Tarun Das ADB Nepal Inception Report-Update-Annex

8/9/2019 Tarun Das ADB Nepal Inception Report-Update-Annex

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Nepal Macroeconomic Model- Inception Report

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INCEPTION REPORT:NEPAL MACROECONOMIC MODEL (NMEM) AND

DYNAMIC STOCHASTIC GENERALEQUILIBRIUM (DSGE) MODEL-MODEL STRUCTURES, DATA BASE, AND SOFTWARE-HARDWARE REQUIREMENTS

PART-II ANNEX

DR. TARUN DAS1,2

(FORMERLY, ECONOMIC ADVISER, MINISTRYOF FINANCE, INDIA)

INASSOCIATIONWITH

PROFESSORDURGA LAL SHRESTHA3

DR. VIKASH RAJ SATYAL4

MR. ROJAN BAJRACHARYA5

8 OCTOBER2009Executing Agency:

The Nepal Rastra Bank,Baluwater, Kathmandu.

For any clarifications, write to: [email protected]

1 Macroeconomic Modeling Specialist/ Team Leader (International).

2 Authors would like to express their sincere thanks to Mr. Shahid Parwez, ADB Project/ ProgramImplementation Officer and Dr. Nephil Matangi Maskay, Director (Research), Nepal Rastra Bank foroverall guidance, valuable discussions and comments on an earlier draft. However, the Report expressespersonal views of the authors and does not necessarily imply the views of the ADB Nepal ResidentMission, NRB, MOF and the CBS, Nepal.

3 Macroeconomic Modeling Specialist (National)4 Econometrician (National)5 Information Technology Specialist (National)

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mailto:[email protected]://flagspot.net/images/n/np)2006.gifmailto:[email protected]


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NEPAL GEOGRAPHIC MAP

Location: 26 22' N to 30 27' North, 80 4'E to 88 12' East

Political and Administrative StructureNepal ( ), officially known as the Federal Democratic Republic of Nepal, is alandlocked country in South Asia and the world's youngest republic. It is divided into fivedevelopment regions (Eastern, Central, Western, Mid-Western and Far-Western), and threeecological regions (Mountain, Hill and Terai). For administrative purpose, it is divided into 75districts (Zilla) and each district is divided into municipalities and Village DevelopmentCommittees that are further divided into wards.

Basic Facts about Nepal:Fiscal year: 16th July to 15th July of the following yearItems (Year) Units Value Rank in the World

from topin descending order

Area (2009) Sq. km. 147,181 98 out of 248 countries

Population (2008) Million 29.5 41 out of 241 countries

GDP PPP (2004) Billion US$ 29.3 103 out of 229 countries

GDP Nominal (2006) Billion US$ 8.1 122 out of 229 countries

GDP PPP per capita (2004) US$ 1,352 141 out of 163 countries

GDP per capita (2006) US$ 291 195 out of 207 countries

Poverty Ratio (% of people belowOne-US$) (2000)

Percent 37.7 14 out of 59 countries

External debt (2006) Billion US $ 3.1 116 out of 196

Debt service ratio (2006) Percentage 4.9 105 out of 128

Source: http://www.nationmaster.com

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http://en.wikipedia.org/wiki/Landlockedhttp://en.wikipedia.org/wiki/South_Asiahttp://en.wikipedia.org/wiki/Republichttp://www.nationmaster.com/http://en.wikipedia.org/wiki/Landlockedhttp://en.wikipedia.org/wiki/South_Asiahttp://en.wikipedia.org/wiki/Republichttp://www.nationmaster.com/


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PART-II: ANNEXTable of Contents

ANNEX-1: COUNTRY ECONOMIC INDICATORS 4

ANNEX-2: COUNTRY POVERTYAND SOCIAL INDICATORS 5

ANNEX-3: SCOPE, CHARACTERISTICSAND TYPESOF MACROECONOMICMODELS

3.1 SCOPEOF MODELINGAND FORECASTING3.2 FACTORS AFFECTING MODELING3.3 TYPESOF MODELS3.4 DESIRABLE CHARACTERISTICSOFAN IDEAL MODEL3.5 VARIOUS OPERATIONAL MACROECONOMIC MODELS3.6 MACROECONOMIC MODELIN LEONTIEF I-O FRAMEWORK3.7 SOCIAL ACCOUNTING MATRIX & GENERAL EQUILIBRIUM

3.8 LINEAR PROGRAMMINGAND GRAVITY MODEL3.9 ECONOMETRICMODELS3.10 STRUCTURAL MACROECONOMETRIC MODELS

3.11 VECTORAUTOREGRESSIVE MODEL (VAR)3.12 GENERAL EQUILIBRIUM MODEL3.13 DYNAMIC STOCHASTIC GENERALEQUILIBRIUM MODEL

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66778910

101113151617

ANNEX-4: DATABASEREQUIREDFORTESTANDCALIBRATIONSOFTHENMEM AND DSGE-TYPE MODELSFORNEPAL

21-25

ANNEX-5: TERMSOFREFERENCEOFTHECONSULTANTS 26-27

ANNEX-6: BRIEFCVOFTHECONSULTANTS6.1 INTERNATIONAL MACROECONOMIC MODELING SPECIALIST6.2 NATIONAL MACROECONOMIC MODELING SPECIALIST6.3 NATIONAL ECONOMETRICIAN6.4 NATIONAL IT SPECIALIST

28-3128293031

ANNEX-7: WORKPLAN MATRIX OF CONSULTANTS7.1 INTERNATIONAL MACROECONOMICMODELINGSPECIALIST7.2 NATIONAL MACROECONOMICMODELINGSPECIALIST7.3 WORKPLANMATRIXOFTHEECONOMETRICIAN7.4 WORKPLANMATRIXOFTHEITSPECIALIST

32-3632343536

ANNEX-8A: BROAD FEATURESOF E-VIEWS 6 SOFTWARE 37-56ANNEX-8B: EVIEWS COMMERCIALAND GOVERNMENT VOLUMELICENSING CONDITIONSAND PRICES

57-62

ANNEX-9: LISTOF OFFICERS CONSULTED 63

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Annex-3: Scope, Characteristics and Types of Macroeconomic Models

3.1 Scope of Modeling and Forecasting

It is well known that a model, (like a model for a house, automobile, aircraft etc.), is an

approximation of reality and contains basic and selected features, and not the exact or allfeatures of the real world. A macroeconomic model consists of a set of mathematical,statistical and econometric equations indicating best possible underlying inter-relationships among major macroeconomic variables such as GDP, consumption, savings,investment, external trade and capital flows, government revenues and expenditure,money supply, inflation, interest and exchange rates etc. Macroeconomic modeling andprojections form the basic foundations of the government budgeting, national planningand monetary programming. These are essential to formulate appropriate public policiesand decisions on budgeting, investment, employment, physical, financial and monetaryplanning for achieving sustained growth with poverty reduction and achievement ofMDGs.

Basic building blocks of any economic modeling are the empirical trends of majormacroeconomic variables and estimation of best fitted interrelations among thesevariables. However, they are subject to identification and specification problems and arebased on a number of presumptions. First, there is the assumption that the behavior ofeconomic variables is the joint result of a number of economic variables influencing eachother. Second, although the model is a simplification of complexities of reality, itcaptures the crucial features of the economic sectors or systems being studied by us.Third is the hope that the underlying relations will continue to hold good in future unlessappropriate policies are taken or there are unforeseen internal or external shocks tochange the system.

3.2 Factors affecting modeling

Before building an economic model, we need to take decisions on a number of factorssuch as the basic purpose, dimensions and data base of the model, such as the following:

(a) What are the basic objectives of modeling and forecasts? Who are going to usethese and for what purpose?

(b) Planning and forecasting horizon- it could be short term (for one year), ormedium term (for five years) or for the long term (usually ten to fifteen years).

(c) Sectoral disaggregation- what are the broad sectors of the real economy? What

are the broad categories of government taxes and expenditure or the broad itemsof the balance of payments we would like to forecast? Actual disagregationdepends on the availability of data and the resources (in terms of skilledmanpower, time, money and computer capabilities in terms of hardware andsoftware) available at the disposal of the modeling team.

(d) Regional disagregation- For consistency, macroeconomic modeling may besupplemented by regional models for selected variables such as production,

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investment and employment for regional planning and budgeting. This againcalls for suitable regional demarcation such as Meso (Middle) level (states andprovinces), Micro (unit) level (villages, towns), sectoral (rural, urban) etc.

(e) Data base Generally we need past data on the selected variables for at leasttwenty years.

(f) Environment- For the short term we may assume no change in technology andinstitutional set up. But for the medium and long term we need to have clear perception and vision about the changes in technology, productivity andimproved governance and institutional set-up.

