisis has shownthat more needs to be done to explain, predict and prevent crises. Although the recent crisis is unprecedented in terms of size andcoverage, since the 1990s there have been other instances of crises, most spectacular being: Mexico in 1994; Thailand, Korea, andIndonesia in 1997; Russia in 1998; Brazil in 1999; Turkey and Argentina in 2001.

There are different views regarding the cause of the current financial crisis: capital market imperfections, failure of prudentialsupervision and regulation (Freixas, 2009) lack of lender of last resort as well as agency problems, monetary excess caused by thehousing boom (Taylor, 2009), high risk taking and the low interest monetary policy.

Besides the contribution of these factors to the crisis what was the initial kick for the crisis? Calvo (2009) provides a rationalefor the behavior discontinuity in the recent financial crises. He explains the recent meltdown driven by financial innovation andcollapse in line with the Diamond and Dybvig (1983) bank run literature. The model displays equilibrium multiplicity where noreal shocks are needed and the trigger for the meltdown is viewed as self-fulfilling behavior.

Liquidity is, by its very nature, based on confidence and can therefore be self-fulfilling. “Financial crises can just erupt becausepeople expect that other people expect that financial instruments issued by shadow bankswill be bereft of liquidity” (Calvo, 2009).Financial markets are always subject to self-fulfilling prophecies: if they believe that things will go wrong, things go wrong(Wyplosz, 2007) and the debt crisis in Greece is the latest evidence.

There are other indebted countries in Europe and the fear is that a default on Greece can be a trigger for a wave of speculativeattacks on other potential defaulters, such as Portugal and Spain, an action likely to be self-fulfilling andmassively damaging to thestability and prestige of the currency.1 To prevent such a self-fulfilling speculative attack on the currency Eurozone has agreed on a

: +1 831 459 5077.

ant role in the spread of crises across countries, i.e. contagion, see Goldstein and Pauzner (2004), Guimaraes

Elsevier Inc.

45U. Tamgac / International Review of Economics and Finance 20 (2011) 44–58

rescue package of €110 billion over the next three years including assistance from the International Monetary Fund as well asbilateral loans from the euro area countries.

Experience and researchhas shown thatmarket expectations can contribute to the occurrence of a crisis thatwould not have takenplace if such expectations could have been prevented at first place. Hence any analysis on crises should consider the existence of self-fulfilling expectations. This requires a consideration of the effect of fundamentals together with the possibility of multiple equilibria.

While fundamental-based crises can be predicted (Saqib, 2002) what sets self-fulfilling crises is a harder question. In principleanything could be a trigger to increase devaluation expectations and hence to the generation of a self-fulfilling crisis. This is like“sunspot” dynamics, in which any arbitrary piece of information becomes relevant if market participants believe it is relevant. Self-fulfilling crises can result even when the fundamentals were not week ex-ante. As Obstfeld (1994) points out we do not have aconvincing account of how and when market expectations coordinate on a particular self-fulfilling set of expectations.

This paper is an attempt to understand the factors that make a country vulnerable to a financial crisis. In the middle of the quest forsolution to the current crisis we study the experience of Turkey, an emerging country that has gone through several crises in the pastcouple of decades.We follow a similar approach to Jeanne andMasson (2000) and use aMarkov regime switching framework, explainedin Section 3, which incorporates the effect of self-fulfilling expectations to analyze the Turkish crisis episodes for the period 1980–2005.

Since liberalizing its economy in the 1980s Turkey has experienced twomajor currency crisis episodes: the first in 1994 and thesecond in 2000–2001.2 Since then Turkey has better regulated and supervised its banking system, recapitalized the banks, reducedinflation, reduced the government debt and opened the industry to international competitors, all these contributed to ward off thecurrent crisis. The motivation is to determine the driving factors that lead to the occurrence of the Turkish crisis episodes.Particularly, to test the existence of self-fulfilling behavior and to detect how much of the crises can be attributed to self-fulfillingexpectations and how much to the fundamental components in the economy.

We first estimate the pure fundamentals based model using a standard OLS regression. Then we estimate the model allowingfor multiple equilibria via self-fulfilling expectations using a Markov switching framework. Finally we update the model to allowfor time varying transition probabilities where agents' expectations are influenced by the fundamentals in the economy. Theresults show that shifts in agents' devaluation expectations have played a crucial role in the Turkish crises of 1994 and 2000–2001and that a Markov switching model (MSM) with constant transition probabilities provides better estimates for the crises.

Following is a methodological discussion on regime switching models and their application in the study of currency crises inSection 2. The empirical framework developed by Jeanne and Masson (2000) to test self-fulfilling expectations in crises using aMarkov switching methodology is discussed in Section 3. After the description of the data and the estimation results in Sections 4and 5 respectively the paper is concluded in Section 6.

2. Methodological discussion

2.1. Self-fulfilling crisis

Self-fulfilling crises are not new phenomena. The failure of the “first generation” crisis models to explain the speculative attackson EMS currencies 1992–1993 and Mexico 1994–1995 lead to the development of “second generation models” led by Obstfeld(1996), Eichengreen, Rose, and Wyplosz (1994).3 In this class of models, the interaction between market expectations and policyoutcome can generate self-fulfilling crises. These models are characterized by multiple equilibria, where depending on agents'expectations different outcomes, crisis or no crisis, can occur.

