Прикладная эконометрика, 2016, № 41

2016, т.41 

ISSN 1993-7601

Журнал «Прикладная эконометрика» включен в список периодических изданий ВАК, 
рекомендованных для публикации результатов диссертационных исследований.  
Он индексирован в международных базах научных журналов по экономике RePEc и EconLit,  
а также в Russian Science Citation Index (RSCI) на платформе Web of Science.

Члены редколлегии

Аистов А. В. — канд. физ.‑мат. наук, Нижегород‑
ский филиал НИУ ВШЭ. 
Бродский Б. Е. — д‑р физ.‑мат. наук, ЦЭМИ РАН.
Ван Суст А.  — Ph. D., Тилбургский университет, 
 Нидерланды.
Вербик М. — Ph. D., Школа менеджмента, Роттер‑
дам, Нидерланды.
Денисова И. А. — Ph. D., Центр экономических 
и финансовых исследований и разработок ( ЦЭФИР); 
ЦЭМИ РАН.
Елисеева И. И. — чл.‑кор. РАН, д‑р экон. наук, 
 Социо логический институт РАН; Санкт‑Петер бург‑
ский университет экономики и финансов.
Ениколопов Р. — Ph. D., Университет Помпеу Фа‑
бра, Барселона, Испания; РЭШ. 
Канторович Г.  Г. — канд. физ.‑мат. наук, НИУ ВШЭ.
Карлеваро Ф. — д‑р наук, Женевский универси‑
тет, Швейцария.
Кумбхакар С. — Ph. D., Университет штата Нью‑
Йорк в Бин гем тоне, США.

Макаров В. Л. — акад. РАН, д‑р физ.‑мат. наук, 
ЦЭМИ РАН; РЭШ.

Максимов А. Г. — канд. физ.‑мат. наук, Нижего‑
родский филиал НИУ ВШЭ.

Микушева А. Е. — Ph. D., канд. физ.‑мат. наук, 
Массачусетский технологический институт, Кэм‑
бридж, США.

Мхитарян В. С. — д‑р экон. наук, НИУ ВШЭ.

Рубин Ю. Б. — д‑р экон. наук, профессор, 
чл.‑кор. РАО, ректор МФПУ «Синергия».

Рудзкис Р. — д‑р наук, Институт математики и ин‑
форматики, Каунасский университет, Литва.

Слуцкин Л. Н. — Ph. D., Институт экономики РАН.

Суслов В. И. — чл.‑кор. РАН, д‑р экон. наук, Инсти‑
тут экономики и организации промышленного про‑
изводства СО РАН.

Харин Ю. С. — чл.‑кор. НАН Беларуси, д‑р физ.‑мат. 
наук, Белорусский государственный университет; 
НИИ прикладных проблем математики и информа‑
тики БГУ, Беларусь.

Главный редактор

Айвазян Сергей Артемьевич — д‑р физ.‑мат. наук, акад. (иностранный член) НАН Армении, Централь‑
ный экономико‑математический институт РАН (ЦЭМИ РАН); Высшая школа экономики (НИУ ВШЭ); 
Московская школа экономики МГУ.

Заместитель главного редактора

Пересецкий Анатолий Абрамович — д‑р экон. наук, НИУ ВШЭ; ЦЭМИ РАН; Российская экономиче ‑ 
с кая школа (РЭШ).

Ответственный секретарь

Сластников Александр Дмитриевич — канд. физ.‑мат. наук, ЦЭМИ РАН.

 2016, vol.41 

ISSN 1993-7601

Applied

CONOMETRICS
E

Applied Econometrics is indexed in RePEc (Research Papers in Economics),  
EconLit (The American Economic Association’s electronic bibliography)  
and RSCI (Russian Science Citation Index) on Web of Science.

Associate Editors

Andrey Aistov — National Research University High‑
er School of Economics (NRU HSE) branch in Nizhny 
Novgorod.

Boris Brodsky — Central Economics and Mathemat‑
ics Institute (CEMI RAS), Moscow.

Fabrizio Carlevaro — University of Geneva, Geneva 
(Switzerland).

Irina Denisova — Centre for Economic and Financial 
Research (CEFIR); Central Economics and Mathemat‑
ics Institute (CEMI RAS), Moscow.

