Final Thesis MSC Economics

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INTRODUCTION 1

Transcript of Final Thesis MSC Economics

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INTRODUCTION

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Chapter No 1

INTRODUCTION

Interest   Rate

Definition: Interest rate is the percent charged, or paid, for the use of money. It

is charged when the money is being borrowed, and paid when it is being loaned.

Interest is paid by a bank when money is deposited because the bank uses that

money to make loans. The bank then charges the borrower a little more for that

same money so it can make a profit for its service. When the central banks set

interest rates, such as the U.S. Fed Funds rate, it is the amount they charge

other banks to borrow money. This is a critical interest rate, in that it affects the

entire supply of money, and hence the health of the economy.

Examples: High interest rates can cause a recession.

Interest rate. Interest rate is the percentage of the face value of a bond or the

balance in a deposit account that you receive as income on your investment. If

you multiply the interest rate by the face value or balance, you find the annual

amount you receive.

For example, if you buy a bond with a face value of $1,000 with a 6% interest

rate, you'll receive $60 a year. Similarly, the percentage of principal you pay for

the use of borrowed money is the loan's interest rate. If there are no other costs

associated with borrowing the money, the interest rate is the same as the annual

percentage rate (APR).

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Inflation

Inflation is the pervasive and sustained rise in the aggregate level of prices for

goods and services measured over a given period (Morris and Morris 1999).

Repetitive price increases erode the purchasing power of money and other

financial assets that have fixed values, creating serious economic distortions and

uncertainty. Inflation occurs when actual economic pressures and the anticipation

of future developments cause the demand for goods and services to exceed the

supply available at existing prices or when available output is restricted by

faltering productivity and marketplace constraints.

What Is Inflation?

Inflation is defined as a sustained increase in the general level of prices for

goods and services. It is measured as an annual percentage increase. As

inflation rises, every dollar you own buys a smaller percentage of a good or

service.

The value of a dollar does not stay constant when there is inflation. The value of

a dollar is observed in terms of purchasing power, which is the real, tangible

goods that money can buy. When inflation goes up, there is a decline in the

purchasing power of money. For example, if the inflation rate is 2% annually,

then theoretically a $1 pack of gum will cost $1.02 in a year. After inflation, your

dollar can't buy the same goods it could beforehand.

There are several variations on inflation:

Deflation is when the general level of prices is falling. This is the opposite

of inflation.

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Hyperinflation is unusually rapid inflation. In extreme cases, this can lead

to the breakdown of a nation's monetary system. One of the most notable

examples of hyperinflation occurred in Germany in 1923, when prices rose

2,500% in one month!

Stagflation is the combination of high unemployment and economic

stagnation with inflation. This happened in industrialized countries during

the 1970s, when a bad economy was combined with OPEC raising oil

prices.

In recent years, most developed countries have attempted to sustain an

inflation rate of 2-3%.

Causes of Inflation

Economists wake up in the morning hoping for a chance to debate the causes

of inflation. There is no one cause that's universally agreed upon, but at least

two theories are generally accepted:

Demand-Pull Inflation - This theory can be summarized as "too much money

chasing too few goods". In other words, if demand is growing faster than

supply, prices will increase. This usually occurs in growing economies.

Cost-Push Inflation - When companies' costs go up, they need to increase

prices to maintain their profit margins. Increased costs can include things

such as wages, taxes, or increased costs of imports.

How Is It Measured?

Measuring inflation is a difficult problem for government statisticians. To do this,

a number of goods that are representative of the economy are put together into

what is referred to as a "market basket." The cost of this basket is then compared

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over time. This results in a price index, which is the cost of the market basket

today as a percentage of the cost of that identical basket in the starting year.

In North America, there are two main price indexes that measure inflation:

Consumer Price Index (CPI) - A measure of price changes in consumer

goods and services such as gasoline, food, clothing and automobiles. The

CPI measures price change from the perspective of the purchaser. U.S.

CPI data can be found at the Bureau of Labor Statistics. 

Producer Price Indexes (PPI) - A family of indexes that measure the

average change over time in selling prices by domestic producers of

goods and services. PPIs measure price change from the perspective of

the seller. U.S. PPI data can be found at the Bureau of Labor Statistics.

You can think of price indexes as large surveys. Each month, the U.S. Bureau of

Labor Statistics contacts thousands of retail stores, service establishments,

rental units and doctors' offices to obtain price information on thousands of items

used to track and measure price changes in the CPI. They record the prices of

about 80,000 items each month, which represent a scientifically selected sample

of the prices paid by consumers for the goods and services purchased.

In the long run, the various PPIs and the CPI show a similar rate of inflation. This

is not the case in the short run, as PPIs often increase before the CPI. In general,

investors follow the CPI more than the PPIs.

Problem Statement

In this study we are interested to reduce the higher inflation and interest rate.

Their causes and effects in Pakistan so that we may find the solution to solve the

problem of inflation and interest rate.

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Objectives of The Study

This thesis introduced a relationship between interest rate and inflation rate. It

effects the economic fluctuations and as well as currency level. So there’s an

inverse relationship between inflation and interest rate. Whenever you hear the

latest inflation update on the news, chances are that interest rates are mentioned

in the same breath.

Objective of this study is too specifying a link between the budget deficit and

taxes and consumption whether this link is positive and negative. We are

interested to give policy recommended according to this result of our study.

Hypothesis 1: H0: There is no significant relationship between inflation and interest rate. H1: There is significant relationship between inflation and interest rate. 2: H0: There is no significant relationship between interest rate and inflation. H1: There is significant relationship between interest rate and inflation.

Limitations

Due to nature of study and availability of time and resources there can be some

various which might be left out from this analysis.

Sources of Data

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Central bank of Pakistan, Economy of Pakistan, Economy survey, Statistical year

book.

Document Structure

This thesis is structured as follows:

Chapter 2 outlines research and reviews empirical literature from both developed

and developing countries. The chapter then examines alternative explanations

put forward by researchers in an attempt to explain why hypothesis lacks

empirical consistency.

Chapter 3 examines the theoretical framework and its effect on the strength of

the hypothesis. The chapter begins by briefly describing the role of different

economists with their views, outlining its responsibilities, legislative and

governing structure. This is followed a detailed description of inflation-targeting

monetary policy framework, including an outline of the rate of inflation and the

role that monetary policy plays in determining interest rates. The various

channels of the monetary transmission mechanism are then explored. The

chapter concludes by analyzing the dynamics between inflation-targeting,

credibility and the long-run real rate of interest.

Chapter 4 describes the econometric methodology used in the study, the data

and the results are presented.

Chapter 5 provides a summary of the findings, policy implications, limitations and

future research.

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LITERATURE REVIEW

Chapter No 2

LITERATURE REVIEW

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An interest rate is the price a borrower pays for the use of money he does not

own, and the return a lender receives for deferring the use of funds, by lending it

to the borrower. Interest rates are normally expressed as a percentage rate over

the period of one year. Interest rates targets are also a vital tool of monetary

policy and are used to control variables like investment, inflation, and

unemployment. There are many causes of interest rate like; deferred

consumption, Inflationary expectations, Alternative investment, Risks of

investment, Liquidity preference, Taxes.

Inflation is defined as a sustained increase in the general level of prices for

goods and services. It is measured as an annual percentage increase. As

inflation rises, every dollar you own buys a smaller percentage of a good or

service. The value of a dollar does not stay constant when there is inflation. The

value of a dollar is observed in terms of purchasing power, which is the real,

tangible goods that money can buy. When inflation goes up, there is a decline in

the purchasing power of money.

So there is a typically an inverse relationship between inflation and interest rate,

high interest rates equals low inflation, low interest rates = high inflation. For

example; if there is more money in an economy, people tend to spend more, thus

(as a whole) driving up the cost of goods and services. If there is less money in

an economy, there is less to spend and low demand equals lower prices.

If interest rates are low, money is easier and cheaper to borrow, hence more

money in an economy. If rates are high, it is more expensive to borrow, hence

less money in an economy.

There is also a concept know as stagflation, when interest rates and inflation

both increase, such was the case in the Carter Administration. External market

factors or market manipulation may cause stagflation. Economy is like an ocean -

very difficult to see the depth. Inflation and interest rate is razor sharpened on

both side When interest rate rise money flow will be towards secured debt market

and liquidity is contained - funds availability in the market will be rationalized - it

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does not guarantee lower inflation - in fact it will have higher inflation because of

cash flow management - Bank lending rate will be higher and it will have

cascading effect of higher production cost - higher inflation. Short term capital

inflow will further confuse the strong economy.

If rate is low (int. cut) debt market will sink and there will be more liquidity and

this fuel inflation and outflow of capital to other countries where interest rate is

higher or to sound economy (out of country) will this reduce the currency

valuation. This will also fuel inflation.