Attempts of estimating escape clause models have been few, which is due in no small part to the difficulty of estimatingnonlinear models with multiple equilibria. One of the initial attempts to detect self-fulfilling expectations is based on Jeanne andMasson (2000). They provide a theoretical framework in which agents' expectations give rise tomultiple equilibria inwhich undercertain macroeconomic conditions; these expectations can generate a self-fulfilling crisis. Linearization of their model gives aMarkov switching regimes model for the devaluation probability. Based on this model they suggest the application of a MSM thatallows for the representation of the agents' expectations as different states in the economy where switches across different statescorresponds to jumps between different equilibria governed by aMarkov process. Their model provides a justification of the use ofthe Markov switching regimes approach in empirical work on currency crises.

2.2. Regime switching models in crisis literature

Regime switching models are used to model time series data in which the behavior of the series seems to change quitedrastically.4 Such apparent changes can be seen in almost any macroeconomic or financial time series and can result from eventssuch as wars, significant change in government policy or financial panics.

After Engel and Hamilton's (1990) pioneering work, there have been many studies that have found that a regime switchingmodel outperforms the random walk or other specifications when it comes to modeling and forecasting exchange rates.5 Thesefindings suggest regime switching models as appealing alternatives in modeling the behavior of exchange rates.

2 2000–2001 crisis is a shortcut to refer to the two consecutive crisis episodes: in November 2000 and February 2001. For references on the Turkish crises referto Celasun (1998), Ozatay and Sak (2002), Ekinci and Erturk (2004).

3 For the first generation crisis models refer to Krugman (1979), Flood and Garber (1984) and Jeanne (1997).4 See Hamilton (1989).5 See Engel (1994), Bergman and Hansson (2005) and Lee and Chen (2006).

46 U. Tamgac / International Review of Economics and Finance 20 (2011) 44–58

Staring with Jeanne and Masson (2000) Markov switching models have been used in the crisis literature. There are variouspapers that employ different regime switching models to detect crisis signals. Jeanne and Masson (2000) use MSM to analyze thespeculation against the French franc in 1987–1993, Boinet, Napolitano and Spagnolo (2002) use a similar MSM for the analysis ofthe 2002 Argentinean crisis, Alvare-Plata and Schrooten (2003) for the EMS crisis. There is also a literature that uses MSM in amultivariate framework (e.g. Sarantis and Piard (2004), Soledad and Peria (2002)).

Abiad (2003) who provides a survey on the empirical crises literature shows that a Markov switching estimation procedureperforms better than probit and signaling models as an early warning system for currency crises. One advantage of regimeswitching model over a deterministic model is their flexibility. By specifying a probability law consistent with a broad range ofdifferent outcomes, and choosing particular parameters within that class based on the data alone this framework allows the datato tell the nature and incidence of significant changes.

MSM provide simplicity compared to the standard models that require many ad hoc assumptions. Using MSM, we can directlyestimate the probability of a crisis. The direct use of a measure of the market pressure instead of a crisis dummy, avoidsinformation loss compared to logit or probit models. Flood andMarion (1999) argue that the change in the SPI jumps is reduced atthe attack time by the extent to which the attack is anticipated. Thus, a binary crisis dummy is more likely to pick up unpredictedcrises. This reduces the share of predictable crises that are likely to be correlated with fundamental economic determinants. Thisintroduces a sample bias into the estimation procedure which is avoided in MSM.

Besides the listed advantages, MSM allow for nonlinearities between the fundamentals and the crisis probability which cannotbe estimated by ordinary regressions. Thus MSM provide an analytical framework to estimate multiple equilibria which is themost important consideration in estimating models with self-fulfilling expectations.

3. The empirical framework

3.1. Markov switching regimes framework to detect self-fulfilling expectations

In the following sections we look at the driving factors of the Turkish crisis episodes in 1994 and 2000–2001. We follow asimilar approach to Jeanne and Masson (2000) and use a MSM to identify the existence of self-fulfilling expectations in the crises.Jeanne and Masson's (2000) model is a based on the second generation crises models where occurrence of crises is jointlydetermined by the fundamentals in the economy and the agents' expectations.

In second generation currency crises models a crisis, which is unlikely to happen when agents do not have crisis expectations,can happen when the agents develop expectations of a crisis occurring hence making the expectations self-fulfilling even thoughthe fundamentals can be far from irrelevant. Higher devaluation expectations lead to higher probability of a devaluation byincreasing the policy maker's desire to devalue, and eventually leads to a self-fulfilling crises. The most obvious way in which theydo so is by raising interest rates. Faced with a dilemma between high interest rates and devaluation, the policymaker may choosethe latter, especially if the fundamental economic situation is fragile. In fact, the policymaker may prefer devaluation to highinterest rates even though she would have maintained the fixed peg if interest rates had been low; in that case, whether or notdevaluation occurs depends purely on market expectations. Jeanne and Masson (2000) show that this idea can be applied for theidentification of self-fulfilling crises using a Markov switching regimes framework. This framework allows the theory of self-fulfilling crises to be brought to the data.

