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Forthcoming inJournal of Economic Surveys, Vol 15. Issue 4, 2001.

Theory and Practice of Econometric Modelling

Using PcGive10

Giovanni Urga

City University Business School,

Department of Investment, Risk Management and Insurance,

Frobisher Crescent, Barbican Centre, London EC2Y 8HB (U.K.).

Tel. +/44/(0)20/7477 8698, Fax. +/44/(0)20/7477 8885,

e-mail: [email protected]

http://www.business.city.ac.uk/irmi/giovanni_urga.html

6 June 2001

Abstract: This review offers a guided tour to PcGive 10 modules for econometrics

analysis of time series (PcGive), limited dependent variable (LogitJD) and static and

dynamic panel data analyses (DPD), financial econometric (GARCH) and time series

(ARFIMA) modelling. Several empirical applications are reported to illustrate the

package.

Keywords: Econometric Modelling, Econometric Software, GiveWin, PcGive.

1. IntroductionVersion 10 is the first really new release of PcGive since its launch for Windows. It is

written in Ox, though it remains a fully interactive menu-driven program, with the

front-end (GiveWin) supporting the various econometric packages. In this review, I

will briefly illustrate the new features of GiveWin (Version 2) and PcGive (Version

10) and provide some guidelines to the documentation available. However, the bulk of

my presentation focuses on the use of PcGive 10 to implement the numerous

econometric techniques now available to undertake sound econometric modelling.

2. GiveWin (Version 2)GiveWin (Version 2), an interactive menu-driven graphics-oriented program, is the

front-end to a series of integrated modules, namely PcGive, PcNaive, PcGets,

STAMP, TSP, X12Arima, of which Ox Professional (OxDebug, OxRun, OxGauss,

OxPack) is the implementation language. GiveWin 2 allows one to load, edit,

transform and save data, and create a wide variety of graphs that can be edited,

amended and saved/exported in various format. This new version, which operates

under Windows 95, 98, ME and NT/2000, has improved graphics capability and

quality, providing almost 50 types of graphs, ranging from time-series plots to cross-plots, ACF, density, 3-D plots; it also provides a workspace window (left window in

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Figure 1) which allows easy navigation between open files and between GiveWin and

the various modules.

The structure of the menu at the top of the screen (reported in Figure 1) allows data

input and output and file handling (file), editing window information (edit), setting

fonts, and keeping and reading from graphs (view), data transformation and creation

of the various types of graphs (tools), launches modules (modules), selecting thewindow focus (window) and access to the contents and index of the help system

(help).

The data can be read from and written to human-readable (ASCII) files and Excel

spreadsheet files (various versions up to the latest), as well as GAUSS and Stata data

files. The primary mode of data storage is a pair of files with extensions .IN7 and

.BN7. The former holds the information contents of the binary file (.BN7) containing

the actual data. For instance, in the workplace window in Figure 1 in the Data Files

list in bold appears the currently selected file Blu220.IN7 containing data from

Banerjee, Lazarova and Urga (2001) that I will use later in this review for illustration

of empirical implementations. Graph files can be stored and retrieved and they appearin Graphics files in the workplace windows. We can create various types of graphs,

and graph files can be saved in encapsulate PostScript (.EPS), PostScript (.PS),

Windows Metafiles (.WMF), enhanced metafiles (.EMF), and GiveWin graphics

(.GWG) of which the last can be read by GiveWin for further editing. Text and results

outputs are stored and shown in the Results of the Text Files of the workplace

window: the text can be edited and used for word processing.

Figure 1: Key Window in GiveWin

The menu command modules in GiveWin allows us to launch the various packages:

Figure 2 shows the full list of modules supported by GiveWin. Note that STAMP and

TSP are not highlighted, meaning that they are not installed/available in my current

version of GiveWin.

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Figure 2: Choices from Modules menu in GiveWin

The GiveWin manual provides an easy introduction to the interactive programme.