Yasuhide yajima (1997) to enhance the effect of monetary policy, the central

bank has been shifting away from its conventional stance of taking action without

elaborating reasons, to a new stance of conveying its policy intentions to financial

markets. In the interest of a more efficient dialogue with the market, the

transparency and accountability of monetary policy must be improved. In line with

this thinking, there have been calls for the bank to introduce inflation targeting.

With inflation targeting, the central bank would announce medium-term inflation

rate targets, and give these targets top priority in monetary policy, while at the

same time being accountable for explaining the current status of its policies.

Because the bank would announce targets and release information pertaining to

implementation measures and policy evaluations, its policies would be accessible

to the public.

A number of countries introduced inflation targeting during the 1990s. New

Zealand was first to do so in 1990, followed by Canada and Israel in 1991,

England in 1992, in most countries; inflation targets have been set to achieve a

consumer price inflation rate of

Around 3%. None of the countries has targeted an inflation rate of zero. This can

be attributed to the fact that most of them have suffered from high inflation rates

in the past, and to the view that an inflation rate near zero is undesirable.

Due to its short history, the general effectiveness of inflation targeting has yet to

be verified quantitatively. However, results from the eight countries show that

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inflation rates of around 10% in the 1980s have subsided following the

introduction of inflation targeting. Even allowing for the existence of other factors.

Sebastian Edwards (2006) for decades the exchange rate was at the center of

macroeconomic policy debates in the emerging markets. In many countries the

nominal exchange rate was often used as a way of bringing down inflation; in

other countries -- mostly in Latin America – the exchange rate was used as a

way of (implicitly) taxing the export sector. Currency crises were common and

were usually the result of acute (real) exchange rate overvaluation. During the

1990s academics and policy makers debated the merits of alternative exchange

rate regimes for the emerging economies. Based on credibility-based theories

many authors argued that developing and transition countries should have hard

peg regimes – preferably currency boards or polarization. One of the main

arguments for favoring rigid exchange rate regimes was that emerging

economies exhibited a “fear to float.”2 After the currency crashes of the late

1990s and early 2000, however, a growing number of emerging economies

moved away from exchange rate rigidity and adopted a combination of flexible

exchange rates and “inflation targeting.” Because of this move the exchange rate

has become less central in economic policy debate in most emerging markets.

This, however, does not mean that the exchange rate has disappeared from

policy discussions. Indeed, with the adoption of inflation targeting a number of

important exchange rate-related questions – many of them new – have emerged.

In this paper I address three broad policy issues related to inflation targeting (IT)

and exchange rates that have become increasingly important in analyses on

monetary policy in emerging countries.

•First, I deal with the effectiveness of the nominal exchange rate as a shock

absorber in IT regimes. This issue is related to the extent of the “pass-through”

from the exchange to domestic prices. I argue that much of the literature on pass

through has missed the important connection between “pass-through” and

exchange rate effectiveness as a shock absorber.

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•Second, I analyze whether the adoption of IT has had an impact on exchange

rate volatility. Many authors have pointed out that since IT requires (some degree

of) exchange rate flexibility, it necessarily results in higher exchange rate

volatility. This, however, is not a very interesting statement. A more useful

analysis would separate the effects of IT, on the one hand, and of a more flexible

exchange rate regime, on the other, on exchange rate volatility. This is what I do

in section III of the paper.

And third, I discuss whether in an inflation targeting regime the exchange rate

should affect the monetary policy rule. I point out that, from an analytical

perspective, this is still an unresolved issue. At the policy level, very few IT

central banks openly recognize using the exchange rate as a (separate) term in

their policy rules (i.e. Taylor rules). However, existing empirical evidence

suggests that almost every central bank does take exchange rate behavior into

account when undertaken monetary policy.

Berna Kocaman (2006) Stock prices can go up and down frequently. These

changes supposedly reflect the changing demand for that stock (and its potential

resale value) or changing expectations of a company’s profitability. Therefore,

investors wonder how stock prices are determined. Shares in most large

established corporations are listed on organized exchanges like the Istanbul

Stock Exchange or New York Stock Exchange. Shares in smaller or newer firms

are listed on the NASDAQ-an electronic system that tracks stock prices. Every

time a stock is sold, the exchange records the price at which it changes hands. If,

a few seconds or minutes later, another trade takes place, the price at which that

trade is made becomes the new market price, and so on. Organized exchanges

like the New York Stock Exchange will occasionally suspend trading in a stock if

the price is excessively volatile, if there is a severe mismatch between supply

and demand (many people wanting to sell, no one wanting to buy) or if they

suspect that insiders are deliberately manipulating a stock’s price. But in normal

circumstances, there is no official arbiter of stock prices, (no person or institution

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that “decides” a price). The market price of a stock is simply the price at which a

willing buyer and seller agree to trade.

One of the most-widely reported figures in the financial press is the price to

earnings (P/E) ratio. Traditionally, the P/E ratio has been assumed to be an

indicator of the quality of an investment; a relatively low P/E ratio implies a good

investment, whereas a relatively high P/E ratio indicates a “poor” investment

prospect. Given the apparent widespread market interest in the P/E ratio,

considerable academic research has investigated the role of the ratio in market

valuation of securities. For example, Basu (1983) provides evidence that P/E

ratios can be used to construct portfolios which outperform the market and

Ohlson (1983; 1989a) employs analytical framework to relate market prices of

equity securities to capitalized earnings. Given the wide acceptance of the P/E

ratio in practice and the academic interest in theoretical relationships between

prices and earnings, it is no great surprise that many recent studies have studied

why P/E ratios differ across firms. Beaver and Morse (1978) and Zarowin (1990)

investigate variables, including growth, risk and differences in accounting

practices that may explain some of the cross-sectional variation in P/E ratios

among United States firms. A related series of studies is concerned with

observable differences in P/E ratios across countries (Aron, 1989; Bildersee et

al., 1990; Lee and Livant, 1991; Poterba and French, 1989; Scheineman, 1989).

Dontoh, Livnat, and Todd (1993) investigated the relationships among P/E ratios,

growth and risk, after deriving exact definitions for growth and risk from a simple

theoretical model. They defined growth in terms of expected earnings beyond

“normal” earnings, where normal earnings are a function of the risk-free rate, the

firm’s dividend policy and random shocks. Similarly, they defined risk in terms of

the uncertainty in forecasting future growth (i.e., abnormal earnings) rather than

the usual systematic risk measure or “beta”. The relationship between P/E ratios

and these measures of abnormal earnings and estimation risk is shown to be

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non-linear and their empirical tests incorporate this nonlinearity into the research

design.

Dontoh, Levant and Todd (1993) developed and empirically tested a theoretical

model relating growth (abnormal earnings) and estimation risk to one of the most

fundamental financial indicators, the earnings / price ratio. They found that

growth in excess of the risk-free rate, that is, in excess of the long-term expected

return for firm in equilibrium, is theoretically negatively related to the E/P ratio,

i.e., the E/P ratio is decreasing in the growth rate. Similarly, the model suggests

that the E/P ratios should be positively associated with estimation risk; the

greater is the uncertainty about future abnormal earnings the smaller is the price

and the larger is the E/P ratio. Their model indicates that the relationship

between E/P ratios and both growth and estimation risk is not linear, and should

be estimated appropriately. Their results also indicate the importance of growth

(abnormal earnings) and the estimation risk to the explanation of cross-sectional

variation of E/P ratios both across and within various countries. Their model and

associated results also document the importance of variation in short-term

interest rates as an explanatory variable for cross-country variation of E/P ratios.

Bulent Guler and Umit Ozlale (2004) Inflation uncertainty may affect interest

rates for safer assets through two distinct channels. First, an increase in

uncertainty will raise the interest rates by an increase in the inflation risk

premium. Such a relationship is documented in several studies like Refs.. On the

other hand, there is another channel,’ flight to quality’’, that has not been

investigated previously in this context. According to that view, an increase in

inflation uncertainty might lead to a perceived increase in economy-wide risk,

prompting a ‘‘flight to quality’’ by investors, in which their demand for safe assets

like treasury bills rises. This increase in demand for these assets would tend to

lower the associated interest rates. Therefore, a negative relation between

inflation uncertainty and interest rates for safe assets may also exist.

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This study takes the above discussion as its starting point and investigates the

validity of these two different channels for the United States economy. Apart from

the studies that have dominated the literature, inflation uncertainty is modeled in

a time-varying parameter framework with GARCH specification. Such a

methodology is originally introduced by Evans (1991) and can be used to identify

several types of inflation uncertainties: Uncertainty can either emerge from the

randomness in the structure of inflation process that we name as ‘‘structural

uncertainty’’, or it can be generated by the shocks that hit the economy, which we

name as ‘‘impulse uncertainty’’. The effects of these two distinct types of

uncertainties on safe assets can vary significantly. We find that while structural

uncertainty effects treasury bills positively as expected, impulse uncertainty

generates a ‘‘flight to quality’’ effect and it is negatively associated with safe

interest rates.