3.2. The model specification

In the model of Jeanne and Masson (2000) the government is assumed to minimize a loss function so there is a continuouscomparison of the government's net benefits from changing the exchange rate versus defending it. The net benefit of the exchangerate peg depends on the fundamentals, and it is sensitive to the devaluation expectations through the level of interest rates. Otherthings equal, higher devaluation expectations force the government to set a higher interest rate, making the peg more costlythrough a number of channels (lower economic activity, fragilization of the banking sector, higher interest burden on publicdebt etc.). The government abandons the peg whenever the net benefits are less than zero given the devaluation expectations inthat state. Thus for each state of devaluation expectations, there is a corresponding level of fundamentals that triggers adevaluation. Agents' expectations are forward looking and expectations are influenced by the state the economy will transition toin the next period. The net benefit of the policy maker, thus the occurrence of a crisis, depends jointly on the current state ofexpectations, the fundamentals and the transition probability from current state to other states. Consequently the level offundamentals that triggers a crisis is different for each state: in states with high devaluation expectations the fundamentalsthresholds for a crisis can be lower than those in which devaluation expectations are higher.

Jeanne and Masson (2000) show that fundamentals based equilibria with different average levels of devaluation expectationsmay coexist. Multiplicity of equilibria makes it possible to construct a model in which the economy jumps across states withdifferent levels of devaluation expectations. They show that this idea can be applied for the identification of self-fulfilling crisesusing a Markov regime switching framework in which the states characterize different devaluation expectations. The transitionacross states, i.e. shift in the agents' expectations, is assumed to follow a Markov process independent of the fundamentals.6

6 Jeanne and Masson (2000) use this model for the experience of French Franc between 1987 and 1993 find that the devaluation expectations were influencedby sunspots.

47U. Tamgac / International Review of Economics and Finance 20 (2011) 44–58

3.3. Markov switching model (MSM)

In the model the devaluation probability at time t, Π t, is specified as

7 Pij a8 The

(Gray, 1

Πt = ast + b1x1;t + b2x2;t + b3x3;t + …et ð1Þ

Where xit are the fundamental variables that affect the probability of a crisis at time t, bi is the coefficient of explanatoryvariable i that will be estimated and et is the normally distributed error term with variance σe

2 . The value of the constant term astdepends on the state st with st=0 or 1.

There are two regimes in the economy. Regime 0 is a so-called “tranquil” regime (low state or no crisis state), during which theprobability of devaluation is low; whereas regime 1, the so-called “taut” regime (high state or crisis state) represents times of higheconomic tensions and reflects periods during which the devaluation probability is considerably higher for the same level offundamentals. Specifically,

In regime 0 no crisisð Þ Πt = a0t + b1x1;t + b2x2;t + b3x3;t + …et ð2Þ

In regime 1 crisisð Þ Πt = a1t + b1x1;t + b2x2;t + b3x3;t + …et ð3Þ

The only difference in the two regimes is the state dependent constant ast. This term identifies the contribution of the agents'expectations to the probability of devaluation. If self-fulfilling elements are in play we expect the model to generate differentestimates for ast. We expect a higher estimate for ast in regime 1 than in regime 0, i.e. that the agents' higher devaluationexpectations lead to higher probability of crisis occurring in the crisis state. The contribution of the fundamental variables to crisisprobability is assumed to be the same in both regimes, reflected by the same coefficients for the explanatory variables in bothregimes.

Whenever the crisis is driven by second generation models we should see a shift in agents' expectations that increase theprobability of devaluation for the same level of fundamentals. Depending on the agents' expectations the economy can be in a lowdevaluation expectations state (regime 0) or in a high devaluation expectations state (regime 1). The regime will switch from lowstate to high state when agents develop high expectations for devaluation and these higher expectations increase the probabilityof a crisis for the same level of fundamentals.

3.4. CTP and TVTP Markov switching model (MSM)

In the first part of the analysis we assume that the regime shifts, i.e. the shifts in expectations of the agents, are governed by a 2state Markov process with CTP from state i to state such that: 7

P St = j jSt−1 = ið Þ = Pij; i; j = 0;1ð Þ for t = 0;…T ð4Þ

Pi0 + Pi1 = 1; i = 0;1ð Þ ð5Þ

The unconditional probabilities that the system is in state i, Pi, can be calculated as:

P0 = P St = 0ð Þ = 1−P112−P00−P11

=P10

P01 + P10

P1 = P St = 1ð Þ = 1−P002−P00−P11

=P01

P01 + P10

ð6Þ

In the CTP framework the switching probability from one state to the other depends only on the initial state at the time ofchange and is exogenously determined. This corresponds to the case where the change in agents' expectations exhibits sunspotdynamics, uncorrelated with any information about the fundamentals in the economy.