Part I provides a brief presentation of the main features of the menu commands and

the way to start to work with it. Part II provides very insightful and easy-to-use

tutorials on graphics, graph editing (chapters 4, 5 and 12), data input and output

(chapters 6 and 13) and transformations (chapter 7). Part III provides a useful

presentation of the statistical formulae (chapter 8) utilised throughout the book, data

files formats (chapter 9) and the syntax of how certain functions can be accessed via

entering commands (chapters 10 and 11)

3. PcGive (Version 10)PcGive, activated from the modules menu option (Figure 2), provides essential tools

for modern econometric modelling (PcGive), the implementation of limited dependent

variable models (LogitJD), dynamic panel data (DPD), volatility models (Garch) and

time series models such as Arfima. Figure 3 illustrates the choices.

Figure 3: Choices from the Package Menu in PcGive

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Figure 4: Choices from the Model Menu in Econometric Modelling

If we select the first option in the package menu, econometric modelling provides the

list of econometric techniques available from single equation methods to multivariate

cointegration, and also some simple cross-section regression as well as more

complicated non-linear models as showed in Figure 4.

In order to illustrate the various options in the menu reported in Figure 4, suppose that

we want to estimate an autoregressive distributed lag model of order one where real

money demand for U.K. (m-puk) is a function of income (yuk) and the interest rate

(iuk)1 :

ttttttt iukciukcyukbyukbpukmaconspukm ++++++= 12111011 )()( (1)

The sample period runs from 1969:4 up to 1996:4. Figure 5 reports the graphs 2 of the

series, scaled by both means and ranges as can be found in the edit/edit graphics

objects/regression,scale in GiveWin.

Further, using the Descriptive statistics option we can calculate a set of descriptive

statistics such as means, standard deviations, correlations, and normality tests for each

variable. In addition, we can compute unit-root tests for each variable by

appropriately selecting the lag structure of the ADF test with the option of including

either only a constant, or trend and constant or seasonals and constant. In our case, weconclude that all variables are I(1), with important implications for a cointegration

analysis between money, income and the interest rate.

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Figure 5: Time-series plot of m-puk, yuk and iuk.

The PcGive output that we get by running (1) using OLS as the estimation method 3

is 4 :

EQ( 1) Modelling m-puk by OLS (using Blu2606.in7)

The estimation sample is: 1970 (3) to 1996 (4)

Coefficient Std.Error t-value t-prob Part.R^2

m-puk_1 1.02754 0.01481 69.4 0.000 0.9797

Constant -0.483938 0.2162 -2.24 0.027 0.0477

yuk 0.364748 0.1131 3.22 0.002 0.0942

yuk_1 -0.331300 0.1123 -2.95 0.004 0.0801

iuk 0.00316221 0.0006299 5.02 0.000 0.2013

iuk_1 -0.00258360 0.0006128 -4.22 0.000 0.1509

sigma 0.0123577 RSS 0.0152711852

R^2 0.99361 F(5,100) = 3110 [0.000]**

log-likelihood 318.39 DW 1.71

no. of observations 106 no. of parameters 6

mean(m-puk) 9.93215 var(m-puk) 0.0225475

The Test menu allows us to evaluate the performance of our model (Figure 6).

Graphic analysis provides actual and fitted values, scaled residuals, cross-plot and

various other residuals graphs.

It is straighforward then to switch (in the test menu) to either Test(it brings up the test

dialog) or Test Summary, that provides the battery of mis-specification tests for serial

correlation, ARCH residuals, normality of the residual distribution, heteroscedasticity

and functional form mis-specification. Note that in the latter case the AR and ARCH

default lag length is automatically selected by PcGive on the basis of the data

frequency and sample size.

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Figure 6: Choices from the Test Menu in Econometric Modelling

In our case, the results reported below signal the presence of non normality in the

residuals and heteroscedasticity:

AR 1-5 test: F(5,95) = 2.2462 [0.0559]

ARCH 1-4 test: F(4,92) = 0.72792 [0.5751]

Normality test: Chi^2(2) = 6.8905 [0.0319]*

hetero test: F(10,89) = 1.9382 [0.0501]

hetero-X test: F(20,79) = 2.2543 [0.0058]**

RESET test: F(1,99) = 3.3692 [0.0694]

In addition, other specification test options such as Exclusion, Linear and General

restrictions plus Omitted variables are available. Dynamic analysis allows one to

calculate static long-run solutions, to evaluate the lag structure and the roots of the lagpolynomials, and test for common factors (COMFAC). Note that in Figure 6 the

option Recursive Graphics is not highlighted because we havent selected recursive

regression: if implemented, we will have available plots of the tscoefficien SE2 , a

series of graphs of recursive residuals, and various Chow tests.