3.3 Types of Models

There are various types of macroeconomic Models as indicated bellow:

a) Static (at a particular time period, say for a year) and dynamic (takes care of

change of time and business environment). In dynamic model, time is specificallyintroduced as an important variable influencing basic macroeconomic variablesand their interrelations.

b) Consistent (shows consistency among the systems equations), behavioral(depends on the behavior of economic agents) and optimizing(maximizing gainssuch as revenue, social welfare, employment etc; or minimizing losses such asgovernment deficit, BOP deficit, inflation, poverty, inequality etc.).

c) Partial equilibrium (deals with specific sectors) or general equilibrium(considers equilibrium in the whole system).

d) Sectoralmodel (deals with a sector such as energy, transport) or the economy-

wide model (deals with all sectors).

e) Spatialmodel (over space) or regionalmodel (over regions) such as a transportmodel

f) Inter-temporal(over time) multi-year dynamic models are called inter-temporalmodel.

g) Intergenerationalmodels Models dealing with more than one generation usuallyspanning over sufficiently long period such as 25 to 40 years.

h) Closed model (which does not consider external trade) and open model (whichdeals with both internal and external sectors and their Interlinkages)

3.4 Desirable characteristics of an Ideal Model:An ideal model needs to have some desirable characteristics such as the following:

(a) Internal consistency- There should not be any inherent contradictions in theunderlying equations of a model

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(b) Comprehensiveness with two-way feed backs- As far as possible, a modelshould capture the feedbacks to the model.(c) A model should be dynamic to take care of changes over time.(d) A model should lead to unique and stable solutions.(e) A model should be easily specified, identified, estimated, tested and

calibrated with the help of available data, computer capacities, simple algorithms, andsimple statistical and econometric techniques.(f) Model should be continually tested, calibrated, monitored, reviewed, updated,simulated and improved to make it more realistic over time.

3.5 Various Operational Macroeconomic Models

Depending on the purpose and availability of data and resources, alternativemacroeconomic models as indicated below can be developed:

(i) Consistency Model in the Leontief Input-Output (I-O) Framework

(ii) Social Accounting Matrix (SAM) and General Equilibrium Model(iii) Linear Programming (LP) Model and Gravity Model(iv) Structural Macroeconometric Model(v) Vector Autoregressive (VAR) Model(vi) Dynamic Stochastic General Equilibrium (DSGE) Model

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3.6 Macro-economic Model in Leontief Input-Output Framework

The basic macroeconomic model in Leontief Input-Output Framework consisting of nsectors stands as the following:

Di= Xi= aij Xj +Fi i=1,2,3 .nFi = Ci + Gi + Ii + STi + EXPi IMPi i=1, 2,3 .nWhereDi = Gross Demand for i-th goodXi = Gross output of i-th goodaij = Leontief input-output coefficient = Amount of i-th good required forproduction of one unit of the j-th good.Fi = Final demand for i-th goodCi = Private consumption for i-th goodGi = Public consumption for i-th goodIi = Investment for i-th goodSTi = Stocks/ inventories for i-th good

EXPi = Exports of i-th goodIMPi = Imports of i-th good,

The Model uses the concept of End-Use where total demand is decomposed into

intermediate demand ( aij Xj) and final demand (Fi). Intermediate demand is used upin the process of production and so does not enter into GDP, whereas final demandcreates value addition and is considered for estimation of GDP. Different methods areused to estimate separate components. Generally, private consumption is estimated byfitting Engel curves on the basis of consumer expenditure surveys. Various forms ofEngel curves (such as linear, log-linear, semi logarithmic, exponential, log-inverse, log-log-inverse etc) can be tried and the best fitted form can be used for projection.

Linear C = + Y

Log linear Log C = + Log Y

Semi log Log C = + Y

Log Inverse Log C = + / Y

Log Log Inverse Log C = + 1 Log Y+ 2 / Y

Public consumption is estimated by the social welfare programs. Investments is estimated by a distributed lag model by linking investment and output with successivelydiminishing .effects over time. Stocks and inventories are estimated with fixedcoefficients on the basis of past trends. Exports and imports are estimated by appropriate

econometric equations or by gravity models on international trade.

Above discussions indicate that the needs for data and resources to develop a Leontiefmodel are huge. Even India, which has prepared detailed input-output tables and hadbeen using the Leontief input-output model for planning for more than four decades, isnow discarding the model because it has become non-operational in the context ofongoing globalization and economic reforms. The application of the Leontief Input-Output model requires preparation of a reliable technology matrix and its updating from

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time to time. Given the extent of liberalization, privatization and globalization in almostall countries of the world leading to liberal technology transfer, the technology matrix ischanging at a fast speed. Consequently, the Leontief input-output model has becomeoutdated and is hardly used by any country for planning.

3.7 Social Accounting Matrix and General Equilibrium ModelGeneral equilibrium model can also be built up in the social accounting framework. Giventhe increasing importance of environmental sustainability over time, there is a tendencyto develop social accounting matrices by the developed countries and some of thedeveloping nations like India and Bangladesh. But, such attempts are more effective onlywhen the basic National Accounts Statistics have been fully developed. For a country likeNepal, which is presently engaged in preparing quarterly estimates of national accountsstatistics, it may take a few more years to construct a feasible social accounting matrix.

3.8 Linear Programming (LP) and Gravity Model

The standard Linear Programming (LP) Model aims at either maximizing gains (such asnational output or social welfare or employment) or minimizing losses (such as costs of production, poverty, inequality, energy consumption, use of foreign exchange etc.)subject to resource constraints and supply and demand balance equations. Such modelshold good only under perfect competition. But, the real world and the existing marketsare neither perfect nor competitive. Therefore, the use of LP Model is not advisable forforecasting and planning (except for certain controlled sectors such as public transportand public utilities).

While Linear Programming model is an optimizing model, gravity model is a behavioristmodel, mainly used for forecasting inter-regional transport flows and international tradeflows. For the transport model, transport flow (goods or passengers) from region r toregion s is estimated by the following relation:

Trs = Ar Bs SrDs exp (- Crs ) for r, s=1,2,3 .n

Where Trs = Transport flow from region r to sCrs = Unit transport cost from region r to sSr = Supply of region rDs = Demand for region s

= entropy parameter

A

r ,

B

s

= Balancing parameters such thatSr = Trs summed over s for r=1,2,3 .n

Ds = Trs summed over r for s=1,2,3 .n

For transport model in general, LP Model holds good for homogeneous goods whilegravity model holds good for heterogeneous goods and when there are aggregations overtime, over commodities and over large areas.

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3.9 Econometric Models

Advantages of Econometric Model

An Econometric Model is the combination of mathematical and statistical techniques to

estimate best fitted relations among the macroeconomic variables. It helps:

(a) to formalize the system,(b) to establish inter-relations among major policy variables that can be specified,

calibrated, tested, monitored, updated, simulated and predicted with certaindegree of confidence, and to identify trade-off among alternative policy options.

Steps in Econometric Modeling

(a) Specify objectives and purpose of the model.(b) Have a sound theoretical basis for the underlying relations.

(c) Identify potential variables(d) Specify the set of equations(e) Identify the equations so that they are neither under nor over identified.(f) Use suitable calibration techniques for the estimation of parameters(g) Test the Model with the help of various goodness of fit statistics such as R ,

, RMSE etc.(h) Simulations/ sensitivity analysis for alternatives scenario and policy options(i) Monitoring, reviewing and updating(j) Prediction and Projection.

Types of Data

(a) Time series (over time- also called inter-temporal)(b) Cross section (across consumers and regions etc. at a particular time)(c) Pooled- Pooling of sectors or regions (urban and rural, and all states etc.)(d) Panel - Combination of time series and cross section data

Types of variables

In order to describe various types of variables let us consider the following simpleeconometric equation determining the aggregate consumption C:

Ct =a

+b

Yt +c

Wt +d

Ct-1 +e

Dt + f T + Ut

Ct = Consumption in time t

Yt = Gross National Income in time t

Wt = Wealth in time t

Ct-1 = Consumption in time t-1

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T, t = Time with value of 1 for the initial year and 2, 3, 4, n for subsequent years

Dt = Dummy variable having a value of 1 for the years after 1991, and 0 otherwise

Ut = random error term in time t (in classical least squares Ut is assumed to be normallydistributed with zero mean and constant variance in repeated samples)

Variables used in this equation can be classified as follows:

(a) Endogenous variables (which are determined within the model) Ct, Yt(b) Exogenous/ predetermined variable (which are not determined in the model,

rather given from outside) Wt.(c) Lagged variable/ pre-determined variable Ct-1(d) Parameters a , b, c, d, e, f(e) Instrumental variable Wt. Here Wt is called an instrumental variable

implying that it could have been endogenized in the model, but has beentaken as predetermined to influence current consumption.