In Section 5.3 we allow for the transition probabilities to vary with time and use the MSM with Time Varying TransitionProbabilities (TVTP) which involves the nonlinear parameterization of the transition probabilities in terms of a set ofpredetermined explanatory variables.8 In this specification the probability that St= j depends not only on the value of St−1 but alsoon a vector of exogenous variables zt, where zt may include elements of xt. Formally:

P St = j jSt−1 = i; St−2 = k;…; ztð Þ = P St = j jSt−1 = i; ztð Þ = Pij ztð Þ ði; j = 1;2;…NÞ ð7Þ

Where Pij(zt)may have either a probit or logistic specification of the history of the economic-indicator variables zt={zt, zt−1…}.In this framework the probability of switching from one regime to another is time varying and endogenously determined. In our

re conditional probabilities, they specify the probability to be in state i, given that the previous state was j.first work in this area is by Lee (1991), followed by the studies on business cycle fluctuations (Filardo, 1994; Ghysels, 1992), interest rate dynamics996), bubbles and asset pricing (van Norden & Schaller, 1997), and exchange rates (Diebold, Lee & Weinbach, 1994; Engel & Hakkio, 1994).

Fig. 1. Evolution of the variables e, r and i.

9 The CTP model is a nested alternative to the TVTP model. It corresponds to the case where the last (k−1) terms of the (k x 1) transition probabilityparameter vectors are set to zero so that pt00 and pt

11 and are constant, meaning the economic-indicator variables are not informative about the evolution of thestate of the economy.10 This method, called as an ad hoc method to measure exchange market pressure, is based on the earlier work by Girton and Roper (1977) and is firsintroduced by Eichengreen et al. (1994) for the identification of crises.11 There are variations in the construction of the SPI and some studies only use exchange rate and reserve changes and omit the interest rate differential such asKaminsky and Reinhart (1999).12 Eichengreen at al. (1994) conduct a sensitivity analysis to measure the effect of different weighting schemes for the index. They showed that differenweights do not have significant impact on the empirical results.

48 U. Tamgac / International Review of Economics and Finance 20 (2011) 44–58

analysis we assume the transition probabilities to evolve according to a logistic function of economic fundamentals. This logisticfunctional formmaps the information variables, zt, into the open interval (0, l) and thereby guarantees a well-defined log-likelihoodfunction.

P00t = P St = 1 jSt−1 = 1; ztð Þ = pðztÞ =

exp z0tβo

� �1 + exp z

0t−1βoð Þ

P11t = P St = 0 jSt−1 = 0; ztð Þ = qðztÞ =

exp z0tβ1

� �1 + exp z

0tβ1ð Þ

ð8Þ

zt is a (kx1) vector of economic variables that affect the transition probabilities, βiis a (kx1) vector of parameters for state i

wherethat govern the transition probabilities from that state.9 Finally in Section 5.4 we estimate the model with regime dependentcoefficients.

The TVTP specification is consistent with the idea that the agents' expectations can be influenced by fundamentals in theeconomy. In this setting we acknowledge that the periods leading to crisis are intrinsically different from tranquil, non crisisperiods since there is an association between the agents' expectations and the state of the economy. Therefore the occurrence of acrisis cannot be considered as the result of sunspot equilibrium alone.

4. Data description and sources

4.1. Definition of a crisis: the dependent variable

The dependent variable is the estimate of the devaluation probability,Πt. Jeanne andMasson (2000) approximate the devaluationprobability with the interest differential (i− i⁎) between Euro–DM and Euro–Franc instruments. However in the crisis literature it iscommon to look for a broader range of indicators and include episodes of unsuccessful attacks, inwhich the government could defendthe currencyby a sizable loss in international reserves or through large increases in domestic interest rates.We follow thismethod anduse the Speculative Pressure Index (SPI) to proxy for the devaluation probability.10 The SPI is calculated as theweighted average of themonthly percentage changes in the real effective exchange rate (Δe) and international reserves (Δr) and monthly change of theinterest rate differential [Δ(i− i⁎)=Δi].11

SPI = we%Δe–wr%r–wiΔi ð9Þ

Following Eichengreen et al. (1994) to equate the conditional volatilities of the three components we use theweights as inverselyrelated to the standard deviation of each of the three variables12:

SPI =1σe

� �*

Δeet−1

� �− 1

σr

� �*

Δrrt−1

� �+

1σi

� �* Δið Þ ð10Þ

t

t

13 In the literature a currency crisis is identified by “large” changes in the SPI, relative to their “normal” values. “Large” defined as changes in the index thaexceed a threshold value. The threshold value is usually taken as the mean plus two standard deviations, provided that it also exceeds five percent. If index valueexceeds the threshold level, a crisis signal is issued. The studies also use alternative criteria in their identification of the “threshold” value.14 Kaminsky et al. (1998) provide a list of 103 indicators used in literature.15 The full set of the explanatory variables are: Bank deposits/M2 (level and growth), bank reserves/total bank assets, current account/GDP, central bank credito banks/total bank liabilities, foreign debt/exports, short term foreign debt/reserves, short term debt, government fiscal deficit/GDP, private credit by domestimoney banks/GDP, export growth, import growth , loans/deposits (level and growth), growth rate of broad money to reserves, growth rate of M1 ,cumulativenon-FDI flows, growth rate of portfolio investment/GDP, industrial production, world real interest rate, growth rate of real domestic credit, growth rate of reaGDP, deviation of the real exchange rate from trend ,stock market performance, terms of trade, trade balance, external debt, long term debt, short term debtexports.16 For some variables that were available only in a lower frequency the conversion to a monthly frequency was made via quadratic interpolation. Thesevariables are: external debt, GDP, GDP deflator, current account, direct investment, portfolio investment, government budget balance and government's debposition.