Further, if we select the last 12 observations for forecasts, the output from the

estimation will also contain the following parameter constancy tests:

1-step (ex post) forecast analysis 1994 (1) to 1996 (4)Parameter constancy forecast tests:

Forecast Chi^2(12)= 8.4571 [0.7485]

Chow F(12,91) = 0.44770 [0.9391]

showing that the model does not present any apparent problem of instability. Using

the Forecast option in the Test menu we can investigate further the forecasting

properties of our model by switching, for instance, to h>1 step-ahead forecasts or

dynamic forecasts with quite rich options in terms of calculations of forecast standard

errors and their plots.

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Let us turn to another option in Figure 4, and use Multiple-equation Dynamic

Modelling containing, under Model Settings, unrestricted systems, cointegrated

VARs, simultaneus equations models and constrained simultaneous equations models.

In order to test for the presence of multivariate cointegration amongst m-puk, yuk and

iuk, the first step is to identify an appropriate unrestricted VAR. The nature of the

data set used suggests the choice of a VAR of order four. Once I estimate that system,the evaluation of how well specified it is can be done using the appropriate option in

the Test menu, as shown in Figure 6. If we find that the VAR is not mis-specified

(using either Test/Test or Test/Test Summary) we can then proceed to test for the

presence of cointegration: in this case, we have to select from the test menu Dynamic

analysis and cointegration tests and then the option I(1) cointegration analysis. The

output that we obtain is the following:

I(1) cointegration analysis, 1970 (3) to 1996 (4)

eigenvalue loglik for rank

444.2257 0

0.28452 461.9703 1

0.071026 465.8750 20.029754 467.4759 3

H0:rank

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may need help from economic theory to link the cointegrated vectors to the structural

relationships. Cointegrated VAR option inModel Settings provides the framework to

impose the rank of the cointegration space and cointegration restrictions to uniquely

determine the cointegration vectors and a sound economic interpretation.

Finally, a series of simultaneous equations models may be implemented, and FIML,2SLS and 3SLS and equation-by-equation OLS are available.

New modules: LogitJD, DPD, Garch, Arfima

- There are quite a few new modules attached to this version of PcGive. Following the

order in Figure 3, the Limited Dependent Models (LogitJD) option allows one to

estimate discrete choice models where the dependent variable only takes on two

(integer) values. PcGive implements binary discrete choice, binary logit and probit

models, and multivariate discrete choice models, as well as some count data models.

The documentation available in Volume III of PcGive is nicely integrated and mainly

drawn from Cramer (2001).The output below reports the results from the estimation of the logit binary discrete

model choice model as reported in PcGive III manual. There is evidence that variable

CAR01, a (0,1) dummy variable for the presence of a car in the household, is

influenced by the logarithm of household income per equivalent adult, in Dutch

guilders (LINC):

CS( 1) Modelling CAR01 by Logit

The estimation sample is 1 - 2820

Coefficient Std.Error t-value t-prob

Constant -2.77231 0.8283 -3.35 0.001LINC 0.347582 0.08579 4.05 0.000

log-likelihood -1831.28599 no. of states 2


baseline log-lik -1839.627 Test: Chi^2( 1) 16.681 [0.0000]**

AIC 3666.57197 AIC/T 1.30020283

mean(CAR01) 0.641844 var(CAR01) 0.22988

Newton estimation (eps1=0.0001; eps2=0.005): Strong convergence

Count Frequency Probability loglik

State 0 1010 0.35816 0.35816 -1032.

State 1 1810 0.64184 0.64184 -799.3

Total 2820 1.00000 1.00000 -1831.