(f) Dummy variable (also called binary, categorical, qualitative, dichotomous

variable, which takes different values on different occasions) Dt(g) Omitted variables (which are not considered in the model)(h) Catch all variable (which can catch the influence of all omitted variables)

here time T is taken as a catch all variable(i) Ut is the random error term which measures the residuals of fitted equation

over the actual values of the variable.Types of Equations

(a) Technical relation such as Cobb-Douglas production function which shows thetechnical relation between output (Y), capital (K) and labor (L)

a. Y = A L K,

where: Y= output; L = laborinput ; K= capital input and A, and are constantsdetermined by technology and returns to scale.

(b) Behavioral equation depends on the behavior of economic agents. Forexample, the consumption function depends on the behavior of households.Another well known behavioral relationship is the gravity equation used in atransport model (also used in external trade models to determine exports andimports), which has been described earlier.

(c)Definitional such as the incremental capital/output ratio (ICOR)

ICOR = K / Y

(d) Identities/ balance equation such as GDP = C + G + I + ST+X Mand GNP = GDP + NFI

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http://en.wikipedia.org/wiki/Labour_(economics)http://en.wikipedia.org/wiki/Capital_(economics)http://en.wikipedia.org/wiki/Labour_(economics)http://en.wikipedia.org/wiki/Capital_(economics)


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WhereGDP = Gross Domestic ProductGNP = Gross National ProductNFI = Net factor income from abroadC = private consumption expenditure

G = government consumption expenditureI = gross domestic investmentST = Stocks and inventoriesX = exports of goods and (non-factor) servicesM = imports of goods and (non-factor) services

(a) Structural / reduced form equations

Structural equations show simultaneous two-way relationships among variableshaving influences on each other, whereas reduced form shows one way relationships.For econometric estimation, structural relations are converted into reduced form

equations. We present below a simple example involving consumption andinvestment functions.

Structural Form (Closed economy with government)

Ct = a1 + b1 Yt + UtIt = a2 + b2 Yt + b3 Yt-1 + VtYt = Ct + It + Gt

Reduced form

Yt = a3 + b4 Yt-1 + b5 Gt + Wt

Where a3 = (a1 + a2)/ b6, b4 = b3 / b6, b5 =1/ b6, b6 =1 - b1- b2

Ut, Vt, Wt = (Ut+Vt)/ b6 are random error terms with standard assumptions of normaldistribution with zero mean and constant standard deviation (homoscedasticity) inrepeated samples.

3.10 Structural Macroeconometric Model

Structural macro modeling dates back to the pioneering work of Tinbergen and Klein andsubsequent work at the Cowles Commission. Keynesian macroeconomic forecastingmodels, based on income-expenditure-employment relations, a set of stochasticbehavioral and technological equations complemented by suitable identities, enjoyed agolden age in the 1950s and 1960s and progressively grew in size and sophistication ofestimation. Some of the important examples of such large scale structural models are theFederal Reserve Board (FRB) Models, Fairs model of the US economy, MurphysModel of the Australian economy (1988), London Business School (LBS) Model,National Institute of Economic and Social Research (NIESR) Model and HM Treasury(HMT) model for the UK economy.

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Critiques of Large Scale Econometric Models

Four important methodological critiques against the large scale models are worth noting:

(a) First, more than three decades ago Lucas (1976) questioned the practicalusefulness of large scale macroeconomic models as a guide to policy planning, because most models were built on the assumption of a given structure andstability of parameters, which did not exist in real markets because ofimperfections in knowledge and information. The Lucas critique, also known asthepolicy irrelevance doctrine, remains a milestone in macro modeling literatureand more and more models (Fair 1994, Taylor 1993 and Diebold 1998) were builtto incorporate imperfections and rational expectations.

(b) Secondly, Sims (1980) raised serious doubts about the traditional modeling ofbehavioral relations, because of their incredible restrictions on the short-term

dynamics. Sims alternative modeling strategy led to the Vector AutoRegression (VAR) models. While VAR models usually produce unconditionalforecasts that might outperform forecasts generated by large macroeconomicmodels, their application for wide ranging policy planning is still limited.

(c) Thirdly, greater attention was paid to the treatment of non-stationarity in macrovariables. This led to modeling techniques involving cointegration and provideda framework for model dynamics to evolve around long term equilibriumrelationships. In this sphere, major works date back to Nelson and Plosser (1982)and Engle and Granger (1987).

(d) Finally, large econometric models suffered from what is known as the curse ofdimensionality. By including too many variables, often accidental or irrelevantdata are embodied into the model, leading to poor estimates of parameters due toproblems of multi-collinearity (Clements and Hendry 1995).

In spite of these criticisms, large models had left a rich analytical, methodological, andempirical legacy. Diebold (1998) concludes: "Although the large-scale macroeconomicforecasting models did not live up to their original promise, they nevertheless left a usefullegacy of lasting contributions from which macroeconomic forecasting will continue tobenefit. They spurred the development of powerful identification and estimation theory,computational and simulation techniques, comprehensive machine-readablemacroeconomic data-bases and much else.

It is also worth noting that noble laureate Klein (1999), one of the foremost experts onmacro modeling, continues to put faith in large size models arguing that small modelscannot capture the complex nature of an economy and that this may lead to misleadingpolicy conclusions.

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3.11 Vector Autoregressive Model (VAR)

Because of their poor performance, large scale macro econometric models were followedin the 1980s and 1990s by powerful non-structural models such as Vector Auto-Regression (VAR) model and Dynamic Stochastic General Equilibrium (DSGE) model.

Although called structural models, these models lacked depth in their structuralspecification. One of the first efforts to rectify the limitation was made by Lucas (1972) based on a dynamic stochastic model that provided for fully articulated preferences,technologies and rules of the game. This type of modeling was given the name ofDynamic Stochastic General Equilibrium (DSGE) modeling.

More recently, work on autoregressive moving average (ARMA) and autoregressiveintegrated moving average (ARIMA) models developed at a rapid pace with thepioneering work ofBox and Jenkins (1970). Although Box-Jenkins framework dealtprimarily with univariate modeling, extensions of the Box-Jenkins models involved

multi-variate modeling and notably Sims advocated the use of Vector Autoregression(VAR) models. Sims (1972) had argued that the division of variables into endogenousand exogenous variables, as done in the structural models, was arbitrary and VAR modelscould avoid that by treating all variables as endogenous.

Over the past two decades vector autoregressive (VAR) analysis has become a standardtool in empirical research. It has several advantages.

First, it is a flexible way of modeling since it allows all past variables to have an impacton any present variable. Second, it is a systems approach that takes into account the interaction of variablesamong themselves. Third, it has desirable time series properties

Disadvantages of VAR

Though VAR analysis is a convenient tool these advantages come at a price.

First, the number of variables that can be included in the VAR is limited because themodel is unrestricted and runs out of degrees of freedom quickly. "In practice, VAR modeling for more than four variables is rarely feasible" (Charemzaand Deadman 1997).

Non-structural models have been used as a powerful tool for forecasting. These are alsoconvenient, as no independently predicted values of exogenous variables are needed togenerate forecasts as in the case of structural models. However, as these models produceunconditional forecasts; these are not directly useful for policy analysis.

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3.12 General Equilibrium Model (GEM)

General equilibrium models are economy wide models and can include multi-sector,multi-commodity and multi-economic agents.

Such models have the advantage of responding to shocks and fulfilling the conditions ofoptimality, technological feasibility and resource constrains. General equilibrium model has also a strong theoretical and analytical background. In the 1970s there were major advances in econometric estimation and test techniquesthat permitted application of general equilibrium models to large data sets.

Computing general equilibrium

Until the 1970s, general equilibrium analysis remained theoretical. However, withadvances in computing power and the development of Leontiefinput-output matrices, itwas possible to model national economies or even the global economy. Attempts weremade by the economists and multilateral organizations to solve for general equilibriumprices and quantities empirically.

Applied general equilibrium(AGE) models were pioneered byHerbert Scarfin 1967and subsequently by his students John Shoven and John Whalley in 1972 and 1973,,which provided a methodology to solve numerically the Arrow-Debreu GeneralEquilibrium system. In the 1980's however, AGE models lost popularity due to theirinability to provide a precise solution and their high cost of computations. Also, Scarf'smethod was proved to be non-computable to a precise solution by Velupillai (2006).

Computable general equilibrium (CGE) models replaced AGE models in the mid1980s, as the CGE model was able to provide relatively quick and large computablemodels for the whole economy, and was preferred by the governments and the WorldBank. Operational CGE models are based on static, simultaneously solved, macrobalancing equations (from the standard Keynesian macro model), giving a precise andexplicitly computable result (Mitra-Kahn 2008).

Keynesian6, Post-Keynesian economists7, and neoclassical economist in general,criticized general equilibrium theory. Specifically, they argue that general equilibriumtheory is neither accurate nor useful that economies are not in equilibrium and thatmodeling by equilibrium is "misleading", and that the resulting theory is not a usefulguide, particularly for understanding ofeconomic crises.