Fig. 2. Components of SPI.

49U. Tamgac / International Review of Economics and Finance 20 (2011) 44–58

σe, σr, σi are the standard deviations of the exchange rate, international reserves and the interest rate differential over the

wheresample period respectively.13

An increase in the index reflects stronger selling pressure on the domestic currency (a depreciation pressure). That way theindex captures either a successful attack (a sharp devaluation), or a successful defense (the monetary authorities deter an attackby a combination of interest rate increases and foreign market interventions and the exchange rate remains unchanged), or anunsuccessful defense (all three variables move sharply).

We use the CPI based trade weighted effective exchange rate as the real exchange rate. Reserves correspond to the totalinternational reservesminus gold and the interest rate differential is calculated as the difference between Turkish andUS threemonthdeposit interest rates. There is a noticeable variation in the behavior of these variables during crisis times of 1994 and 2000–2001(Fig. 1). Specifically there is a sharp fall in the real exchange rate and reserves and a rise in the interest rate differential. The crisis timesare evident from the weighted components of the SPI (Fig. 2). The exact timing of the crisis can be seen from the spikes of the SPI inFig. 3, and the crisis dummy is construed from the SPI for a threshold level of mean plus two standard deviations in Fig. 4.

4.2. Crisis indicators: the explanatory variables

Studies have found that different set of variables have different explanative power for different countries. As Kaminsky, Lizondoand Reinhart (1998) conclude currency crises seem to be usually preceded by multiple economic problems and an effective warningsystemshould consider a broad variety of indicators.14 Since there is nodefinite rule for the “right” set of variableswe consider a broadset of indicators, including generalmacroeconomic variables, indicators related with the real sector, the financial sector, government,aswell as political and institutional variables,whichmight have influenced the Turkish crisis episodes. The data is obtained from IMF'sInternational Financial Statistics (IFS; CD-ROMversion), The TurkishUnder Secretariat of Treasury and the electronic data distributionsystem of Central Bank of the Republic of Turkey.15

The analysis is carried for the post liberalization period of Turkeywhich starts in 1980, untilMay2005.Monthly data is used since itis more useful as a policy tool. Being available almost immediately with a fifteen to twenty days lag it gives the policymaker a fasterknowledge of the proximity of a crisis and a faster reaction capacity when the indicators start sending signals.16

5. Estimation results

5.1. OLS estimation

First we assume there is nomultiple equilibria and estimate themodel without regime switches using OLS. This allows us to test apurely fundamentals based model. After estimating the model for different combinations of variables we a get reduced the set ofvariables that have the highest R-square values. The results of the OLS estimation for these variables are presented in Table 1. All

t

tc

l/

t

17 For the EM algorithm see Dempster, Laird, and Rubin (1977).

Fig. 3. Speculative Pressure Index (SPI).

Fig. 4. Currency crisis dummy.

50 U. Tamgac / International Review of Economics and Finance 20 (2011) 44–58

variables except the current account to GDP ratio, and export growth have the expected signs. The positive coefficient for exportgrowth is consistent with the findings of Kaminsky and Reinhart (1999) that export growth is lower under balance of payments andtwin crisis episodes. Theexpected signon import growthcanbeeitherwaysince import growth is expected todeclineduringabalanceof payments crisis, and rise during a banking crisis. The estimation results show a negative coefficient as would be predicted by abalance of payments crisis. The significant variables are: the deviation of real exchange rate from trend, annualized growth rate of M2to reserves ratio, trade balance, growth rate of the ratio of bank deposits to M2 and ratio of short term debt to reserves.

The OLSmodel has a poor performance since it gives a false crisis signal in 1990 as can be seen from the predicted SPI and crisissignals in the upper and lower panels of Fig. 5 respectively.

5.2. CTP–MSM estimation

Next we estimate the devaluation probability allowing for multiple equilibria using the MSM explained in Section 3.3 byimplementing the Expectation Maximization (EM) algorithm, programmed in RATS. 17 We use only the reduced set of the 16variables based on the OLS estimation results. Since the MSM estimation is sensitive to missing observations the estimation iscarried over the period 1987:01–2005:12 where there are no missing data points.

Table 1OLS estimation results.