Why do increases in the Fed’s target for the federal funds rate raise interest rates

on long-term Treasury securities? One might expect that concretionary monetary

policy would raise short-term rates because of a liquidity effect and reduce long-

term rates by lowering expected inflation. Yet Cook and Hahn (1989) reported

that increases in the Fed’s target for the federal funds rate during the September

1974 – September 1979 period raised interest rates at all horizons. Similarly,

Kuttner (2001) found that unanticipated increases in the federal funds rate target

over the June 1989 – February 2000 period increased interest rates at all

maturities.

One interpretation of Cook and Hahn’s (1989) and Kuttner’s (2001) results is that

contraction monetary policy raises longer-term real interest rates. The nominal

interest rate equals the real interest rate plus the expected inflation rate. If

contraction monetary policy lowers expected inflation or leaves it unchanged,

then evidence that it increases the nominal interest rate implies that it must be

increasing the real interest rate also.

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Romer and Romer (2000) provided an alternative explanation for these findings.

They showed that the Fed has substantially more information about future

inflation than is available from commercial forecasts. Their results imply that the

optimal strategy for individuals with access to both the Fed’s forecasts and

commercial forecasts would be to rely exclusively on the Fed’s forecasts. They

also demonstrated that Federal Reserve policy actions reveal some of the Fed’s

private information about inflation. An increase in the federal funds rate target

could thus increase interest rates by raising expectations of future inflation.

Gürkaynak, Sack, and Swanson (2005a) presented evidence indicating that

increases in the federal funds rate target have the opposite effect, lowering

expected inflation over the 1990 – 2002 periods. They found that unexpected

increases in the funds rate target lower the one-year forward rate ten years

ahead. They also found that real long-term forward rates derived from inflation-

indexed Treasury securities do not respond to monetary policy surprises, while

the compensation for inflation responds with a significant negative coefficient to

positive innovations in the federal funds rate target. They interpreted these

findings to mean that surprise increases in the federal funds rate target lower

future expected inflation.

Campbell (1995) noted that the effect of funds rate increases on inflation

expectations should depend on the Fed’s credibility in fighting inflation. If the

Fed’s anti-inflationary policies are credible, then they should forestall increases in

longer run expected inflation. If they are not credible, then they may increase

both shorter-term real rates and longer-term expected inflation. Bernanke and

Mishkin (1997) have argued that central bank credibility in financial markets

depends on delivering low inflation. Inflation in the U.S. in the 1970s was high

and volatile while inflation since 1990 has been quiescent. Thus, as Yellen

(2006) discussed, the Federal Reserve's credibility was much weaker in the

1970s than it is now.

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One way to test whether monetary policy surprises affected inflation expectations

differently in recent years than in the 1970s is to look at how they impacted daily

traded commodity prices.2 Commodities such as gold and silver are widely

regarded as hedges against inflation. The evidence of Gürkaynak, Sack, and

Swanson (2005a) implies that unexpected increases in the federal funds rate

after 1990 lowered longer-term expected inflation and raised short-term real

interest rates. Frankel and Hardouvelis (1985), Hardouvelis and Barnhardt

(1989), Frankel (2008) and others have shown that if monetary policy actions are

expected to increase real interest rates they will lower commodity prices and if

they are expected to lower inflation they will also lower commodity prices. Thus, if

positive federal funds rate innovations are having the effects posited by

Gürkaynak et al., they should unambiguously lower commodity prices.

We find that positive funds rate innovations raised gold and silver prices during

the 1970s and lowered them after 1989. In addition, funds rate hikes over both

sample periods primarily affected short-term interest rates and near-term forward

rates. These results indicate that Romer and Romer’s (2000) information-

revelation explanation applied in the 1970s, when the Fed lacked credibility. They

also imply that funds rate increases in recent years affected short-term real rates.

The findings for commodity prices in recent years are consistent with the

conclusions of Gürkaynak, Sack, and Swanson (2005a). The statistically

insignificant response of far-ahead forward rates is inconsistent with GSS’s

findings, however, and may occur because we lack data to test for a response

over a narrow (thirty-minute) window.

Michel Brzoza-Brzezina For many years money has been a central issue in

monetary policy making. Central banks used to set monetary targets and

academics used to teach monetary policy, as a story about how central bankers

adjust the money supply. Even the name of the main activity of central banks

took its origins from the word .money... Thus, it is no wonder that many economic

papers describing inflationary phenomena still assume that central banks control

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the money supply. Its important role in monetary policy, a lot of research has

been done on testing the long-run relationship between money and inflation. The

probably best known study, is based on the quantity theory of money, and called

P-star1. The model shows that the quantity equation, being a very simplified

description of the relationship between money and prices, can be used for

monetary targeting and inflation forecasting, provided that some additional

assumptions are fulfilled. However, the world is changing, and targeting

monetary aggregates becomes less and less fashionable. The main reason is

probably the growing instability of money demand functions. In reaction,

monetary authorities move from targeting the money supply towards controlling

nominal interest rates at the money market. As a result, in the recent decade, a

huge amount of papers, describing monetary policy rules based on nominal

interest rates, has been written. As it is, however, well known, assuming there is

no money illusion, it is in fact the real and not the nominal interest rate that can

influence spending decisions of enterprises and households.

Monetary authorities can alter real rates (at least in the short run) as long as

prices and inflationary expectations are sticky2. Thus, it is crucial for a central

banker not only to look at the level of nominal interest rates, but also to monitor

the behavior of real rates. Despite the growing importance of interest rate

oriented policies, our knowledge on this topic is still unsatisfactory. The first

approach to describe the relationship between real interest rates and inflation is

often ascribed to K.Wicksell (1898, 1907). However already 100 years earlier,

two British economists, H.Thornton and T.Joplin, described economic processes

resulting from the central bank’s influence on the real rate of interest

(T.M.Humphrey 1993).

Nevertheless, not much has been done in this field since. Recent papers, among

others by M.Woodford (1999, 2000), revived the (now called) Wicksellian idea of

inflationary processes being determined by the gap between the real and

natural3 rates of interest. In a very recent study K.Neiss and E.Nelson (2001) use

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a stochastic general equilibrium model to examine the properties of the interest

rate gap as an inflation indicator. The above mentioned studies are strongly in

favor of using the gap as a measure of the stance of monetary policy that could

be used by central bankers in their day-to-day (or rather month-to-month) policy

setting.

Turkey has had a high level of inflation since the mid 1970s. Governments used

Various fiscal and monetary policy tools to control inflation. In addition to these

tools, governments also attempted to control inflation by regulating the prices of

publicly produced goods and services. Governments either use the publicly

produced goods’ prices as a nominal anchor to decrease inflation (e.g., July 1997

and early 2000 ant inflation programs) as a part of their general anti-inflation

programs, or they try to postpone price increases of publicly produced goods and

services until after elections (as was the case prior to the 1991, 1995 and 1999

elections). However, governments ultimately had to correct the lower prices in

the public sector, mainly to avoid losses in the state owned enterprises. On a

parallel with this, Turkish data suggests that, on the average price increases in

the private and public sectors are approximately the same; however, the

frequency of these price increases in the public sector is lower than those in the

private sector.

The purpose of this article is to show that this infrequency of price changes in the

public sector increases the volatility of the general price level, causing

uncertainty in forecasting general price level, and this in turn increases interest

rates. There is extensive literature regarding the effects of inflation uncertainty on

macroeconomic performance. Cukierman and Wachtel (1979) and Holland

(1993, 1995) examined how inflation uncertainty affects the inflation rate while

Hafer (1986) and Holland (1986) searched for the results of inflation uncertainty

on employment.

Prior to this, Froyen and Waud (1987) and Holland (1988) looked at the inflation

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Uncertainty-output relationship. In Berument (1999), the impact of inflation

uncertainty on interest rates was investigated for the UK by using the Fisher

Hypothesis framework. A similar approach for determining the result of inflation

uncertainty on interest rates in Turkey was pursued in Berument and Güner

(1997) and Berument and Malatyalý (2001). The Fisher hypothesis suggests a

positive relationship between the expected inflation and interest rates. Berument

(1999), Berument and Malatyalý (2001) and Chang (1994) argue that inflation

uncertainty also accelerates interest rates. This study aims to explore the effect

on treasury auction interest rates of the uncertainty stemming from differences in

the public and private sector pricing behavior. In order to model inflation

uncertainty, it is assumed that each component of inflation (public and private

Sector pricing) follows an unbalanced vector autoregressive process and that

their weighted conditional means are equal to the expected inflation. The

conditional variances of the prices of goods produced by the public and private

sectors are estimated via Generalized Autoregressive Conditional

Heteroskedastic (GARCH) processes. The square root of the weighted average

of these conditional variances is used as a measure of inflation uncertainty, and

its effect is investigated within the Fisher hypothesis. Parallel to Berument

(1999), Berument and Malatyalý (2001) and Chang (1994), it is shown that

inflation uncertainty increases the interest rate. Moreover, if the conditional

variance of the public prices was the same as the conditional variance of private

prices, then interest rates would be lowered by 12 percent.