- I am very pleased to see that at last there is an official version of a series of routines

originally written in Gauss by Manuel Arellano and Stephen Bond later in the 1980s

and which have been heavily used in implementing (static and) dynamic panel data

econometrics over the last decade. As such, the Panel Data Models (DPD) in PcGive

deals with the standard setup of (balanced and unbalanced in terms of T) a typical

panel, i.e. a panel where the number of T observations is relatively short but the

number of units N is large. PcGive allows one to implement OLS in levels, between

and within group estimators, feasible GLS (and GLS with OLS residuals), maximum

likelihood (procedures also available in other package), one-step instrumental

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variables Anderson-Hsiao method and one-step (robust) and two-step GMM

estimation (note that this procedure was only available in DPD and now in PcGive).

The PcGive output DPD(1) below reports the two-step OLS estimates of a simple

AR(1) model for LI, defined as the logarithm of current gross investment (the data set

is from the tutorial file grunfeld.xls in PcGive). In order to control for fixed effects,the variables are transformed in terms of orthogonal deviations, and a full set of time

dummies is enclosed in the regression to account for factors varying over time but

common to all units:

DPD( 1) Modelling LI by 1 and 2 step (using grunfeld.xls)

---- 2-step estimation using DPD ----

Coefficient Std.Error t-value t-prob

LI(-1) 0.324350 0.05089 6.37 0.000

Constant -0.203228 0.02019 -10.1 0.000

T1938 -0.00254701 0.03257 -0.0782 0.938

T1939 -0.0315257 0.05232 -0.603 0.548

T1940 -0.128736 0.04535 -2.84 0.005

T1941 -0.0708284 0.04308 -1.64 0.102T1942 0.115091 0.04312 2.67 0.008

T1943 0.103626 0.04438 2.33 0.021

T1944 -0.232467 0.08696 -2.67 0.008

T1945 -0.190685 0.08110 -2.35 0.020

T1946 -0.0201863 0.05512 -0.366 0.715

T1947 0.0757306 0.05466 1.39 0.168

T1948 0.174425 0.04515 3.86 0.000

T1949 0.0626937 0.05539 1.13 0.259

T1950 -0.141672 0.04066 -3.48 0.001

T1951 -0.111055 0.04778 -2.32 0.021

T1952 -0.0220058 0.03391 -0.649 0.517

T1953 0.0997002 0.02790 3.57 0.000

T1954 0.156674 0.03355 4.67 0.000

sigma 0.2389418 sigma^2 0.05709316

R^2 0.3685844

RSS 9.1919988765 TSS 14.557763196


Using robust standard errors

Transformation used: orthogonal deviations

constant: yes time dummies: 17

number of individuals 10 (derived from year)

longest time series 18 [1937 - 1954]

shortest time series 18 (balanced panel)

Wald (joint): Chi^2(1) = 40.62 [0.000] **

Wald (time): Chi^2(18) = 644.5 [0.000] **AR(1) test: N(0,1) = -2.573 [0.010] *

AR(2) test: N(0,1) = -1.861 [0.063]

No surprisingly, the autoregressive coefficient and the time dummies are highly

significant even though do not fully explain the dynamics of the investment series. In

addition to other standards statistics, at the bottom of the output above, two Wald tests

(to test for the joint significance of all coefficients and time dummies, respectively)

and two AR tests (to test the null of no serial correlation of first- and second-order,

respectively) are reported.

What is missing in this new implementation of Panel Data Models is the set of

procedures recently proposed in the literature dealing with so-called square-panels,that is panels in which the number of T observations is sufficiently large (with N

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remaining large but not so large as in the typical panel) such that the time-series

properties of the data may be exploitable. This involves the implementation of both

panel data unit roots and cointegration testing procedures. Banerjee (1999) provides a

useful overview.

- The Volatility models (Garch) package allows one to implement a variety of models

in the ARCH and GARCH family. First, we formulate the conditional mean of the

process of the variable under consideration (asset returns, for example) and then we

specify in the Model Settings the ARCH/GARCH structure of the residuals.

To illustrate the estimation of a GARCH(1,1), we use the series of returns

( ))/log(*100 1= ttt ppr of the Argentinean stock index MERVAL )( tp (including 28stocks) from Bellini and Urga (2001). The series runs from 8-Nov-1995 to 8-Nov-

2000, containing 1306 observations, it is plot in Figure 7.