Variable Coefficient Std error T-stat Significance

Constant −5.080 ⁎⁎⁎ 1.193 −4.258 0.000Deficit/GDP 3.355 3.897 0.861 0.390Current account/GDP 22.145 17.540 1.263 0.208Deviation of REER from trend −3.298 ⁎ 1.872 −1.762 0.080Annual real GDP growth −0.028 0.021 −1.358 0.176Domestic credit growth 0.132 0.107 1.229 0.220M2/reserves growth 0.236 ⁎⁎⁎ 0.056 4.198 0.000Trade balance −0.001 ⁎⁎⁎ 0.000 −2.728 0.007Stock market growth −0.055 0.087 −0.634 0.527Bank deposits/M2 growth −9.753 ⁎⁎⁎ 3.451 −2.826 0.005CBCRE_BASSET 0.045 4.494 0.010 0.992Loans/deposits growth 0.412 0.409 1.007 0.315Short term debt/reserves 0.004 ⁎⁎ 0.002 2.368 0.019External debt/exports 0.041 0.026 1.580 0.116Export growth 1.855 1.219 1.522 0.130Import growth −1.329 1.016 −1.307 0.193DMB_private credit/GDP 0.400 0.327 1.223 0.223

⁎ Denotes variables significant at 10%.⁎⁎ Denotes variables significant at 5%.⁎⁎⁎ Denotes variables significant at 1%.

51U. Tamgac / International Review of Economics and Finance 20 (2011) 44–58

We test the null hypothesis of a single regime by comparing the parameter estimates of the two models. The strength andexistence of self-fulfilling expectations can be interpreted from the difference of the coefficient estimates for the constant term astin the two regimes.

The result of the two-regime CTP–MSMestimation is presented in Table 2. All the coefficients estimates, except for the deficit toGDP ratio, have the same sign as in the OLS estimation. The estimate for the constant term, which captures the importance of theagents' devaluation expectations, is−2.606 in regime 0 (a0) and 3.463 in regime 1 (a1), both significant at the 1 percent level. Thisshows that we can distinguish between two states in the economy, one with lower devaluation expectations and the other withhigher devaluation expectations.

The estimatedmatrix of transition probabilities below shows that two fairly persistent regimes do exist, with the probability ofa high devaluation expectation state being much lower.

Θ = P00 P10P01 P11

� �= 0:97989 0:49742

0:02011 0:50258

� �ð11Þ

The steady state probabilities of the states are calculated using Eq. (6).

P0 = steady state probability of state 0 low stateð Þ = 0:96114P1 = steady state probability of state 1 high stateð Þ = 0:03886

The estimatedSPI under the two regimesusing theparameter estimatesof theCTP–MSMare shown in Fig. 6. The jumps to regime1from regime 0 during the 1994 and 2000–2001 crises can be seen from the plots and the estimated SPI from the CTP–MSM closelymatches the realized SPI.

The smoothed probability estimates of being in state 1 (high state), P(st|YT), which is based on all T observations of Yt, asgenerated by the RATS program, are shown in the lower panel of Fig. 7 together with the plot of the dependent variable, the SPI, inthe upper panel. These probabilities correctly pick the two crisis episodes in 1994 and 2000–2001 showing that during both crisesthe economy had switched to a high devaluation expectations state. The CTP–MSM allows us to correctly estimate the Turkishcrisis episodes. The switch of the economy to a high devaluation expectations state during both crises shows that that the agents'increased expectations for devaluation had a significant contribution to the occurrence of the crisis. Thus we can conclude thatbesides the deteriorating fundamentals, self-fulfilling expectations were in play in the Turkish crisis episodes.

5.3. TVTP–MSM estimation

In the CTP–MSM, the transition probabilities are assumed to be constant over the estimation period. In other words thelikelihood that the regime will shift into a taut or a tranquil state is time independent and exogenously determined. We relax thisassumption and allow the transition probability to depend on the fundamentals in the economy. This modifiedmodel is estimatedusing the TVTP framework explained in Section 3.2 where the transition probabilities are defined as a function of the exogenousvariables.

Fig. 5. OLS estimation results. Estimated SPI and crisis probability based on OLS estimation results.

52 U. Tamgac / International Review of Economics and Finance 20 (2011) 44–58

Transition probabilities are assumed to evolve according to the logistic function in Eq. (8). The vector of economic variables thataffect the transition probabilities, zt, consists of the same 16 economic indicators which are our fundamental variables xt. Excludingthe missing observations the model is estimated over the period 1987:01–2005:12.

The result of the two-regime TVTP–MSM estimation is presented in Table 3. Annualized growth rate of ratio of M2 to reserves,trade balance, growth rate of bank deposits to M2, short term debt to reserves, and external debt to exports are the significantvariables. We again get two different estimates for the constant term: −2.419 in regime 0 (a0) and 4.361 in regime 1 (a1) whichare both highly significant. This shows that, allowing the transition probabilities to vary endogenously, we estimate two differentstates in the economy: one with lower devaluation expectations and one with higher devaluation expectations.

The estimated values of the SPI under the two regimes using the parameter estimates of the TVTP–MSM are shown in Fig. 8. Theestimated value of SPI follows closely the realized value and a switch from regime 0 to regime 1 occurs during the crisis times in1994 and 2000–2001.