6 The long run is a misleading guide to current affairs. In the long run we are all dead. Economists setthemselves too easy, too useless a task if in tempestuous seasons they can only tell us that when the stormis past the ocean is flat again. John Maynard Keynes,A Tract on Monetary Reform, 1923, Ch. 3.

7 It is as absurd to assume that, for any long period of time, the variables in the economic organization, orany part of them, will "stay put," in perfect equilibrium, as to assume that the Atlantic Ocean can ever bewithout a wave. Irving Fisher,The Debt-Deflation Theory of Great Depressions, 1933

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More methodologically, it is argued that general equilibrium is a fundamentally staticanalysis, rather than a dynamic analysis, and thus is misleading and inapplicable. Thetheory ofdynamic stochastic general equilibrium seeks to address this criticism.

3.13 Dynamic Stochastic General Equilibrium Model (DSGEM)

Dynamic Stochastic General Equilibrium Model

The DSGE methodology attempts to explain macro economic phenomena, such aseconomic growth and the effects of monetary and fiscal policies, on the basis ofmacroeconomic models derived from microeconomic principles. Unlike traditionalmacroeconometric forecasting models, DSGE macroeconomic models are not vulnerableto the Lucas critique (Woodford, 2003, p. 11; Tovar, 2008, p. 15).

Structure of DSGE models

As the name indicates, DSGE models are dynamic, studying how the economy evolvesover time. They are also stochastic, taking into account the fact that the economy isaffected by random shocks such as technological change, fluctuations in the price of oil,or errors in macroeconomic policy-making.

Traditional macroeconometric forecasting models used by central banks in the 1970sestimated the dynamic correlations between prices and quantities in different sectors, andoften included hundreds of variables. Since DSGE models are technically more difficultto solve and analyze, they tend to abstract from sectoral details, and include limitednumber of variables. DSGE models provide logical consistency and spell out thefollowing aspects of the economy.

Preferences: the objectives of economic agents are specified. For example, householdsmight be assumed to maximize a utility function over consumption and labor efforts.Firms might be assumed to maximize profits.

Technology: the productive capacity of economic agents are specified. For example,firms might be assumed to have a production function, specifying the relationshipbetween output and inputs of labor and capital, and technological change.Institutional framework: the institutional constraints under which economic agentsinteract are also specified. In many DSGE models, this simply means that economicagents make their rational choices within existing socio-political context, exogenouslyimposed budget constraints, and specific monetary and fiscal policies.

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http://en.wikipedia.org/wiki/Staticshttp://en.wikipedia.org/wiki/Dynamicshttp://en.wikipedia.org/wiki/Dynamic_stochastic_general_equilibriumhttp://en.wikipedia.org/wiki/Staticshttp://en.wikipedia.org/wiki/Dynamicshttp://en.wikipedia.org/wiki/Dynamic_stochastic_general_equilibrium


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Advantages and disadvantages of DSGE modeling

By specifying preferences (what the agents want), technology (what the agents canproduce), and institutions (the way they interact), it is possible to solve the DSGE modelto predict what is actually produced, traded and consumed. In principle, it is also possible

to make valid predictions about the effects of changing the institutional framework.

The DSGE model offers several following advantages.

The key feature of DSGE model is the use of microeconomic foundations formodeling the behavior of the aggregate economy, such as impact of changes in thedegree of competition between firms, and analyzing the effects of economic policymeasures from economic agents welfare point of view.

The individual rationality behind the aggregate behavior is useful to analyze theimpact of monetary policy on private agents expectations. Moreover, rationalexpectations differentiate effects between permanent and transitory shocks andbetween anticipated and unanticipated shocks.

The general equilibrium structure maintain in the model the consistency between flowand stock variables, such as investment with respect to capital and the current accountbalance with respect to the net foreign assets position.

There is recent empirical evidence showing that DSGE models can have a betterforecasting performance than purely statistical models.

The flexibility of these models allows solving a wide range of questions relevant tothe central bank, such as the role of financial frictions in the transmissionmechanisms, the role of frictions in the labor market, effects of changes in relativeprices, implications of aggregate shocks to a specific sector and others.

Additionally, the larger structure helps to disentangle the sources of macroeconomicfluctuations (eg, inflation of recent years has been generated by supply or demand

shocks?).

The major uses of this model are:

to conduct policy analysis, forecast and simulations conditional on the behavior ofmonetary (and / or fiscal) policy.

to decompose macroeconomic variables on the factors that explain their fluctuations(shocks), both in history and forecast.

to evaluate assessment of the effects of a lower reaction to the exchange rate or astronger reaction to inflation.

to estimate the non-observable variables such as the natural interest rate, the potentialoutput, the real exchange rate equilibrium and the natural unemployment rate.

to assess the impact of economic policy measures in the long term using the steadystate equilibrium.

to estimate all of them simultaneously and in consistency framework.

However, given the difficulty of constructing accurate DSGE models, most central banksstill rely on traditional macroeconometric models for short-term forecasting. However,the effects of alternative policies are increasingly studied by DSGE methods.

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Examples of DGSE Model

One well-known example of the DSGEM is that of Kydland and Prescott (1982) model

which argued that a neo-classical model driven by technology shocks could explain alarge part of US business cycles. These models, also initially called real business cyclemodels, combine preferences with technologies. Kydland and Prescott (1982) used non-linear quadratic models so that non-linearity in technologies can be accommodated.Although solving these models is not a straightforward exercise, in most cases these areapproximated by vector autoregressions. In estimating the DSGE models, formalestimation is often combined with calibration methods, a good description of which isavailable in Kydland and Prescott (1996).

More recent arguments favor formal estimation of the DSGE models and search for bestfitting parameters. Maximum likelihood estimators have been the most preferred

estimators. Current work on DSGE modeling aims at accommodating heterogeneity inagents using representative agents and suitable aggregator functions. One characteristicof DSGE models is their parsimony.

The European Central Bank (ECB) has developed a DSGE model, often called theSmets-Wouters model, which analyzes the economy of the Eurozone as a whole (withoutanalyzing individual European countries separately). The model is intended as analternative to the Area-Wide Model (AWM), a more traditional empirical forecastingmodel which the ECB has been using for several years. The ECB webpage that describesthe Smets-Wouters model also discusses the advantages of building a DSGE modelinstead of relying on more traditional methods.

Equations in the Smets-Wouters model describe the choices of 3 types of decisionmakers: households, who choose how much to work, to consume, and to invest; firms,which choose how much labor and capital to employ; and the central bank, whichcontrols monetary policy. The parameters in the equations were estimated using Bayesianstatistical techniques so that the model approximately describes the dynamics of GDP,consumption, investment, prices, wages, employment, and interest rates in the Eurozoneeconomy. In order to accurately reproduce the sluggish behavior of some of the variables,model incorporates several types of frictions that slow down adjustment to shocks,including sticky prices and wages, and adjustment costs in investment.

Critics of DSGE Model

In his blog for the Financial Times, Willem Buiter has argued that DSGE models can bemisleading. In his view, DSGE models rely excessively on an assumption of completemarkets, and are unable to describe the highly nonlinear dynamics of economicfluctuations, making training in 'state of the art'macroeconomic modeling 'a privatelyand socially costly waste of time and resources'.

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N. Gregory Mankiw, regarded as one of the founders of New Keynesian DSGEmodeling, has argued that 'Neoclassical and new Keynesian research has little impact onpractical policy makers. From the standpoint of macroeconomic engineering, the work ofthe past several decades of modeling looks like an unfortunate wrong turn.

Replying to Mankiw, Michael Woodford argues that DSGE models are commonly usedby central banks today, and have strongly influenced policy makers. However, he arguesthat what is learned from DSGE models is not so different from traditional Keynesiananalysis.

Making an assessment of the future of macroeconomic modeling and forecasting,Diebold (1996) wrote: The hallmark of macroeconomic forecasting over the next 20 years will be a marriage of the best of nonstructural and structural approaches,

facilitated by advances in numerical and simulation techniques that will help

macroeconomics to solve, estimate, simulate, and yes, forecast with rich models.

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Annex-4Data required for Macroeconomic Model

I T E MS 1985 1986 1987 .. .. 2006A. Population (million, as on 2 July

Population growth rate (% per annum)Work force (age 15-65 years)

Employment (in '000)

Total

Agriculture

Industry

Services

B. GDP: Supply Side

1. GDP const FC (Mill. NR)

Agriculture and allied

Industry

-- Manufacturing Mining and quarrying

-- Electricity, gas, water supply

-- construction

Services

-- Trade, hotels and restaurants

-- Transport, storage, commn.