Norges Bank operates a flexible inflation targeting regime, so that weight is given

to both variability in inflation and variability in output and employment. The

outlook for these key variables is influenced by current information about

economic developments. Several simple rules for interest rate setting are found

in the literature. The rules can be used as rough indicators of whether the interest

rate is adapted to the economic situation. Common to these interest rate rules is

that the interest rate is set with a view to keeping inflation around a specific target

over time, at the same time that the interest rate should contribute to stabilizing

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output. One example is the Taylor rule, where the interest rate depends on

inflation’s deviation from the inflation target and the output gap. The Taylor rule

will not capture all factors that are given weight in interest rate setting, and the

parameters in the rule are not based on the experience of Norwegian monetary

policy.

External analysts and observers will over time seek to form a broader picture of

how the central bank reacts to new information than that which emerges from

simple rules. In order to understand the response pattern in monetary policy,

observers may estimate the relationship between the interest rate and

developments in macroeconomic variables. Such a relationship may be thought

of as the “average pattern” in interest rate setting. In actual interest rate setting

emphasis is placed on many indicators that have a bearing on developments in

inflation and output. An estimated equation will not capture all relevant factors. In

particular, it does not capture separate assessments at the various monetary

policy meetings. An estimated equation for interest rate setting will thus be a

considerable simplification and only provide an indication of how the interest rate

on average has reacted to a selection of variables. Moreover, the estimation

results will depend on the data period and the econometric method. We have

estimated a simple equation that captures Norges Bank’s average response

pattern from 1999 to the third quarter of 2004.

The chart below shows the interest rate path that follows from the average

pattern of Norges Bank, as estimated here, and developments in the sight

deposit rate. The estimated equation includes developments in inflation, wage

growth, Norges Bank’s projections for mainland GDP growth and external money

market rates. The equation also implies that the interest rate in the previous

period is of importance. Given the projections in this report, the estimated

equation indicates that the sight deposit rate will increase by ¾ percentage points

in the first quarter of 2005, primarily because inflation is projected to rise. An

interpretation may be that Norges Bank has normally changed the interest rate

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when it was certain that inflation has moved up or down. The chart illustrates,

however, that the interest rate in periods has deviated from the estimated

average pattern. There may often be good reasons for these deviations. The risk

that the krone exchange rate will move in an undesirable direction and bring

inflation further from the target may, for example, imply that the average

response pattern should not be followed. This risk may vary over time, for

example depending on themes in the foreign exchange market. An increase in

interest rates in line with the estimated equation in the period ahead may

increase the probability of an appreciation of the krone and thereby persistently

low inflation.

Marcelle Chauvet and Heather L. R. Tierney In recent years, several factors have

revitalized interest in monetary policy in the U.S. The unusual price stability

achieved in the early 2000s, with a declining inflation rate after the 2001

recession raised concerns regarding the risks of potential deflation. This

possibility instigated public debates and several academic contributions over the

costs and benefits of the preemptive decrease in nominal interest by the Fed to

its lowest level in many decades in order to stimulate Economic growth. More

recently, the resurge in core prices have triggered new debates on monetary

policy conduct.

We propose a nonparametric tool to investigate local dynamic impulse response

functions in a VAR system. In general, nonparametric estimation requires

aggregation of coefficients for the purpose of statistical inference. A large body of

research has been devoted to the examination of the relationship between

inflation and nominal interest rates using vector auto regression models. One of

the findings is that sudden rises in interest rates have been followed by an initial

positive response in inflation, which is known and the price puzzle. Sims (1992)

suggests that this might be caused by an insufficient response by the Federal

Reserve to expectations of higher future inflation. Others find evidence of breaks

in the relationship between inflation and nominal interest rates, especially around

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1980. Finally, there is a large recent literature examining the reduced variability

of inflation and nominal interest rates since the mid 1980s. Some authors

attribute this to changes in the real side of the economy that have transmitted to

inflation, whereas others reckon that the culprit is the more transparent and

effective monetary policy conduct, which has reduced the oscillations in inflation

and interest rates.

Central bankers across North America have adopted targets for short-term

interest rates as their mechanism for implementing market-conditioning monetary

policy. Recent history indicates that there is substantial information conveyed to

markets through changes in targets for the US federal funds rate, as well as the

target band for the overnight financing rate set by the Bank of Canada. Whether

these shifts in the target rate or the target band are in response to market

conditions, or the final result of “watchful waiting” as might have been suggested

by Poole (2001), they are ultimately aimed at sheltering the economy from the

vagaries of the business cycle. Mishkin (1990a, 1990b, 1991) employed Fisher

equations to demonstrate that the slope of the term structure could explain

changes in the inflation rate. During the expansion of the 1990s, many

economists believed that an increase in the slope of the term structure would

restrain inflation: it was possible to maintain a low and stable inflation rate,

thereby generating the associated benefits.

Tkacz (2000) revisited this issue to determine whether there were non-linearity in

the relationship between the slope of the term structure and changes in the

inflation rate. This was justified by appealing to evidence suggesting asymmetric

effects of monetary policy either within the context of a non-linear Phillips curve,

or more generally within a non-linear model of investment and banking behavior.

Using neural network models, Tkacz (2000) demonstrated that a substantial

tightening of the term structure was required to maintain changes in the inflation

rate at low and stable levels for all policy horizons.

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Conclusion

This study argues that public and private sector pricing behavior affects the

inflation risk premium differently and this increases the interest rate. The

empirical evidence for Turkey shows that the difference in pricing behavior cost

11 % on average for the period from January 1989 to November 2000. There are

two distinct channels through which inflation uncertainty may affect interest rates

for safer assets like treasury bills. First, an increase in inflation risk associated

with higher uncertainty is expected to increase interest rates. Second, an

increase in uncertainty, which could also be viewed as a rise in economy-wide

risk, might generate a ‘‘flight to quality’’ effect and increase the demand for safer

assets, which, in turn decreases their interest rates. In this paper, we explored

the validity of these two channels after decomposing inflation uncertainty into two

parts, namely structural and impulse uncertainty, consistent with the

methodology. We find that while structural uncertainty supports the first channel

and increases the interest rates for treasury bills, impulse uncertainty generates

a ‘‘flight to quality effect’’ and decreases their interest rates.

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THEORETICAL FRAME

WORK

Chapter No 3

Theoretical Frame Work

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Introduction

A general explanation of how something works. A theory says what is related to

what and why. A theory is, in part, a collection of related hypotheses. However, a

theory also contains a sense of process and mechanism -- a sense of

understanding of why and how the variables are related the way they are.

Desirable characteristics of a theory include: falsifiability, parsimony, truth,

fertility, generality, surprise, and a sense of process or mechanism.

A theoretical framework is a theoretical perspective. It can be simply a theory, but

it can also be more general -- a basic approach to understanding something.

Typically, a theoretical framework defines the kinds of variables that you will want

to look at.

Theories

Keynesian economic theory proposes that money is transparent to real forces in

the economy, and that visible inflation is the result of pressures in the economy

expressing themselves in prices.

There are three major types of inflation, as part of what Robert J. Gordon calls

the "triangle model":[37]

Demand-pull inflation: inflation caused by increases in aggregate demand

due to increased private and government spending, etc. Demand inflation

is constructive to a faster rate of economic growth since the excess

demand and favorable market conditions will stimulate investment and

expansion.

Cost-push inflation: also called "supply shock inflation," caused by drops

in aggregate supply due to increased prices of inputs, for example. Take

for instance a sudden decrease in the supply of oil, which would increase

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oil prices. Producers for whom oil is a part of their costs could then pass

this on to consumers in the form of increased prices.

Built-in inflation: induced by adaptive expectations, often linked to the

"price/wage spiral" because it involves workers trying to keep their wages

up (gross wages have to increase above the CPI rate to net to CPI after-

tax) with prices and then employers passing higher costs on to consumers

as higher prices as part of a "vicious circle." Built-in inflation reflects

events in the past, and so might be seen as hangover inflation.