The dependent variable, the SPI and the smoothed probability estimates of being in regime 1 (high state) for the TVTP–MSMestimation as generated by the RATS program are displayed in the upper and lower panel of Fig. 9 respectively. As can be seen themodel correctly picks up the 1994 and 2000–2001 crisis by showing a spike for the probability of being in a crisis state (regime 1).However the existence of another spike at the end of 1998 shows that TVTP–MSM performs relatively poorly compared to theCTP–MSM by signaling a crisis that did not occur.18

18 Since the transition probabilities are time varying we cannot construct the transition probabilities matrix for the TVTP–MSM and nor can we calculate thesteady state probabilities.

Table 2CTP–MSM estimation results.

Variable Coefficient Std error T-stat Significance

P12 0.020 ⁎⁎ 0.008 2.424 0.015P21 0.497 ⁎⁎⁎ 0.180 2.765 0.006A0 −2.606 ⁎⁎ 1.052 −2.477 0.013A1 3.463 ⁎⁎ 1.360 2.546 0.011Deficit/GDP −8.186 ⁎ 4.559 −1.796 0.073Current account/GDP 19.003 18.651 1.019 0.308Deviation REER from trend −1.636 1.580 −1.035 0.301Annual real GDP growth −0.025 0.017 −1.410 0.158Domestic credit growth 0.073 0.089 0.818 0.413M2/reserves growth 0.203 ⁎⁎⁎ 0.049 4.180 0.000Trade balance 0.000 ⁎⁎ 0.000 −2.141 0.032Stock market growth −0.065 0.077 −0.838 0.402Bank deposits/M2 growth −5.019 3.017 −1.664 0.096CBCRE_BASSET 3.537 4.030 0.878 0.380Loans/deposits growth 0.363 0.412 0.883 0.377Short term debt/reserves 0.001 0.001 0.917 0.359External debt/exports 0.013 0.021 0.593 0.553Export growth 0.430 1.026 0.420 0.675Import growth −0.252 0.864 −0.292 0.771DMB_private credit/GDP 0.487 0.352 1.383 0.167Sigma (σe

2) 1.465 ⁎⁎⁎ 0.075 19.540 0.000

⁎ Denotes variables significant at 10%.⁎⁎ Denotes variables significant at 5%.⁎⁎⁎ Denotes variables significant at 1%.

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Among the coefficient estimates for the βi's, the vector of parameters that govern the transition probabilities, only thecoefficient of short term debt to reserves ratio for switching from regime 1 to 2 (P10) is significant.19 This suggests that theeconomic fundamentals do not have a good power to predict the shifts in the agents' devaluations expectations.

Both in the CTP and TVTP models the coefficient estimate of the constant term is larger in the high devaluation expectationsstate (regime 1). This shows that besides the deteriorating fundamentals, agents' devaluation expectations were in play in theTurkish crisis episodes. In both models, during crisis times the probability of being in regime 1 is estimated as one, i.e. crisesoccurred when agents developed higher expectations for crisis occurring. Thus a crisis is influenced by agents' expectationsmaking the crisis self-fulfilling.

Both the CTP and the TVTPmodels correctly pick the two crisis episodes, however the CTPmodel does a better job at predictingof the occurrence of crises. This shows that market expectations are not fundamentals driven. The expectations cannot beexplained by the economic fundamentals; rather they are like sunspot dynamics. Therefore the fundamental variables are notenough to predict a crisis.

We have shown that devaluation probability is jointly determined by the fundamentals in the economy and the marketexpectations. The importance of the fundamentals is evident by the significance of the external variables, and the existence of twosignificant states with different devaluation expectations indicates the importance of expectations.

5.4. CTP–MSM with variable coefficients estimation

Finally we estimate a CTP–MSM with regime dependent coefficients:

19 Therequest20 The

Πt = ast + b1;stx1;t + b2;stx2;t + b3;stx3;t + …est ð12Þ

Where es,t is the normally distributed error term of regime i, with variance σei2 . Specifically,

In regime 0 no crisisð Þ Πt = a0 + b1;0x1;t + b2;0x2;t + b3;0x3;t + …e1;t ð13Þ

In regime 1 crisisð Þ Πt = a1 + b1;1x1;t + b2;1x2;t + b3;1x3;t + …e2;t ð14Þ

In this model the fundamental variables' effect on to the devaluation probability differs depending on the state of the economy,reflected through the different coefficient estimates in the two regimes. The estimation is again carried over the period 01-2005:12.The estimated SPI under the two regimes in Fig. 10 and the smoothed transitionprobabilities in Fig. 11 shows that themodel has a poorpredictive power.20

results for the coefficient estimates for theβi`s, the vector of parameters that govern the transition probabilities, are available from the author upon.results of this estimation are available from the author upon request.

Fig. 6. CTP Markov switching simulation results. Estimated SPI based on CTP–MSM results.

Fig. 7. CTP Markov switching estimation results. Estimated SPI and smoothed transition probability of regime 1 based on CTP–MSM results.