-- Financial, real estate, business

-- Community, social, personal

GDP const 2004 FC (Mill. NR)

(Plus) Indirect taxes less subsidies

GDP at cons mp (Mill. NR)

2. GDP at current mp (Mln NR)Agriculture and allied

Industry

-- Manufacturing

Mining and quarrying


-- construction

Services





GDP at current market prices(Less) Indirect taxes less subsidies

GDP at current FC (Mill. NR)

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I T E MS 1985 1986 1987 .. .. 2006C. Demand side of GDP:

1.GDP at constant price (GDP) (Mln NR)=a+b+c+d+e-f+g

(a) Private consumption

(b) Government consumption

(c) Gross fixed capital formation

(d) Change in stocks

(e) Exports of goods and services

(f) Imports of goods and services

(g) Statistical discrepancy

2.GDP at current mp (Mln NR) =a+b+c+d+e-f+g

(a) Private consumption

(b) Government consumption

(c) Gross fixed capital formation

(d) Change in stocks

(e) Exports of goods and services

(f) Imports of goods and services(g) Statistical discrepancy

3. Investment (at constant prices)


Industry

-- Manufacturing



-- construction

Services


-- Transport, storage, commn.-- Financial, real estate, business


4. Investment (at current prices)


Industry

-- Manufacturing



-- construction

Services





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I T E MS 1985 1986 1987 .. .. 20065.Investment Financing at current mp (Mill NR)

5. Gross domestic capital formation = 4(c)+ 4(d)

--- Public investment

--- Private investment

D.Gross national saving =a+b+c(a) Gross domestic saving

--- Public savings

--- Private savings

(b) Net factor income from abroad

(c) Net current transfers from abroad

E. Agriculture sector

1 (a) Total cropped area (000 hectares)

(b) Irrigated area ('ooo hectares)

(c) Average rainfall (mm per day)

(d) Rainfall dispersion over months (mm per day)

(e) Food grains production (000 tones)

2. Agriculture production index 1999-2001=100

Manufacturing prod. index 1986/87 = 100

Electricity consumption (Million KWH)

Exchange rate and Inflation

Ave Exchange Rate (NR/US$)

GDP at current mp (mn US$)

Percapita GDP current mp (US$)

Consumer (Urban) Price Index (1995/96=100)

Food

Non-Food

CPI Inflation (Annual % change)

Food

Non-Food

Implicit GDP Deflator

Balance of Payments (Million US$)Goods balance

Exports of goods f.o.b.

Imports of goods c.i.f.

Non-factor services and factor incomes,net

Credits

Debits

Transfers, net

Official, net

Private, net

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I T E MS 1985 1986 1987 .. .. 2006BOP continued:

Remittances

Current account balance (CAB)

Exports of goods & services

Imports of goods & servicesCapital account

Official loans, net

Loans

Amortization (AMORT)

Private capital inflow

FDI, net

Other investment, net

Errors and omissions

Overall balance

Foreign exch reserve end period

Equivalent to months of imports

Exports of goods to India

Exports of goods to China

Imports of goods from India

Imports of goods from China

Central Govt Operations (Billion NR)

1.Total Revenue and grants = 1.1+1.2

1.1 Total revenue = (a) + (b)

(a) Current Revenue = (i)+(ii)

(i) Tax revenue

(ii) Nontax revenue

(b) Capital receipts1.2 Grants

2. Total Expenditure and net lending = 2.1+2.2

2.1 Total expenditure = (a) + (b)

(a) Current expenditure

--- Wages and salaries

--- Other charges

--- Interest payments

(b) Capital expenditure and net lending

--- Capital expenditure

--- Net lending

3. Current A/C Balance =1.1(a) - 2.1 (a)

4. Capital A/C Balance = 1.1 (b) - 2.1 (b)

5. Overall Fiscal Balance = 1-2 = 3+4

6. Primary Balance = 5 - Interest payments

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I T E MS 1985 1986 1987 .. .. 2006Monetary survey (Billion NR)

Net foreign assets

Net domestic assets

Domestic credits

Claims on government, net

Claims on public enterprises, net

Claims on private sector, net

Claims on financial institutions

Other items, net

Broad money (M3) demand

Broad money (M3) supply = M1+Time Deposits

Quasi Money (M1) = (a) +(b)

(a) Currency outside banks

(b) Demand Deposits

Time Deposits

Interest Rates (% per annum)Savings deposit rate

Term deposit rate (one year)

Bank lending rate

NRB Bank rate

External Debt Outstanding: (Million US$)

Medium and long term

Public and publicly guaranteed

Private non-guaranteed

Short-term debt

Use of IMF credits

External debt service

Amortization


IMF

Interest payments


IMF

Short Term

Average Terms of external borrowing:

Interest (percent per annum)

Maturity (years)

Grant period (years)

Grant element (percent)

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Annex-5: Terms of Reference for the Modeling Consultants

Component B: Macroeconomic Modeling

5.1 Macroeconomic Modeling Specialist/Team Leader (international, 6 person

months) will perform the following tasks:

(i) Update the Nepal Macroeconomic Model (NMEM) based on the new system ofnational accounting adopted by CBS and changes in the structure of the Nepaleseeconomy;(ii) develop a new dynamic stochastic general equilibrium (DSGE) type model suitablefor Nepal;(iii) develop software for updated NMEM and DSGE-type macro models;(iv) develop a user manual for both the NMEM and DSGE-type model that includes thecalibration methodology;(v) devise a model consistent with the data linkages and reporting systems required for

an efficient modeling exercise, and ensure the sustainability of efforts in the area ofmacroeconomic modeling in Nepal;(vi) undertake growth and macroeconomic projections for the Nepalese economy andcompare the consistency of results from both the models;(vii) provide training to the staff of the agencies involved, and other agencies suggestedby the steering committee and the technical committee, on features of the models andsoftware to enable them to undertake the modeling exercise independently;(viii) work closely with the national consultants and provide guidance on their respectiveassignments and supervise their work;(ix) arrange the facilities for workshops, training, seminars, and dissemination of thefindings;(x) undertake consultations with all stakeholders and incorporate feedback fromdeveloping the two models and other expected outputs of the TA;(xi) work closely with the Executing Agency (EA), the steering committee, technicalcommittee, and other relevant stakeholders for developing the models;(xii) oversee overall TA implementation and prepare required reports;(xiii) coordinate the procurement of hardware and software needed for upgrading thesoftware for NMEM and the new model with ADB, the EA, and the national informationtechnology specialist; and(xiv) undertake other tasks as required by the steering committee and ADB.

5.2 Macroeconomic Modeling Specialist (national, 5 person-months) will perform thefollowing tasks:

(i) review the previous macroeconomic modeling work in Nepal, identify gaps andprovide suggestions for upgrading the NMEM and DSGE-type of model for Nepal;(ii) provide the data inputs on Nepal required by the team leader to upgrade the NMEMand develop a new DSGE-type model for Nepal;(iii) assist the team leader in undertaking the macroeconomic modeling andmacroeconomic forecasting for the Nepalese economy;

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(iv) assist the team in designing models that are based on economic principles andmethodology for macroeconomic modeling of the economy;(v) assist the international consultant in upgrading the NMEM and developing the DSGEmodel for Nepal;(vi) train staff in the EA on the features of the economy, and interpretation of the results;

(vii) participate in the consultation process and incorporate feedback;(viii) work closely with the team leader, EA, steering committee, and technicalcommittee, and provide required inputs; and(ix) undertake other tasks as required by the steering committee and ADB.

5.3 Econometrician (national, 5 person-months) will perform the following tasks:

(i) assist the team leader on data needs and calibration methods for upgrading the NMEMand developing a new DSGE-type macro model;(ii) assist the team leader in designing the models that are statistically sound and yield

robust results;

(iii) assist the team in designing models that use appropriate econometric principles andmethodology for macroeconomic modeling of the economy;(iv) train EA staff on data requirements, inputs to the model, calibration methods, andrunning the models;(v) participate in the consultation process and incorporate feedback;(vi) work closely with the team leader, EA, steering committee, and technical committee;and provide required inputs; and(vii) undertake other tasks as required by the steering committee and ADB.

5.4 Information Technology Specialist (national, 2 person months) will perform thefollowing tasks:

(i) check the specifications of the hardware and software that have been purchased andinstalled at the Nepal Rastra Bank (NRB) and their connectivity to the databases at NRBand CBS;(ii) install the new macroeconomic modeling software in the NRB and ensure that staff atNRB can install the software in case need arises;(iii) complete the database migration to the new modeling format;(iv) undertake data validation and train the staff of the NRB on database and systemmaintenance activities; and(v) undertake other tasks as required by the steering committee and ADB.

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Annex-6.1 Brief Curriculum Vitae of Prof. Tarun Das

Prof. TARUN DAS, Ph.D.International Modeling Specialist

E-mail: [email protected]: Economic. Adviser, Ministry of Finance and

Adviser ( Modeling), Planning Commission, Govt. of India,

Professor (Public Policy), IILM, New Delhi and

ADB Strategic Planning Expert ,Min of Finance, Govt of Mongolia.