A major demand-pull theory centers on the supply of money: inflation may be

caused by an increase in the quantity of money in circulation relative to the ability

of the economy to supply (its potential output). This is most obvious when

governments finance spending in a crisis, such as a civil war, by printing money

excessively, often leading to hyperinflation, a condition where prices can double

in a month or less. Another cause can be a rapid decline in the demand for

money, as happened in Europe during the Black Death.

The money supply is also thought to play a major role in determining moderate

levels of inflation, although there are differences of opinion on how important it is.

For example, Monetarist economists believe that the link is very strong;

Keynesian economics, by contrast, typically emphasize the role of aggregate

demand in the economy rather than the money supply in determining inflation.

That is, for Keynesians the money supply is only one determinant of aggregate

demand. Some economists disagree with the notion that central banks control

the money supply, arguing that central banks have little control because the

money supply adapts to the demand for bank credit issued by commercial banks.

This is the theory of endogenous money.

The inflationary genie is out of its bottle. Or so we are told by orthodox

economists and their dependent politicians in Australia. The new Australian

Prime Minister, Kevin Rudd, claims that “inflation is the ultimate enemy of

working families”, and his Treasurer, Wayne Swan, is certain that “inflation is a

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‘cancer’ eating away at living standards’. At the December 2007 election we were

promised a new approach to economic and social problems, but while the

rhetoric has changed the message is just the same – inflation is evil and has to

be surgically removed from society. Nothing has changed. In the past, we were

told by former Treasurers, Peter Costello in the Liberal Party and Paul Keating in

the Labor Party, that it was essential to “slay the dragon of inflation”. Politicians,

of course, are captives of the technical mysteries of neoclassical economists.

Therefore, while we can change governments and their rhetoric, we can’t change

their policies on inflation.

We are all to aware of the weapon, or surgical instrument, of choice wielded by

the Reserve Bank of Australia (RBA), official dragon slayer or economic surgeon.

It takes the form of persistent increases in interest rates. The latest interest rate

increase (5th February 2008), of 0.25 percentage points, is the eleventh

consecutive increase since May 2002, and the third in six months, taking the

cash rate to 7 percent, currently one of the highest in the Western world. But this

is not expected to be the end to this upward trajectory. Newspaper headlines tell

us that: “RBA warns worse to come; rates to keep climbing until inflation curbed”.

The surgical instrument has become a meat axe, and the RBA is all out of

anesthetic.

The pain of inflation excision is palpable. While there is no evidence after six

years of surgery that the “cancer” has been cured, there is growing evidence that

the patient is suffering badly. “Australian working families”, as we are now

referred to by politicians, are hemorrhaging. Increasingly, Australian families are

finding it impossible to maintain mortgages that are absorbing up to a third of

household budgets. Increasingly, family homes throughout Australia are being

forced onto the market, and their former occupants are seeking rental

accommodation. Increasingly, Australian families are being forced to abandon

their dreams of owning their own homes.

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All this pain is necessary – or so we are told by orthodox experts – in order to

stop us spending, thereby bringing inflation under control. In other words, in order

to kill the “cancer” that is “eating away at our living standards”, we must be forced

to reduce our living standards! At least those of us whose family surplus is

already minimal and vulnerable must do so. Pain, the affluent market economists

and economic consultants tell us, is good for the soul. Provided it is someone

else’s soul.

In my quantitative studies of the modern world, I’ve discovered a strong positive

correlation between long run fluctuations in prices and living standards

(measured by real GDP per capita). This data provided the basis for my

discovery of the “growth-inflation curve” – for the very long run (1370–1994), long

run (1870–1994), and short run (since World WarII) – which demonstrates that

inflation is a stable, non-accelerating function of economic growth – see my Long

run Dynamic and “Strategic demand & the growth-inflation curve” (1997). In the

period from the 1950s to the 1980s in OECD countries, high and sustained rates

of economic growth (up to 5% in terms of real GDP per capita) were achieved

with rates of inflation of between 5 and 7 percent per annum. And we all

prospered. Hence, in viable societies economic growth and inflation fluctuate

together, with prices remaining under strategic control.

What about the well-known examples of hyperinflation, such as in Germany in

the 1920s and in Zimbabwe today? The answer is simple. All societies

experiencing hyper-inflation – or inflation accelerating out of control – were/are

unsuccessful strategic societies. I’m able to say, without fear of contradiction,

that no viable strategic society has ever experienced hyper-inflation. In

successful strategic societies, inflation is a stable and non-accelerating function

of economic growth. Further, I ague that in the long run the implication is clear:

no inflation, no economic growth.

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Inflation targeting is a theory-free concept. Instead it appears to arise from a

widespread human fear of societal change – a fear that unless someone is seen

to be in charges of the levers and pulleys of the economy it will surely run out of

control. Orthodox economists, our self-appointed economic guardians, are

forever fantasizing about their fictional world of balance and harmony called the

concept of equilibrium. Even so-called neoclassical “growth theory” is not about

real world dynamics – about disequilibrium – but about convergence to

equilibrium. This is a concept that economics early imported from classical

thermodynamics, and has tenaciously held onto despite the shift in thought in

thermodynamics to analysis of a “far-from-equilibrium” kind.

There is, however, a general dynamic theory that can explain the real-world

relationship between inflation and living standards that is reflected in the real-

world growth-inflation curve. It is the dynamic-strategy theory that I’ve been

developing a large number of books – beginning with The Dynamic Society

(1996) – and articles– the most recent in Complexity (2008) the journal of the

Santa Fe Institute. Only aspects of this theory can be briefly outlined here. For a

full account see my Long run Dynamics (1998).

In the dynamic-strategy theory a distinction is made between strategic and no

strategic inflation. Strategic inflation is central to the dynamic mechanism of a

growing society, while no strategic inflation is an outcome of poor economic

policy and external impacts like the 1970s OPEC oil crisis. Strategic demand

comprises the effective dynamic demand exercised by decision a maker for a

wide range of physical, intellectual, and institutional resources required in the

strategic pursuit as the society’s dominant dynamic strategy (by which the desire

for survival and prosperity is achieved) unfolds. Strategic inflation, therefore, is

the widespread increase in prices resulting from the pressure of strategic

demand on resources, commodities, and ideas.

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Inflation targeting, where this constrains strategic inflation as it usually does, acts

as a brake on the unfolding dynamic strategy because it distorts the strategic

incentive system. By eliminating strategic inflation in the long run we will

inevitably eliminate economic growth. And the means by which strategic inflation

is eliminated – ever higher interest rates – creates a great deal of unnecessary

and unacceptable economic pain and frustration among young families, which

are the future of society. It is essential to realize that this pain is not caused by

strategic inflation, but rather by conservative and incorrect perceptions of the

costs of inflation. Inflation is only the “enemy of working families” because the

RBA has made it so, owing to its totally inappropriate policy responses. Of

course, inflation does have its social costs, but these are overwhelmed by the

benefits of rapid economic growth that it facilitates; and they are more easily

alleviated than officially induced mortgage crises by taking measures to ensure

that money incomes of disadvantaged social groups keep pace with inflation. If

we wish to maximize long run economic growth and minimize mortgage pain

experienced by Australian families, it is essential to abandon the untenable policy

of inflation targeting. The only “cancer” eating into living standards is the failure of

expert and politician alike to comprehend the dynamics of modern society.

A household in Korea having two choices for housing service, either purchase or

chonsei, would consider the following factors. First, there are inherent differences

between homeowners and chonsei tenants. For example, the homeowners are

free to move whenever they want, while the chonsei tenants do not enjoy such a

freedom. This is a factor that boosts the sales price relative to chonsei price. In

contrast, however, the homeowners should bear the cost to maintain the quality

of houses that chonsei tenants do not have to care about. This is a factor that

discounts the sales price relative to chonsei price. A priori, therefore, it is not

clear whether the sales price should be inherently higher than the chonsei price.

Second, the homeowners should bear the risk of price fluctuations, while the

chonsei tenants are relatively well protected from such risks. As far as investors

are risk averse, this is a factor that discounts the sales price relative to chonsei

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price. Third, the homeowners should pay taxes that chonsei tenants are free

from.7 This is another factor that discounts the sales price relative to chonsei

price. In short, these factors cannot explain why sales prices are substantially

higher than chonsei prices for the basically same housing services.

Therefore, the primary reason for the huge discrepancy of the sales price relative

to chonsei price seems to lie in the expectation on capital gain. That is, the

chonsei tenants are expected to recoup only the deposit in monetary unit upon

maturity, but the owners will be able to enjoy capital gains if the house prices rise

as they did in Korea. As any other prices in monetary economy, the rise of house

price is composed of two parts, the rise in the relative price of house over general

prices and the rise of general prices (or inflation) itself. However, the rise of

relative price can hardly be sustained in the long run, and thus this paper focuses

on the general price inflation as the underlying factor that persistently increases

house price.8 For the same reason, the general price inflation can be seen as a

primary factor for the sales price that remains substantially higher than chonsei

price all the time.