Figure 7: Time-series plot of the Argentinean stock index MERVAL.

Suppose that we estimate the following model:

ttttt yrr ++= 1,1,1,0 , (2)

yt t t= , )1,0(~Nt , (3)

with the specification of volatility, t2

, taking the GARCH(1,1) form

2

11

2

10

2

++= ttt y (4)

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The PcGive output that we get in running (2)-(4) using maximum likelihood

estimation method and having imposed stationarity, 111 =0, alpha(1)+beta(1)

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Figure 8: Choices from the Model Setting Menu in Volatility Models

We can evaluate a GARCH(1,1) model (also recursively if the option recursive is

selected in theEstimate menu) using the extremely rich number of features provided

in Test menu. In equation Vol(1) (and Vol(2) below) I report some mispecification

tests. Further, it is also possible to compare the results from GARCH(1,1) to those

obtained from an EGARCH model or a model where we relax the assumption of anormal distribution: we may choose between a t-distributed error structure and a

generalised error distribution. It is also possible to implement asymmetric and

threshold GARCH, and finally select and test the significance of a conditional

variance in mean (GARCH-M) with the variance entering in various way depending

on the nature of the conditioning and the data. When we choose to enter h_t in mean

(Figure 8) in the conditional mean, the result of our application is

VOL( 2) Modelling DLMERVAL by restricted GARCHM(1,1) (Indexes.xls)

The estimation sample is: 3 to 1306

Coefficient Std.Error robust-SE t-value t-prob

DLMERVAL_1 Y 0.0871627 0.03136 0.03016 2.89 0.004Constant X 0.000309457 0.0008185 0.0008472 0.365 0.715

alpha_0 H 2.38152e-005 6.020e-006 9.131e-006 2.61 0.009

alpha_1 H 0.144507 0.02086 0.03927 3.68 0.000

beta_1 H 0.806041 0.02766 0.04726 17.1 0.000

h_t X 1.81092 13.04 12.44 0.146 0.884

log-likelihood 3327.59219 HMSE 4.83256

mean(h_t) 0.00046351 var(h_t) 2.46011e-007


AIC.T -6643.18438 AIC -5.09446655

mean(DLMERVAL) 4.36431e-005 var(DLMERVAL) 0.000458338

alpha(1)+beta(1) 0.950548 alpha_i+beta_i>=0, alpha(1)+beta(1)

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Used sample mean of squared residuals to start recursion

Robust-SE based on numerical Hessian matrix and numerical OPG matrix

BFGS using numerical derivatives (eps1=0.0001; eps2=0.005):

Strong convergence

Used starting values:

0.058186 3.9899e-005 2.2839e-005 0.80631 0.14369 0.00000

Descriptive statistics for scaled residuals:

Normality test: Chi^2(2) = 198.10 [0.0000]**

ARCH 1-2 test: F(2,1294)= 0.64929 [0.5226]

Portmanteau(36): Chi^2(35)= 44.249 [0.1359]

showing insignificance of the conditional variance in the mean equation.

The GARCH parameter restrictions option allows one to evaluate the stationarity/non

stationarity of the process as well as the non-negativity assumptions on the relevant

parameters.

- The Time Series Models package deals with the fractionally-integrated

autoregressive-moving average model ARFIMA (p,d,q). The empirical

implementation involves identification, estimation and testing of the process. A

crucial step is the identification of the order of differencing, d. The value of d

determines whether the process is stationary (-0.5

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Finally, a few words about the vast documentation available. The documentation of

PcGive is contained in three volumes. Volume I and II comprise univariate and

multivariate dynamic econometric modelling, Volume III covers various other useful

econometric topics and techniques.

In particular, Volume I introduces econometric methods and various empiricalimplementations for cross-section regression (chapter 3), descriptive statistics

(chapter 4) single-equation dynamic modelling (chapters 5-8), and nonlinear

modelling (chapter 9). Part II of the book, which comprises chapters 10-15, presents

the econometrics of PcGive.