Google search: Tarun Das, Economic Adviser, Ministry of Finance

Specialization Macro-economic modeling; public policy; governance reforms;strategic planning, performance based output budgeting; povertyand inequality measures; MDGs; National Plans, PRSP and PGRF;management of public debt and ccontingent liabilities.

Diversity in skills Possesses diversity in skills in research, training, teaching,modeling, policy planning and consultancy services.

Worked as Consultant tovarious internationalorganizations:

African Development Bank, Asian Development Bank, GDN,World Bank, IMF, ILO, UN-ESCAP, UNCTAD, UNDP, UN-ECA,UNITAR, UN-SIAP, Commonwealth Secretariat.

Countries worked in: Cambodia, China, Ethiopia, Gambia, India, Indonesia, Japan, LaoPDR, Malaysia, Mongolia, Nepal, Philippines, Samoa, Senegal,Singapore, Switzerland, Thailand, UK and USA.

Attended Conferences in: Bangladesh, Belgium, France, Germany, Morocco, Ireland, U.A.E.

Published Papers inInternational Journals:

Australian Economic Journal, Empirical Economics (Vienna),Environment and Planning (London), International Review ofEconomic and Commercial Science (Milan), Review ofMathematical Economics and Social Science (Milan). Journal ofIncome and Wealth, Journal of Regional Economics.

EducationalQualifications:

Gold Medalist in Quantitative Economics from Calcutta University,and holds a Ph.D. degree, as Commonwealth Scholar, from the EastAnglia University, England.

Major Professional Experiences in Backward Sequence:

23 Aug 2009-till date International Macroeconomic Modeling Specialist, ADB ResidentOffice in Nepal at Kathmandu and Nepal Rastra Bank.

20 Dec 08-10 Aug 09. African Development Bank Macroeconomic Adviser, Ministry ofFinance and Economic Affairs, the Gambia, Banjul.

Nov 2008- 10 Dec 08 Consultant to UN-ESCAP, Bangkok, Thailand.

Sep 2008 Oct 2008 Consultant to World Bank Country Office (Tashkent, Uzbekistan).

Jul 2008- Aug 2008 Consultant (Debt Sustainability Analysis), Com-Sec, London, U.K.June 2007- Jul 2008 ADB Strategic Planning Expert, MOF, Mongolia, Ulaanbaatar.

Feb 2006- May 2007 Professor (Public Policy), IILM, New Delhi-110003, India.

Mar 1989- Jan 2006 Economic Adviser, Ministry of Finance, Government of India.

Jan 1987- Feb 1989 Adviser (Modeling & Policy Planning), Planning Commission, Ind.

Oct 1984- Dec 1986 Chief Economist, Joint Plant Committee, Ministry of Steel, India.

Oct 1982- Sep 1984 Chief (Eco. Division), Bureau of Industrial Costs and Prices, India.

Sep 1978-Sep 1982 Deputy Adviser, UNDP Transport Modeling Project in India.

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Annex-6.2: Brief Curriculum Vitae of Prof. Durga Lal ShresthaMacroeconomic Modeling Specialist

Name: Dr. Durga Lal Shrestha1. Date of Birth; September 13, 1952

2. Academic Qualification: Ph. D. in Economics, Moscow, USSR, 1990.

Master of Arts (M.A.) in Economics, Merit(First) Class, TU,Nepal, 1977

Dr. Shrestha has 31 years of teaching and research experiences in TribhuvanUniversity (TU) of Nepal. Presently, he is the Professor at Centre for EconomicDevelopment and Administration (CEDA), a research centre of TU, Kathmandu.

In addition to teaching and research, he served in TU tendering different academicand administrative posts such as: Department Head, Assistant Campus Chief, Campus

Chief, Extracurricular Activity Chief, Member of Economics Subject StandingCommittee, and Member of Scrutiny Board in Controller of Examination of TU.

Dr. Shrestha worked as national consultant (Input-Output Model Expert) inStrengthening National Planning Commission (NPC) Project during (1992-1993).

He worked as an Advisor to the Honble Member for Physical Infrastructure andWater Resources Development Section of the Government of Nepal, NPC Secretariat,for the formulation of the Ninth Plan in 1997-1998.

He worked as an Input-Output Model expert for Perspective Energy Plan in 1995and for the Tenth Plan in 2002 in the Government of Nepal, NPC Secretariat.

He worked as resource economist/ Modeling expert in Phase I, II and III of Waterresources Strategy Formulation in three different periods i.e., 1996, 1999 and 2004

respectively for the Government of Nepal, and Water and Energy Commission.

He worked as national macro-economic modeling specialist in the developmentNepal Macroeconomic Model for debt sustainability analysis in the Government of Nepal Ministry of Finance (MOF) in 2005. Recently, he has also worked asMacroeconomist/Modeling Expert in the Energy Strategy Formulation Project of theGovernment of Nepal, Water and Energy Commission.

He also worked as a Visiting Senior Research Fellow at the Department ofEconomics, the University of Bergen of in 2001. He was involved as a team memberin the Norwegian Review Team of the Melamchi Water Supply Project in 2004.

Besides, being as a team leader, or a consultant or a researcher he hasaccomplished many research projects of different fields of economics such as:national planning, macro modeling, input-output modeling, Mid Term ExpenditureFramework (MTEF), energy, water resources, hydropower pricing, high valueagriculture crops, education, private sector development, tourism, industrial relation,and transportation.

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Annex 6.3:CV of Dr. Vikas Raj SatyalNational Econometrician

1. Personal data:Name : Vikash Raj SatyalAddress: Shantinagar, Kathmandu, Nepal. Tel: 9771- 4492685,

Cell: 9841413453 Email: [email protected]. Education

1994: Ph.D. (Statistics), University of Vienna, Austria M.Sc. (Statistics), Tribhuvan University, Nepal.

3. Other qualification

Course attended: Operational Research, Vienna University, 1994.Training: Logical Frame Approach, IIDS, Kathmandu, 2003Computer ability: SPSS, MINITAB, SAS, EVIEWS

4. Work Experience 1986- date: Tribhuvan University, Nepal: currently as Associate Prof./ Head,

Department of Statistics, Amrit Campus

1996 to-date: Consultant-Statistician, Institute for Integrated Development Studies(IIDS), Madikhatar, Maharajgunj, Kathmandu

2001(4 months): Consultant Statistician/Survey Manager-" Industrial DevelopmentProspective Plan Vision 2020", United Nations Industrial Development Organization(UNIDO)/ United Nations Development Program (UNDP), Kathmandu.

5. Major Publication 2008:Kingdom of Nepal:Technical Assistance to Nepal for Reaching the Most

Disadvantaged Groups in Mainstream Rural Development(co-author), ADB (webbased)

2007, Nepal:Social Security for the Elderly, (co-author), ed. Irudaya Rajan, SocialSecurity for the Elderly in South Asia, Rutledge Taylor Francis.

2007: Targeted Development Programs in Nepal: A Selective Review (co-author),IIDS/ESP/DfID, Nepal

2006, Economic Growth in Nepal, 1996-2000, (co-author), ed. Kirith S. Parikh,Explaining Growth in South Asia, Oxford University Press, London

2006,Nepal Country Report: Research & Dialogue with Political Parties(co-author),International IDEA, Stockholm(web based) 2006, Nepal: Conflict Resolution and Sustainable Peace: Decentralization and

Regional Development (co-author), IIDS 2003: Gender Budget Audit in Nepal (Co-author) : The United Nations Development

Fund for Women (UNIFEM/Nepal) 2000:Macro-Economic Modeling of South Asian Economies with Intra-SAARC Trade

Link(co-author): GDN/SANEI

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Annex 6.4:Curriculum Vitae of National IT Specialist

Name: Mr. Rojan Bajracharya

Address: GPO Box 12558, Kathmandu, NepalTel: +911-1-4282629,Fax: +977-1-4373682Email:[email protected],[email protected]

Education Qualification: Masters Degree in Economics from Tribhuvan University (2002 to2005), Thesis Title Macro Econometric Analysis of Nepal with reference to Macro ModelingExercise between 1990 to 2005

Work Experience

Econometric Modeling AssignmentWorked as Core Team Member of Research Study on Crafting a Socially Inclusive ServicePolicy to Address Vulnerability of Marginalized Communities: Lessons from Nepals EducationPolicy funded by Global Development Network under Global Development Award (February toDecember 2008)

Worked as Junior Researcher of Research Study on Impact of OECD Countries AgriculturalLiberalization on Household Welfare in Nepal funded by Global Development Network (July2005 to December 2006)

Worked as Program Expert to Institute for Policy Research and Development (IPRAD) team onprogramming the simulation part of Macro Econometric Model (1 st to 30th November 2004)