Focusing on the aforementioned factor of expected capital gain, the arbitrage

condition between the sales and chonsei prices. This arbitrage condition can be

recursively solved forward, and the solution will be a complicated function of the

expectations about future chonsei prices and interest rates. Assuming a steady

state with no speculative bubbles (in which the interest rate is fixed at and the

chonsei price increases at a constant inflation rate of), however, produces a

simple and intuitive result: That is, the ratio of the sales to chonsei price is equal

to the ratio of nominal to real interest rate. Of course, this result is based on

many restrictive assumptions. Nevertheless, if the sustained real interest rate is

around 4 percent and chonsei price inflation rate is around 3 percent (a medium-

term target inflation rate of the monetary authority in Korea), this ratio becomes

1.75, which is similar to the ratio of sales to chonsei price at the end of 2003.

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Most of the econometric models developed in Sri Lanka during the last few

decades were either the results of designing development plans by the

government agency responsible for economic planning or the doctoral

dissertations of individual researchers.So far, there have been very few modeling

exercises published by government institutions or policy-making bodies in Sri

Lanka.

Leontief developed a methodology known as the Input-Output Framework, which

is very useful for the detailed quantitative and qualitative analysis of the structure

of an economy, involving its inter-sector linkages and associated multipliers. The

Keynesian framework helps to analyze the Macroeconomic performance of the

economy, through appropriate Macro-Econometric Modeling (Colombage, S.S.,

1992). These two approaches are suitable and hence widely used for quantitative

economic modeling and for generating policy prescriptions. Following Klein

(1965, 1978 & 1986), this paper argues that by combining both these mainstream

methodologies, we would obtain a more suitable theoretical framework for

analyzing a developing economy like Sri Lanka. The Input-Output component of

this model captures the details of the production structures of the various sectors

of the economy working as a highly disaggregated production function and

providing the much-needed supply content to the model. The Econometric Model

serves the purpose of modeling the aggregative expenditure components on the

demand side as also the GDP using these components. With this type of a

model, it would be possible to determine the sector-wise investments required to

free the individual sectors from their respective bottlenecks (from the Input-

Output Sub-model), and also, to arrive at the necessary policy adjustments to

ensure that the required policy stimulus comes forth (from the Macro-

Econometric Sub-model), such that there is a proper co-ordination between the

sets of policies at the two levels.

The article sums up the economic thinking behind the system for targeting

inflation in Hungary and the main economic-policy experiences with this. It

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presents briefly a framework model that the author sees as best reflecting

present central-bank thinking about the functioning of the economy. It

summarizes what normative conclusions can be drawn with the model about

optimal monetary policy, and how these theoretical issues are reflected in the

monetary systems for targeting inflation. The article then turns to international

experience with the effectiveness and success of the regime. Finally, the author

looks back over five years at the conditions that accompanied the targeting of

inflation, at subsequent experiences with the economic-policy issues of that

period, and at operation of the system so far.

A brief historical review is followed by a forecast assessment. Based on

theoretically justified assessment of conditional forecasts, the following

conclusions can be drawn. In most cases, the turning points in inflation were

projected correctly, i. e. the monetary-policy signals were adequate. The

statistical analysis of key forecast errors revealed that projection errors were

unbiased. There were, however, projection errors as well, in wage adjustment,

household consumption growth, and external activity of the corporate sector.

Comprehensive analysis of the structure of the forecasting errors indicates that

ex post forecasts have not utilized all information to an optimal extent. There was

overreaction to the latest-quarter CPI figure, while the effect of nominal wages,

exchange rates and oil prices might be weaker in the short run and stronger in

the long run, compared with the National Bank forecasting methods.

During the past five years – in line with the logic of inflation targeting and

following international best practice – the National Bank of Hungary has been

moving towards a greater degree of transparency. The evolution of the

international best practice can be explained by the fact that in the past decade,

views on the desirability of central-bank transparency have changed to a great

extent. In the past, several central banks explicitly aimed to operate discreetly,

but a general tendency towards increased transparency can be seen since the

beginning of the 1990s. Calls for increased transparency may come from two

directions. On the one hand, a democratic political setup requires public

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accountability of decision-makers at independent central banks, while on the

other, economic thinking in the last decade has robustly inferred that central-

bank transparency can preclude the emergence of inflation bias, increase the

effectiveness of monetary policy and under some conditions, and have a welfare-

enhancing effect. The study examines the validity of the latter assertions with two

simple models often applied in transparency literature. It illustrates that the right

degree of transparency can be subject to debate in theoretical and in practical

terms. Finally it shows how transparency practice has evolved at the National

Bank of Hungary since inflation targeting was introduced.

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DATA

AND

METHODOLOGY

Chapter No 4

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Data and Methodology

Data

A sample period of 25 years has been selected for this study for the period of

1983-2007 with annual frequency. Data on all the variables have been collected

from World Development Indicators. Two variables have been selected for this

study. Inflation rate (INF), and interest rate. The description of variables has been

given below:

Inflation rate (INF)

This variable is measured by the consumer price index, which shows the annual

percentage change in the value of a fixed basket of goods and services.

An interest rate (i)

Is the price a borrower pays for the use of money he does not own, and the

return a lender receives for deferring the use of funds, by lending it to the

borrower. Interest rates are normally expressed as a percentage rate over the

period of one year.

Variable

Inflation Rate ═ XInterest Rate ═ Y

Methodology

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Econometric Model

IRt = βo + β1INt + εt

A Stationary process

A stationary time series has a constant mean, a constant variance

and the covariance is independent of time. Stationarity is essential

for standard econometric theory. Without it we cannot obtain

consistent estimators.

First of all I will check whether all above series are stationary or

not? To test the stationary property of all above series, I will

employ the augmented Dickey- Fuller (ADF) test. In statistics and

econometrics, an augmented Dickey-Fuller test (ADF) is a test for a

unit root in a time series sample. The augmented Dickey-Fuller

(ADF) statistic, used in the test, is a negative number. The more

negative it is, the stronger the rejections of the hypothesis that

there is a unit root at some level of confidence.

Testing Procedure:

The testing procedure for the ADF test is,

∆IRt = β0 + λ2IRt – 1 + β2t + α1∆ IRt – 1 + α2 ∆ IRt – 2 + ….. + αp ∆

IRt – p + εt

Where, ΔIRt = IRt − IRt − 1

∆INt = β0 + λ3INt – 1 + β3t + α1∆ INt – 1 + α2 ∆ INt – 2 + …+ αp ∆

INt – p + εt

Where, ΔINt = INt − INt − 1

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Where βo is a constant, α is the coefficient on a time trend and p the

lag order of the autoregressive process. Imposing the constraints α

= 0 and β0 = 0 corresponds to modeling a random walk and using

the constraint β0 = 0 corresponds to modeling a random walk with a

drift.

By including lags of the order p the ADF formulation allows for

higher-order autoregressive processes. This means that the lag

length p has to be determined when applying the test. One possible

approach is to test down from high orders and examine the t-values

on coefficients.

The unit root test is then carried out under the null hypothesis γ = 0

against the alternative hypothesis of γ < 0. Once a value for the test

statistic

Is computed it can be compared to the relevant critical value for the

Dickey-Fuller Test. If the test statistic is greater (in absolute value)

than the critical value, then the null hypothesis of γ = 0 is rejected

and no unit root is present

Now the hypothesis that will be checked by ADF test

For checking the stationary property in time series of Interest rate

(INt),

Hypothesis 1

                H0:INt is non-stationary, λ1 = 0.

    Versus

 HA: INt is stationary, λ1 < 0.

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Hypothesis 2 for checking the series of inflation rate (IRt)

H0: IRt is non-stationary, λ2 = 0.

Versus

HA: IRt is stationary, λ2 < 0.

Decision rule: 

If     t* > ADF critical value, ==> not reject null hypothesis, i.e., unit

root exists.

If     t* < ADF critical value, ==>   reject null hypothesis, i.e., unit

root does not exist.

Simple Regression Analysis

I will run simple regression analysis between poverty and inflation

and between poverty and unemployment individually. And I will use

scatter diagram for seeing the relationship between poverty and

inflation and poverty and unemployment respectively.