Volume II contains tutorial material for more advanced econometric modelling such

as multiple-equation dynamic modelling comprising VAR and cointegration (chapters

3-6, 8), and simultaneous equations analysis (chapter 7). Chapters 9-14 complement

the tutorials of the previous chapters with a sound presentation in a texbook format of

the various topics. My students find the last part of the volume in which the statistical

output of the multiple equation models is reviewed extremely useful. Volume IIIdescribes volatility models (ARCH and GARCH and their variants; chapters 2, 3, 4),

limited dependent variable models (chapters 5 and 6, plus the Cramer (2001) volume),

dynamic panel data models (chapter 7-10), time series ARFIMA models (chapters 11-

13) and X12ARIMA for seasonal adjustment (chapters 14-16).

I have been using the beta version of PcGive10 since October 2000 for my Advanced

Financial Econometrics (AFE) and Advanced Financial Modelling and Forecasting

(AFMF) lectures for the MSc.in Mathematical Trading and Finance course at City

University Business School in London and for my undergraduate lectures in

Econometrics at the Department of Economics of Bergamo University in Italy. Both

samples of students found the program easy to use and the material very useful and

easy to follow: the set up of the three volumes combines quite nicely useful tutorial

chapters, examples and the introduction of the econometric methods and econometric

methodology.

4. Suggestions for Future DevelopmentsAs with most good things, the more you get the more you want. Thus, I look forward

to some further developments, mostly unavailable in other rival packages either: (a)

first, a better handling of data series at high frequency to identify the dates when one

views and graphs daily series. (b) It will be useful to have GMM as an estimationmethod and (c) the state-space models and Kalman filter, even though they are

available in companion packages of the OxMetrics family such as TSP/STAMP. (d) I

would also like to see implemented (in the Descriptive Statistics menu), principal

components analysis, used in finance to test for instance Arbitrage Pricing Theory

(Roll and Ross, 1980, just to mention a seminal paper) and in econometrics to test for

common stochastic trends/cointegration between series (Harris, 1997; Snell, 1999,

and Hall et al., 1999). (e) Panel data unit roots and cointegration testing procedures

will be welcomed by many practitioners in the light of the important developments of

the topic in the last few years, with profound implication for micro- macro-

econometric developments: at the moment there exists a few number of Gauss

routines not always available from authors. (f) There is not a general consensus yet onthe appropriate procedures to test for the presence of structural breaks/changes, but

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this is a topic that cannot remain at the margin of empirical implementations (Bai and

Perron, 1998; Hansen and Johansen, 1999; Hansen, 2000; Banerjee, Lee and Urga,

2001).

It is evident that most of those suggestions cannot be implemented yet given that they

are unsettled research topics. Thus, it may be easy to implement (a)-(d) in PcGive

given the capability of the Ox programming system, but some experimentation for (e)and (f) should first go in an Ox package, and then later, when settled down, in PcGive.

This same route was taken with theArfima and Panel data modules.

5. ConclusionsIn conclusion, PcGive 10 represents a significant step forward in the practice of

econometrics. GiveWin has improved in terms of graphics capability and quality, data

handling and its communication with the various modules. In PcGive, the already

existing modules are better structured (for instance the separation between single

equation and multiple equation estimations has finally vanished) and easier to

implement. In addition, this version contains limited dependent variable models anddynamic panel data, volatility models and time series models such as Arfima, mostly

unavailable in rival packages.

PcGive 10, as earlier versions, has a unique feature that other econometric packages

do not have. It embodies the practice of econometric modelling, theoretically

elaborated over the last twenty years or so by David Hendry and his co-authors. It

provides a proper approach for modelling economic data and model selection

procedures, also available by the end of June 2001 in PcGets, an automatic general-to-

specific econometric model selection program, which represents one of the new

challenges in econometric modelling (Hendry, 2001).

6. Availability and PricingPcGive was released early in May 2001. The single user copy of 1 in a group

consisting of packages (PcGive, Ox and STAMP) plus one set of books and 1 CD

costs 250 for academic and 500 for others. Other combinations for multi-users

(academic and non) plus special introductory offer to the end of August 2001 are

available from the distributor. The software is distributed by Timberlake Consultants

Limited, Unit B3, Broomsleight Business Park, Worsley Bridge Road, London SE26

5BN (U.K.). Tel.+/44/(0)20/86973377, Fax.+/44/(0)20/86973388,

e-mail:[email protected]. Web Sites: http://www.timberlake.co.uk andhttp://www.timberlake-consultancy.com.