Worked as Project Associate in ADB/TA on Strengthening Institutional Capacity for EffectivePublic Debt Management (August 2003 to December 2005)

Other Work AssignmentsWorking as Communication Consultant for Social Inclusion Research Fund, SNV Nepal(February 2009 to Still)

Worked as Education Consultant for South Asia Watch for Trade, Economic and Environmentin UNDP funded research study on Nepals Service Sector Export Potential (January toFebruary 2008)

PublicationEdited Book Social Inclusion and Nation Building: Abstracts of Researches Supported by SocialInclusion Research Fund Published by Social Inclusion Research Fund, SNV Nepal (2009)

Co- Authored Article on Rising Food Price in South Asia in Trade Insight, May 2008 Issue

Software KnowledgeWorking Econometric Software: EviewsWorking Statistical Software: SPSS

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Annex 7.1-A Work Plan Milestones for Prof. Tarun DasFor Component B: Development of Macroeconomic Model for Nepal

Tarun Das, Macroeconomic Modeling Specialist, joined ADB Resident Office in Kathmandu onMonday, the 24 Aug 2009; and held discussions and initial briefings with Mr. Shahid Parwez,Program/Project Officer, ADB Nepal Resident Mission; Dr. Nephil Matangi Maskay, Director,

Nepal Rastra Bank; Prof. Dr. Durga Lal Shrestha, Macroeconomist and Chief, Poverty Analysisand Economic Development Group, Centre for Economic Development and Administration,Tribhuvan University; Dr. Ram Sharan Kharel, Assistant Director, Nepal Rastra Bank; Dr.Vikash Raj Satyal, Consultant Statistician, Institute for Integrated development Studies, and Dr.Bama Dev Sigdel, Deputy Director, NRB. In consultation with all, the following work planmilestones were decided.

Work Plan Milestones:1. Preparation of the Draft Inception Report by 10 September 20092. Finalization of the Inception Report and its presentation to the Steering Committee/

Technical Committee by 22 September 2009

3. Review the NMEM and identify deficiencies and their causes by 09 Oct 2009

4. Conduct research & explore data sources to upgrade the NMEM by 16 Oct 20095. Set up Workstations for Software/ hardware Testing by 4th January 20106. Finalize choice and purchase of software/hardware by 15th January 20107. Finalize the upgraded NMEM by 31 January20108. Undertake research to develop a DSGE-type model for Nepal by 24 Feb 20109. Develop the DSGE-type model and the ensuing software by 31 May 2010.10. Develop a user manual, including calibration methodology, to undertake quantitative

macroeconomic analysis using the DSGE model by 25 June 201011. Conduct seminars/ workshops on the features of the two models by 30 Sep 201012. Provide on-the-job training to three staff from each of the agencies NRB, CBS, NPC,

MOF and the Department of Economics, Tribhuvan University, and other relevant

agencies (staff will be selected in close coordination with the executing agency, SteeringCommittee, and the Technical Committee) by 30 Sep 2010.

Proposed Phases Period Number of workingdays (Total Days)

Total Working days

First Phase 24 Aug 2009 to16 Oct 2009

33 (55) 33

Leave of Absence 17 Oct 2009 to3rd Jan 2010

0 (77) 33

Second Phase 4 Jan 2010 to24 Feb 2010

36 (53) 69

Leave of Absence 20 Feb 2010 to

30 April 2010

0 (70) 69

Third Phase 03 May 2010 to25 June 2010

40 (56) 109

Leave of Absence 26 June 2010 to31 Aug 2010

0 (67) 109

Fourth Phase 1 Sept 2010 to03 Oct 2010

23 (33) 132

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Annex-7.1-B Work Plan Matrix of Prof. Tarun Das, International Consultant(132 working days during August 2009- October 2010)

Tasks 24 Aug-16 Oct2009

04 Jan-24 Feb2010

03May-25

June

2010

01Sep-

03 Oct

2010(i) Update the Nepal Macroeconomic Model (NMEM)

based on the new system of national accounting adoptedby CBS and structure changes in the Nepalese economy;

(ii) develop a new dynamic stochastic general equilibrium(DSGE) type model suitable for Nepal;

(iii) develop software for updated NMEM and DSGE-type

macro models;

(iv) develop a user manual for both the NMEM andDSGE-type model that includes the calibrationmethodology;

(v) devise a model consistent with the data linkages andreporting systems required for an efficient modelingexercise, and ensure the sustainability of efforts in thearea of macroeconomic modeling in Nepal;

(vi) undertake growth and macroeconomic projections forthe Nepalese economy and compare the consistency ofresults from both the models;

(vii) provide training to the staff of the agencies involved,and other agencies suggested by the steering committeeand the technical committee, on features of the modelsand software to enable them to undertake the modelingexercise independently;

(viii) work closely with the national consultants and provideguidance on their respective assignments and supervisetheir work;

(ix) arrange the facilities for workshops, training,seminars, and dissemination of the findings;

(x) undertake consultations with all stakeholders and

incorporate feedback from developing the two modelsand other expected outputs of the TA;

(xi) work closely with the Executing Agency (EA), thesteering committee, technical committee, and otherrelevant stakeholders for developing the models;

(xii) oversee overall TA implementation and preparerequired reports;

(xiii) Coordinate the procurement of hardware and software

needed for upgrading the software for NMEM and thenew model with ADB, the EA, and the national

information technology specialist; and

(xiv) Undertake other tasks as required by the steeringcommittee and ADB.

Total Working Days 33 36 40 23

Note: The appropriate period cell is ticked () in if a particular task continues during that period. Under the number oftotal working days (last row in the matrix), the expected number of working days in each period is indicated.

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Annex-7.3 Work Plan Matrix of Prof. Durga Lal Shrestha,National Macroeconomic Modeling Specialist

((110 working days spread over 5 months)

Tasks 24

Aug-16 Oct2009

04

Jan-24 Feb2010

03

May-25June2010

01

Sep-03 Oct2010

(i) review the previous macroeconomicmodeling work in Nepal, identify gaps andprovide suggestions for upgrading the NMEM andDSGE-type of model for Nepal;

(ii) provide the data inputs on Nepal requiredby the team leader to upgrade the NMEM anddevelop a new DSGE-type model for Nepal;

(iii) assist the team leader in undertaking the

macroeconomic modeling and macroeconomicforecasting for the Nepalese economy;

(iv) assist the team in designing models thatare based on economic principles andmethodology for macroeconomic modeling of theeconomy;

(v) assist the international consultant inupgrading the NMEM and developing the DSGEmodel for Nepal;

(vi) train staff in the EA on the features of theeconomy, and interpretation of the results;

(vii) participate in the consultation processand incorporate feedback;

(viii) work closely with the team leader, EA,steering committee, and technical committee, andprovide required inputs; and

(ix) undertake other tasks as required by thesteering committee and ADB.

(x) Attend meetings (steeringCommittee/ and other)

Total Working Days 11 36 40 23Note: The appropriate period cell is ticked () in if a particular task continues during that period. Under the number oftotal working days (last row in the matrix), the expected number of working days in each period is indicated.

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Annex 7.4 Dr. Vikash Raj Satyal, National EconometricianWork plan matrix (110 working days spread over 5 months)

Items Phase I(24 Oct to

16 Oct,

2009)

Phase II(04 Jan to24 Feb,

2010)

Phase III(03 May to25 June,

2010)

Phase IV(01 Sep to03 Oct,

2010)1. SWOT analysis for

inception report

2. Assist TL on data need andcalibration methods

3. Assist the TL in designingstatistically sound models

4. Assist the team in designingmodels that use appropriateeconometric principles andmethodology formacroeconomic modelingof the economy

5. Train EA staff onsoftware/data requirements,inputs of the developedmodel

6. Attain meetings(steering Committee/and other)

7. Assist TL in reportpreparation

Total working days 11 36 47 16

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Annex 7.5 Mr. Rojan Bajracharya, National IT SpecialistWork plan matrix (44 working days spread over 2 months)

Items of Activities24 Aug 2009 to

16 Oct 200904 Jan 2010 to

24 Feb 201003 May 2010 to25 June 2010

01 Sep 2010 to30 Sep 2010

(i) Assist TA

team leader toprepare the TAInception Report

(ii) Check thespecifications of thehardware andsoftware that havebeen purchased andinstalled at the Nepal Rastra Bank(NRB) and theirconnectivity to thedatabases at NRB

and CBS;

(iii) Install thenewmacroeconomicmodeling softwarein the NRB andensure that staff atNRB can install thesoftware in caseneed arises;

(iv) Completethe databasemigration to the

new modelingformat;

(v) Undertake data validationand train the staff ofthe NRB ondatabase andsystem maintenanceactivities; and

(vi) Undertak e other tasks asrequired by thesteering committee

and ADB.