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6. Conclusion

In this paper we test whether self-fulfilling expectations were in play in the 1994 and 2000–2001 Turkish crisis episodes. We usethe methodology by Jeanne and Masson (2000) that employs a Markov switching model (MSM) for the estimation of the crisisprobability assuming two states corresponding to the market expectations of low and high devaluation probabilities. By allowingnonlinearities MSM can be used to estimate models with multiple equilibria. The MSM generates better estimates compared to afundamentals based OLS model, indicating that two states of the economy with different devaluation expectations exists and crisisoccurs whenever agents' have high devaluation expectations.

Table 3TVTP–MSM estimation results.

Variable Coefficient Std error T-stat Significance

A11 −2.42 ⁎⁎⁎ 0.16 −14.86 0.00A21 4.36 ⁎⁎⁎ 0.57 7.70 0.00B (deficit/GDP) −3.16 2.70 −1.17 0.24B (current account/GDP) 2.01 12.66 0.16 0.87B (deviation of REER from trend) −1.24 1.17 −1.06 0.29B (annual real GDP growth) −0.01 0.01 −1.00 0.32B (domestic credit growth) 0.10 0.07 1.30 0.19B (M2/reserves growth) 0.20 ⁎⁎⁎ 0.04 4.96 0.00B (trade balance) −0.0005 ⁎⁎⁎ 0.00 −61.44 0.00B (stock market growth) −0.03 0.06 −0.48 0.63B (bank deposits/M2 growth) −7.26 ⁎⁎⁎ 2.45 −2.96 0.00B (CBCRE_BASSET) −1.14 2.89 −0.40 0.69B (loans/deposits growth) −0.03 0.29 −0.10 0.92B (short term debt/reserves) 0.00 ⁎⁎ 0.00 2.63 0.01B (external debt/exports) 0.02 ⁎⁎ 0.01 2.60 0.01B (export growth) 0.61 0.75 0.81 0.42B (import growth) −0.19 0.70 −0.28 0.78B (DMB_private credit/GDP) 0.20 0.16 1.26 0.21Sigma (σe

2) 1.47 ⁎⁎⁎ 0.06 22.84 0.00

⁎ Denotes variables significant at 10%.⁎⁎ Denotes variables significant at 5%.⁎⁎⁎ Denotes variables significant at 1%.

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The model is estimated using different regime specifications. We test the model using a TVTP–MSM and a CTP–MSM withregime dependent coefficients on the external variables. These specifications perform poorly compared to the initial CTP–MSMspecification. The two-regime CTP–MSMwith state dependent constant term gives the best estimates of the crisis probabilities andcan detect the exact timing of the Turkish crises.

TheMSM allows us to jointly determine the effect of self-fulfilling expectations and fundamentals in the Turkish crisis episodes.The study shows that besides the fundamentals in the economy, shifts in agents' expectations have played an important role in theTurkish crisis episodes. A crisis happens whenever agents' expectations of devaluation increases, i.e. the regime shifts to a tautstate from a tranquil state. Thus the Turkish crises can be viewed as a case of multiple equilibria where shift in agents' expectationsplayed a role in the occurrence of the crisis making it self-fulfilling. The transition probabilities between the two states ofexpectations cannot be predicted with economic fundamentals, which indicate that the Turkish crisis episodes were driven bysunspot equilibria.

The study shows that MSM can be used in crisis models with multiple equilibria where we observe behavior discontinuity likein the recent financial crisis. Financial markets are always subject to self-fulfilling prophecies, specifically through agents'expectations and investor confidence. Self-fulfilling behavior can function through various channels: through interest rates as in

Fig. 8. TVTP Markov switching simulation results. Estimated SPI based on TVTP–MSM results.

Fig. 9. TVTP Markov switching estimation results. Estimated SPI and smoothed transition probability of regime 1 based on TVTP–MSM results.

Fig. 10. CTP–MSM with regime dependent coefficients simulation results. Estimated SPI based on CTP–MSM with regime dependent coefficients results.

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the current Greek debt crisis, through inflationary expectations as in the EMS currency crises or through investor confidence as inthe asset bubble as in the 2000 dot-com crisis. We have shown that, besides the economic fundamentals, such expectations are animportant factor for the occurrence of a crisis. Whenever expectations are not fundamentals driven, the CTP–MSM model shouldprovide better estimates.

Market expectations can contribute to the occurrence of a crisis that would not have taken place if such expectations couldhave been prevented at first place. Some examples of such self-fulfilling expectations are the collapse of a fixed exchange rateregime, the burst of an asset bubble, the failure to service the debt, or a bank run. Thus there is good reason to take preventivemeasures before it is too late. Howeverwe have shown that self-fulfilling expectations aremore like sunspot behavior, they are not

Fig. 11. CTP–MSMwith regime dependent coefficients estimation results. Estimated SPI and smoothed transition probability of regime 1 based on CTP–MSMwithregime dependent coefficients results.

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fundamentals driven. Thus it may not be possible for a country to prevent self-fulfilling expectations, just as it cannot controlexternal factors. However it is possible to reduce domestic vulnerabilities that might aggravate the impact of negative externalshocks and give rise to self-fulfilling behavior.

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