Following is the Simple regression equation for Poverty and

Inflation

PRt = α + β1IRt + εt

Following is the Simple regression equation for Poverty and

unemployment rate

PRt = α + β2URt + εt

Test Of Goodness Of Fit And Correlation:

We will test the overall explanatory power of the

entire regression; this is accomplished by calculating the coefficient

of determination which is usually denoted by R2. The coefficient of

determination (R2) is defined as the proportion of the total variation

or dispersion in the dependent variable (about its mean) that is

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explained by the variation in the independent or explanatory

variable(s) in the regression. In my study the R2 will measure how

much of the variations in the inflation in Pakistan at long run is

explained by the variation in unemployment respectively, in

Pakistan at long run.

Where

Explained variation in inflation (IRt)

R2 =

Total variation in inflation (IRt)

∑ (IRt - IR) 2

R2 =

∑ (IRt – IR) 2

In the simple regression analysis the square root of the coefficient

of determination (R2) is the absolute value of the coefficient of

correlation, which is denoted by r. That is,

r = √ R2

This is simply a measure of degree of association or co variation

that exists between variables inflation and unemployment.

Adjusted R 2 :

Adjusted R2 is a modification of R2 that adjusts for the number of

explanatory terms in a model. Unlike R2, the adjusted R2 increases

only if the new term improves the model more than would be

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expected by chance. The adjusted R2 can be negative, and will

always be less than or equal to R2.

It is denoted by R2.

R2 = 1 – (1 – R2) (n – 1\ n – k)

Where n is the no. of observations or sample data points and k is

the no. of parameters or coefficients estimated.

Co-integration regressions for Inflation and Interest rate can

be expressed as follows:

IRit = βo + βi INit + εt

Where , і= 1,2,3,4,5,6……

Hypothesis

H0: There is no significant relationship present between inflation

and interest rate in Pakistan in long run, β = 0.

Versus

HA: There is a significant relationship present between inflation and

interest rate in Pakistan in long run, β ≠ 0.

Now I will use F – Test for testing the above hypothesis.

Analysis of Variance

The overall explanatory power of the entire regression can be

tested with the analysis of variance. This uses the value of the F

statistics, or F ratio. Specifically, the F statistic is used to test the

hypothesis that the variation in the independent variables explains

a significant proportion of the variation in the dependent variable.

Thus, I will use the F statistic to test the null hypothesis that all the

regression coefficients are equal to zero against the alternative

hypothesis that they are not all equal to zero.

The value of the F statistics is given by

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Explained variation ∕ (k – 1)

F =

Total variation ∕ (n – k)

Where, n is the number of observation and k is the number of

regression coefficients. It is because the F statistics is the ratio of

two variances that this test is often referred to as the analysis of

variance. I will calculate the F statistics in terms of the coefficient of

determination as follows:

R2 ∕ (k – 1)

F =

(1 – R2) ∕ (n – k)

Here R2 represent the coefficient of determination between poverty

rate and inflation rate in Pakistan in long run.

Then we will compare the calculated value of the F statistics with a

critical value from the table of the F distribution. If the calculated

value of the F statistics exceeds the critical value of the F

distribution I will reject the null hypothesis that there is no significant

relationship between inflation and unemployment rate in Pakistan in

long run, and I will accept the alternative hypothesis at 5 % level of

significance that not all the coefficients equal to zero, and vice

versa.

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DATA ANALYSIS

Chapter No 5

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Data Analysis

In this chapter we check all the data that is stationary and non-stationary. We use

the unit root test of the “Augmented Dickey Fuller” test on the level but all the data

that is non-stationary we check all the data on the 1st difference where all the

variables become stationary. Second test impose on the variables of “Phillips-perron

test” where all the variables are non-stationary on the level. But all the variables

become stationary on the 1st difference, which shows that all the variables are

stationary on the 1st difference.

Then we will check the co-integration between these variables inflation, and interest

rate. If the trace statistics values less the critical values and level of significance

which is 0.05. This co-integration shows that there is no long run relationship

between inflation and interest rate. If there is no long run relationship between

variables then we use the Granger Causality Test in which if there is relationship

between variables then we reject H0 the and accept the H1.

Table

Years Inflation Interest

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Rate Rate

1983 7.28 21.099

1984 7.3 23.686

1985 5.31 25.319

1986 3.51 27.080

1987 4.7 30.128

1988 8.8 32.679

1989 7.9 40.783

1990 9.1 48.125

1991 12.7 54.111

1992 10.6 61.899

1993 9.8 80.520

1994 11.3 99.943

1995 13 100.775

1996 10.8 125.802

1997 11.8 146.324

1998 7.8 183.167

1999 5.7 199.871

2000 3.6 236.585

2001 4.4 312.721

2002 3.5 318.749

2003 3.1 257.434

2004 4.6 317.723

2005 9.3 274.717

2006 7.9 304.794

2007 7.8 295.842

A Stationary analysis of data:

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5.1 Augmented Fuller Test

The results are following:

5.1a:

These are results of AFD test At first Difference

Augmented Dickey-Fuller Test (ADF):

Table 1: Augmented Dickey-Fuller unit root test dependent variable INFLATION:

Null Hypothesis: D(INFLATION) has a unit rootExogenous: ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=5)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -4.494048  0.0019Test critical values: 1% level -3.752946

5% level -2.99806410% level -2.638752

Interpretation:

In the above series naming as the unit root test of Inflation,

the dickey fuller and Mackinnon test shows that the t-statistic value is greater

than critical value therefore we can say that the data is stationery at first

difference. My probability value is less than at 10% level of significance so that

my dependent variable is stationary that’s why we reject null hypothesis and

alternative hypothesis is accepted. My dependent variable inflation is effected the

independent variable interest rate.

Table 2: Augmented Dickey-Fuller unit root test independent variable INTEREST RATE:

Null Hypothesis: D(INTERST_RATE) has a unit rootExogenous: ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=5)

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t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -6.118207  0.0000Test critical values: 1% level -3.752946

5% level -2.99806410% level -2.638752

*MacKinnon (1996) one-sided p-values.

Interpretation:

Here we have a series of unit root test for unemployment

including the results of Dickey fuller (6.118207 > 0.10) which shows that the

calculated value is greater than the critical value therefore we will reject the null

hypothesis H0. My probability value is less than at 10% level of significance so

that my independent variable is stationary that’s why we reject null hypothesis.

My independent variable interest rate is effected inflation.

Phillips-Perron Test

Table 3: Phillips-Perron unit root test dependent variable inflation:

Null Hypothesis: D(INFLATION) has a unit rootExogenous: ConstantBandwidth: 2 (Newey-West using Bartlett kernel)

Adj. t-Stat   Prob.*

Phillips-Perron test statistic -4.486138  0.0019Test critical values: 1% level -3.752946

5% level -2.99806410% level -2.638752

*MacKinnon (1996) one-sided p-values.

Residual variance (no correction)  4.754523HAC corrected variance (Bartlett kernel)  4.425723

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Phillips-Perron Test EquationDependent Variable: D(INFLATION,2)Method: Least SquaresDate: 01/28/09 Time: 11:56Sample (adjusted): 3 25Included observations: 25 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

D(INFLATION(-1)) -0.980566 0.218192 -4.494048 0.0002C 0.021215 0.475858 0.044583 0.9649

R-squared 0.490248     Mean dependent var -0.005217Adjusted R-squared 0.465974     S.D. dependent var 3.122672S.E. of regression 2.281958     Akaike info criterion 4.570886Sum squared resid 109.3540     Schwarz criterion 4.669625Log likelihood -50.56519     F-statistic 20.19647Durbin-Watson stat 1.958845     Prob(F-statistic) 0.000199

Interpretation

My probability Value of Phillips-perron unit root test is less than 0.1

so that my dependent variable inflation is stationary. In the above table my

probability value of Phillips- perron test is less than the 10% level of significance

so that I reject null hypothesis.

Table 4: Phillips-Perron unit root test independent variable Interest Rate:

Null Hypothesis: D(INTERST_RATE) has a unit rootExogenous: ConstantBandwidth: 1 (Newey-West using Bartlett kernel)

Adj. t-Stat   Prob.*

Phillips-Perron test statistic -6.110056  0.0000Test critical values: 1% level -3.752946

5% level -2.99806410% level -2.638752

*MacKinnon (1996) one-sided p-values.

Residual variance (no correction)  711.0987

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HAC corrected variance (Bartlett kernel)  719.0674

Phillips-Perron Test EquationDependent Variable: D(INTERST_RATE,2)Method: Least SquaresDate: 01/28/09 Time: 12:00Sample (adjusted): 3 25Included observations: 25 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

D(INTERST_RATE(-1)) -1.291217 0.211045 -6.118207 0.0000C 15.42514 6.374830 2.419695 0.0247

R-squared 0.640611     Mean dependent var -0.501739Adjusted R-squared 0.623497     S.D. dependent var 45.48152S.E. of regression 27.90739     Akaike info criterion 9.578601Sum squared resid 16355.27     Schwarz criterion 9.677340Log likelihood -108.1539     F-statistic 37.43246Durbin-Watson stat 1.950625     Prob(F-statistic) 0.000005

Interpretation

My probability Value of Phillips-perron unit root test is less than

0.1 so that my independent variable interest rate is stationary. In the above table

my probability value of Phillips- perron test is less than the 10% level of

significance so that I reject null hypothesis.