Acknowledgements

I wish to thank Jurgen Doornik, David Hendry, Stepana Lazarova, and Colin Roberts

for their helpful comments and suggestions on an earlier draft of this review. The

usual disclaimer applies. Financial support from the UK Economic and Social

Research Council (Project N. R 000 23 8145) is gratefully acknowledged.

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Endnotes

1. Real money demand is expressed as a seasonaly adjusted M0 aggregate, income is measured asGDP (1990 prices) and the interest rate is defined as the three-month Treasury bond rate. For more

details on the data set see Banerjee, Lazarova and Urga (2001).

2. In this review, the graphs are done as screen captures from PcGive: one can see they are in awindow. In general, to get good quality one can cut and paste or, even better, use postscript files.

In addition, one can set the Graphics in GiveWin to black&white before copying (Graphic DisplayMode inEdit menu): this is useful when printing is not colour, and one wishes to avoid gray scales

in the Word output for instance.

3. The Model Settings option in the model menu allows us to switch to an alternative estimationmethod, while the optionEstimate allows us to select also some period for ex-post forecast and the

possible implementation of recursive estimation.

4. In this paper all PcGive outputs are in a (fixed) Courier New font with size 9.

References

Bellini, F. and Urga,G. 2001. Testing for Predictability and Integration in the Latin

American Countries. MTF W.P. 09-01, City University Business School, London.

Bai, J. and Perron,P. 1998. Estimating and Testing Linear Models with Multiple

Structural Changes.Econometrica66: 47-78.

Banerjee, A. 1999. Panel Data Unit Roots and Cointegration: An Overview. OxfordBulletin of Economics and Statistics 61: 607-629.

Banerjee, A., Lazarova, S. and Urga,G. 2001. Bootstrapping sequential tests for

multiple structural breaks. City University Business School, revised version of the

European University Institute Working Papers N. 98/24.

Banerjee, A., Lee, S. and Urga,G. 2001. Detecting Structural Breaks in Time Series: a

Monte Carlo Comparison. Mimeo, IRMI, City University Business School.

Cramer, J.S. 2001. The LOGIT Model: An Introduction for Economists, 2nd

Edition.

London: Timberlake Consultants Press.

Doornik, J.A. and Hendry, D.F. 2001. GiveWin. An Interface to Empirical Modelling.

London: Timberlake Consultants Press.

Doornik, J.A. and Hendry, D.F. 2001. Empirical Econometric Modelling using

PcGive. Volume I. London: Timberlake Consultants Press.

Doornik, J.A. and Hendry, D.F. 2001. Modelling Dynamic Systems using PcGive.

Volume II. London: Timberlake Consultants Press.

Doornik, J.A. and Hendry, D.F. 2001. Econometric Modelling using PcGive. Volume

III. London: Timberlake Consultants Press.

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17

Hall, S., Lazarova, S. and Urga, G. 1999. A Principal Components Analysis of

Common Stochastic Trends in Heterogenous Panel Data: Some Monte Carlo

Evidence. Oxford Bulletin of Economics and Statistics 61: 749-767.

Hansen, B. E. 2000. Testing for Structural Change in Conditional Models. Journal ofEconometrics97: 93-115.

Hansen, H. and Johansen, S. 1999. Some Tests for Parameter Constancy in

Cointegrated VAR-Models.Econometrics Journal2: 306-333.

Harris, D. 1997. Principal Components Analysis in Cointegrated Time Series.

Econometric Theory13: 529-557.

Hendry, D.F. 2001. Achievements and Challenges in Econometric Methodology.

Journal of Econometrics100: 7-10.

Roll, R. and Ross, S. A. 1980. An Empirical Investigation of the Arbitrage Pricing

Theory.Journal of Finance35: 1073-1109.

Snell, A. 1999. Testing for r versus r-1 Cointegrating Vectors. Journal of

Econometrics88: 151-191.

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