Working Days 8 14 14 8

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Multiple Window Display

Unlike traditional statistics programs that support viewing only oneestimation equation or graph at a time, EViews allows forsimultaneous display of multiple objects, each in its own window.This true multiple window support makes it easy to perform side-by-

side comparisons of series plots, hypothesis tests, equationestimates, or model forecasts developed under alternativeassumptions.

EViews offers true multi-windowsupport.

Dynamic Object Updating

EViews incorporates the best of modern spreadsheet and relationaldatabase technology into tools for performing the traditional tasksof statistical software. The EViews object-based approach includessophisticated linking technology that allows you to definerelationships between multiple objects and external data sources.Series objects, for example, may be linked by formula to data inother series, to match merged or frequency converted data fromalternate data sets, or to data from external databases. When

defined in this fashion, the linked series dynamically updates itsdata whenever the underlying data change.

Similarly, an EViews model simulation object can be linked toequation or system objects so that the model specification updatesautomatically when the underlying equation or system is re-specified or re-estimated.

Modern linking technology offersdynamic updating of data.

Windows Integration

Couple all of this with strong Windows integration, including drag-and-drop file import for over twenty popular file formats and copy-and-paste export of presentation quality graphs and tables, and youhave a modern interface that allows you to accomplish, with ease,tasks that are difficult or impossible using traditional statisticalsoftware.

Easy data import using drag-and-drop.

Previous:EViews 6 Overview | Next:Powerful Analytic Tools

Part 2: Powerful Analytical ToolsPart 2: Powerful Analytical Tools

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In contrast with most other econometric software, there is no reason for most users to learn acomplicated command language. EViews' built-in procedures are a mouse-click away andprovide the tools most frequently used in practical econometric and forecasting work.

Basic Statistical Analysis

EViews supports a wide range of basic statistical analyses,encompassing everything from simple descriptive statistics toparametric and nonparametric hypothesis tests.

Basic descriptive statistics are quickly and easily computed over anentire sample, by a categorization based on one or more variables,or by cross-section or period in panel or pooled data. Hypothesistests on mean, median and variance may be carried out, includingtesting against specific values, testing for equality between series,or testing for equality within a single series when classified by othervariables (allowing you to perform one-way ANOVA). Tools forcovariance and factor analysis allow you to examine therelationships between variables.

You can visualize the distribution of your data using histograms,theoretical distribution, kernel density, or cumulative distribution,survivor, and quantile plots. QQ-plots (quantile-quantile plots) maybe used to compare the distribution of a pair of series, or thedistribution of a single series against a variety of theoreticaldistributions.

You can even perform Kolmogorov-Smirnov, Liliefors, Cramer vonMises, and Anderson-Darling tests to see whether your series isdistributed normally, or whether it comes from another distributionsuch as an exponential, extreme value, logistic, chi-square,Weibull, or gamma distribution.

EViews also produces scatter plots with curve fitting using ordinary,transformation, kernel, and nearest neighbor regression.

EViews performs a wide range ofbasic statistical analysis.

Examine the distribution of yourdata.

Add regression and curve fitting(and histogram borders) to your

scatterplots.

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Time Series Statistics and Tools

Explore the time series properties of your data with tools rangingfrom simple autocorrelation plots to frequency filters, from Q-statistics to unit root tests.

EViews provides autocorrelation and partial autocorrelationfunctions, Q-statistics, and cross-correlation functions, as well asunit root tests (ADF, Phillips-Perron, KPSS, DFGLS, ERS, or Ng-Perron for single time series and Levin-Lin-Chu, Breitung, Im-Pesaran-Shin, Fisher, or Hadri for panel data), cointegration tests(Johansen for with MacKinnon-Haug-Michelis critical values and p-values ordinary data, and Pedroni, Kao, or Fisher for panel data),causality, and independence tests.

EViews also provides easy-to-use front-end support for the U.S.Census Bureau's X11 and X12-ARIMA seasonal adjustmentprograms, as well as the Tramo/Seats software, which is quitefrequently used by European researchers. Simple seasonal

adjustment using additive and multiplicative difference methods isalso supported in EViews.

You can even use EViews to compute trends and cycles from timeseries data using the Hodrick-Prescott filter, Baxter-King,Christiano-Fitzgerald fixed length and Christiano-Fitzgeraldasymmetric full sample band-pass (frequency) filters.

Explore the time seriesproperties of your data.

EViews provides easy-to-useinterfaces to X12 and

Tramo/Seats.

Use filters to compute trends andcycles from your time series

data.

Panel and Pooled Data Statistics and Tools

EViews features a wide variety of tools designed to facilitateworking with both panel and pooled/time series-cross section data.

Define panel structures with virtually no limit on the number ofcross-sections or groups, or on the number of periods orobservations in a group. Dated or undated, balanced orunbalanced, and regular or irregular frequency panel data sets areall handled naturally within the EViews framework.

Data structure tools facilitate transforming your data from stacked(panel) to unstacked (pooled) formats, and back again. Smart links,auto series, and data extraction tools, allow you to slice, match

Dated or undated, balanced orbalanced...EViews understands

your panel data structure.

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Single Equation Estimation

EViews allows you to choose from a full set of basic single equationestimators including: ordinary and nonlinear least squares (multipleregression), weighted least squares, two-stage least squares(instrumental variables), quantile regression (including least

absolute deviations estimation), and stepwise linear regression.Weighted estimation is available for all of these techniques.Specifications may include polynomial lag structures on anynumber of independent variables.

For time series analysis, EViews estimates ARMA and ARMAXmodels, and a wide range of ARCH specifications. Structural timeseries models may be estimated using the state space object.

In addition to these basic estimators, EViews supports estimationand diagnostics for a variety of advanced models.

Generalized Method of Moments (GMM)

EViews supports GMM estimation for both cross-section and timeseries data (single and multiple equation). Weighting optionsinclude the White covariance matrix for cross-section data and avariety of HAC covariance matrices for time series data. The HACoptions include prewhitening, either quadratic or Bartlett kernels,and fixed, Andrews, or Newey-West bandwith selection methods.

ARCH

If the variance of your series fluctuates over time, EViews canestimate the path of the variance using a wide variety ofAutoregressive Conditional Heteroskedasticity (ARCH) models.EViews handles GARCH(p,q), EGARCH(p,q), TARCH(p,q),PARCH(p,q), and Component GARCH specifications and providesmaximum likelihood estimation for errors following a normal,Student's t or Generalized Error Distribution. The mean equation ofARCH models may include ARCH and ARMA terms, and both themean and variance equations allow for exogenous variables.

Limited Dependent Variables

EViews also supports estimation of a range of limited dependentvariable models. Binary, ordered, censored, and truncated modelsmay be estimated for likelihood functions based on normal, logistic,

and extreme value errors. Count models may use Poisson,negative binomial, and quasi-maximum likelihood (QML)specifications. EViews optionally reports generalized linear modelor QML standard errors.

Panel and Pooled Time Series-Cross Section

EViews offers various panel and pooled data estimation methods.In addition to ordinary linear and non-linear least-squares, equationestimation methods include 2SLS/IV and Generalized 2SLS/IV, and

EViews offers a full range ofsingle equation estimators.

GMM estimation offers a varietyof weighting matrix and

covariance options.

Easy-to-use dialogs make it easyto specify your ARCH model.

EViews estimates both ML andQML count models.

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System Estimation

EViews also offers powerful tools for analyzing systems ofequations. You may use EViews to estimation of both linear andnonlinear systems of equations by OLS, two-stage least squares,

seemingly unrelated regression, three-stage least squares, GMM,and FIML. The system may contain cross equation restrictions andin most cases, autoregressive errors of any order.

Vector Autoregression/Error Correction Models

Vector Autoregression and Vector Error Correction models can beeasily estimated by EViews. Once estimated, you may examine theimpulse response functions and variance decompositions for theVAR or VEC. VAR impulse response functions and decompositionsfeature standard errors calculated either analytically or by MonteCarlo methods (analytic not available for decompositions) and maybe displayed in a variety of graphical and tabular formats.

You may impose and test linear restrictions on the cointegratingrelations and/or adjustment coefficients. EViews' VARs also allowyou to estimate structural factorizations (VARs) by imposing short-run (Sims 1986) or long-run (Blanchard and Quah 1989)restrictions. Over-identifying restrictions may be tested using the LRstatistic reported by EViews.

VARs support a variety of views to allow you to examine thestructure of your estimated specification. With a few clicks of themouse, you can display the inverse roots of the characteristic ARpolynomial, perform Granger causality and joint lag exclusion tests,evaluate various lag length criteria, view correlograms andautocorrelations, or perform various multivariate residual baseddiagnostics.

Multivariate ARCH

Multivariate ARCH is useful in modeling time varying variance andcovariance of multiple time series. A number of popular ARCHmodels, such as the Conditional Constant Correlation (CCC), theDiagonal VECH, and the Diagonal BEKK, are available. Exogenousvariables are allowed in the

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