Co integration:

Table 5: Co integration of the dependent variable Inflation and independent variable Interest Rate:

Date: 01/28/09 Time: 11:30Sample (adjusted): 3 25Included observations: 25 after adjustmentsTrend assumption: Linear deterministic trendSeries: INFLATION INTERST_RATE Lags interval (in first differences): 1 to 1

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Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None  0.194479  4.987092  15.49471  0.8102At most 1  0.000564  0.012975  3.841466  0.9091

 Trace test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None  0.194479  4.974117  14.26460  0.7451At most 1  0.000564  0.012975  3.841466  0.9091

 Max-eigenvalue test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

 Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I): 

INFLATIONINTERST_RAT

E-0.375939 -0.004323 0.000918 -0.009501

 Unrestricted Adjustment Coefficients (alpha): 

D(INFLATION)  0.928340  0.012646D(INTERST_R

ATE) -5.304447  0.551557

1 Cointegrating Equation(s):  Log likelihood -155.0764

Normalized cointegrating coefficients (standard error in parentheses)

INFLATIONINTERST_RAT

E 1.000000  0.011499

 (0.01181)

Adjustment coefficients (standard error in parentheses)D(INFLATION) -0.348999

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 (0.16930)D(INTERST_R

ATE)  1.994149 (2.20891)

Interpretation

The above calculation shows that there is no co integration between the

variables of Inflation and Interest Rate. Because all the traces statistics values

less than that of the critical values and the level of significance which is 0.05.

This co integration shows that there is no long run relationship between inflation

and interest rate. The results shows the short run relationship between inflation

and interest rate. So because of short run relationship we find the results with the

help of Granger Causality Tests.

Granger Causality Test

Table 6: Granger Causality Test of the dependent variable Inflation and independent variable Interest Rate:

Pairwise Granger Causality TestsDate: 01/20/09 Time: 11:37Sample: 1 25Lags: 2

  Null Hypothesis: Obs F-Statistic Probability

  INTERST_RATE does not Granger Cause INFLATION 23  0.49363  0.61843  INFLATION does not Granger Cause INTERST_RATE  0.75609  0.48384

Interpretation:

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In the above table my F-Statistics value is 0.49363 and my probability

value is 0.61843. My probability value is less than 0.1 so my statement is wrong

because Interest rate is Granger Cause Inflation and Interest rate does effect on

Inflation at 10% level of significance. On the other hand my probability value is

less than 0.1 so my statement is wrong because Inflation is Granger Cause

Interest rate and Inflation does effect on Interest rate at 10% level of significance.

CONCLUSION

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Chapter No 6

CONCLUSION

Summary of Findings

This thesis presents an empirical investigation into the strength and validity of the

hypothesis using developed and developing countries data during the inflation-

targeting monetary policy regime. A significant amount of research has been

conducted in developed countries to prove and establish this hypothesis, yielding

conflicting results. However little has been done to explore this relationship in

developing countries which, in contrast to the developed countries studied, tend

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to have higher and more volatile levels of inflation. The sparse empirical research

conducted using data from developing countries is also inconsistent. Three

prominent interpretations have emerged in the literature in an attempt to

reconcile these inconsistencies. These effects suggests that interest rates should

adjust by less than one-for-one to expected inflation, whereas the risk aversion

effect proposes a greater than one-for-one movement. Finally, the inverted

Fisher effect postulates that it is interest rates that remain constant over time and

it is the real interest rate that moves inversely to inflation.

Despite these alternative interpretations, the hypothesis, in its strictest form, still

attracts a significant amount of empirical research. This is largely attributed to the

Fisher equation’s ability to analyze whether the real rate of interest has remained

constant over a given period of time. Due to the infancy of most inflation-targeting

monetary regimes, very few previous studies have empirically investigated the

Fisher effect using data from an inflation-targeting monetary policy framework.

This thesis is therefore an attempt to fill this gap and uncovers a variety of

interesting relationships specific to an inflation-targeting regime.

The thesis adopted an inflation-targeting monetary policy framework. One of the

main reasons that the previous “eclectic” monetary policy approach to a

monetary framework that targeted inflation directly was to achieve greater

credibility of monetary policy in the “eyes of the public”. The inflation-targeting

framework has enjoyed widespread credibility and has made promising steps

towards successfully containing inflation.

Against this backdrop the crucial question addressed is the extent to which

monetary authorities have been able to affect both the long- and short-term real

rates of interest through policy action in an environment where inflation

expectations are becoming increasingly firmly anchored. This equation is

therefore used as a behavioral equation to analyze the relationship between

expected inflation and both short- and long-term nominal interest rates.

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The analysis distinguishes between a short-run effect and a long-run effect. The

short-run effect is unlikely to hold empirically, given the effects of the

transmission mechanism. This is confirmed by the empirical results, which show

no statistically significant long-run relationship between expected inflation and

short-term nominal interest rates. This is consistent with the transmission

mechanism’s influence on the short-term real rate of interest.

The long-run effect analysis is able to identify whether monetary policy has been

able to reduce the long-run real rate of interest. This information is important to

monetary authorities because a reduction in the long-term real rate of interest

enables the economy to achieve long-run increases in economic output. Utilizing

Johansen’s maximum likelihood cointegration framework, the results suggest a

weak long-run cointegrating relationship between expected inflation and long-

term nominal interest rates. This would indicate that the long-term real rate of

interest has not remained constant since the inception of inflation-targeting and

that monetary policy has actually influenced the long-term real rate of interest.

This also implies that changes in inflation expectations do move in the same

direction as the long-term nominal interest rate; however the movement is less

than unity.

CONCLUSIONS AND POLICY IMPLICATION

Short- Run Effect

Economic agents will only place their long-run faith in monetary policy if the

short-run dynamics of the monetary framework work efficiently and in a timely

manner. The analysis of short-term interest rates presents an encouraging

testament to the effectiveness of the SARB’s inflation-targeting framework, and

more especially the monetary transmission mechanism in developing countries. It

also implies that short-term interest rates are not driven by inflation expectation

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and are therefore a good indication of the stance of monetary policy in developed

or developing countries.

Long- Run Effect

The thesis concludes that long-term interest rates are co integrated with

expected inflation. The long-run adjustment is, however, less than unity. This can

primarily be attributed to the credibility of the inflation-targeting framework and

the success it has achieved in locking inflation expectations into the target range,

thereby ensuring that the expected inflation premium required by economic

agents to compensate them for the inflationary erosion of nominal money

balances remain relatively low and stable. Economic agents do not require

nominal interest rate movements to fully adjust to changes in expected inflation

because inflation is not perceived to remain at abnormally high or low levels. If,

for example, an acceleration of the currency causes inflation expectations to

increase beyond the mid-point of the target range, economic agents would feel

confident that this impact would only be transitory due to the effectiveness of

monetary policy.

Applying a similar argument, the weak long-run relationship may be indicative of

the increasingly influential role that the long-term real rate of interest is having on

nominal interest rates. This scenario is even more compelling given the length

and success of inflation-targeting regime. The results may therefore advocate

that the SARB is beginning to evolve into the second evolutionary stage of an

inflation-targeting regime. This stage is characterized by nominal interest rates

that primarily reflect movements in the long-term real rate of interest rather than

the relatively constant expected rate of inflation.

LIMITATIONS AND FUTURE RESEARCH

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The study suffered from two limitations. Firstly, although the results are generally

robust, the Johansen procedure was found to be sensitive to lag length chosen.

Various authors including Gonzalo (1994:220), Hawtrey (1997:344) and Yuhn

(1996:42) have all also reported similar sensitivity. Secondly, a major challenge

in all empirical investigations of the Fisher effect is that inflation expectations are

not directly observable. Although there are various approaches available within

the developed or developing countries context, not all are available or have an

appropriate length to be used in this study.

Since the adoption of inflation-targeting there has been an increased importance

placed on developing accurate expected inflation forecasts. This has spurred an

increase in the number of inflation expectations time series available for use in

future empirical econometric studies, thereby presenting exciting research

opportunities, not only for future studies but also for explorative analysis into the

relationship between expected inflation and interest rate. Due to the crucial role

played by expectations of future inflation and interest rate in all policy decisions,

further insight into the dynamics of inflationary expectations will provide valuable

information for monetary authorities.

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REFERENCES60

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