Irina Eliseeva — Sociological Institute (SI RAS); Saint‑
Petersburg State University of Economics and Finance, 
Saint‑Petersburg.

Ruben Enikolopov — Universitat Pompeu Fabra, Bar‑
celona (Spain); New Economic School (NES), Mos‑
cow.

Grigoriy Kantorovich — National Research Universi‑
ty Higher School of Economics (NRU HSE), Moscow.

Yury Kharin — Belarusian State University; Research 
Institute for Applied Problems of Mathematics and In‑
formatics, Minsk (Belarus).

Subal Kumbhakar — State University of New York, 
Binghamton (USA).

Valery Makarov — Central Economics and Mathe‑
matics Institute (CEMI RAS); New Economic School 
(NES), Moscow.

Andrey Maksimov — National Research University 
Higher School of Economics (NRU HSE) branch in 
Nizhny Novgorod.

Anna Mikusheva — Massachusetts Institute of Tech‑
nology, Cambridge (USA).

Vladimir Mkhitarian — National Research Universi‑
ty Higher School of Economics (NRU HSE), Moscow.

Yury Rubin — Sinergia Moscow University of Indus‑
try and Finance, Moscow.

Rimantas Rudzkis — Institute of Mathematics and 
Informatics, Vilnius (Lithuania).

Lev Slutskin — Institute of Economics (IE RAS), 
Moscow.

Arthur van Soest — Tilburg University, Tilburg (Nether ‑
lands).

Viktor Suslov — Institute of Economics and Indu strial 
Engineering of the Siberian Branch of RAS, Novo si‑
birsk.

Marno Verbeek — Rotterdam School of Management, 
Rotterdam (Netherlands).

Editor-in-Chief

Sergey Aivazian — Central Economics and Mathematics Institute (CEMI RAS), Moscow; National Research 
University Higher School of Economics (NRU HSE); Moscow School of Economics (MSE), Moscow.

Vice-Editor

Anatoly Peresetsky — National Research University Higher School of Economics (NRU HSE); Central Econom‑
ics and Mathematics Institute (CEMI RAS); New Economic School (NES), Moscow.

Executive Editor

Alexander Slastnikov — Central Economics and Mathematics Institute (CEMI RAS), Moscow.



3

Applied econometrics / ПРИКЛАДНАЯ ЭКОНОМЕТРИКА

Contents 
Содержание номера

2016, 41

МакроэконоМика

A.Ndoricimpa,N.E.Osoro,A.Kidane
Threshold effects of inflation on economic growth  
in selected African regional economic communities:  
Evidence from a dynamic panel threshold modeling. . . . . . . . . . . . . . . . . . . . . . . 5

регионы

C.А.Айвазян,М.Ю.Афанасьев,А.В.Кудров
Метод кластеризации регионов РФ  
с учетом отраслевой структуры ВРП  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

эконоМика труда

А.В.Ларин,А.Г.Максимов,Д.В.Чернова
Эластичность предложения труда по заработной плате в России .  .  .  .  .  .  .  .  .  .  .  .  .  . 47

О.Н.Кадрева
Влияние количества и возраста детей на заработки работающих женщин . . . . . . . . . 62

E.Kotyrlo
Space‑time dynamics of fertility and commuting  . . . . . . . . . . . . . . . . . . . . . . . . 78

М.Л.Лифшиц
Прогнозирование мировой миграционной ситуации  
на основе анализа нетто‑миграции в странах мира. . . . . . . . . . . . . . . . . . . . . . 96

Финансы

Е.В.Румянцева,К.К.Фурманов
Моделирование времени жизни ипотечного кредита. . . . . . . . . . . . . . . . . . . . 123

Содержание номера 
Contents

ПРИКЛАДНАЯ ЭКОНОМЕТРИКА / Applied econometrics
2016, 41

MacroeconoMics

ArcadeNdoricimpa,NehemiahOsoro,AsmeromKidane
Threshold effects of inflation on economic growth  
in selected African regional economic communities:  
Evidence from a dynamic panel threshold modeling. . . . . . . . . . . . . . . . . . . . . . . 5

regions

SergeiAivazian,MikhailAfanasiev,AleksanderKudrov
Clustering methodology of the Russian Federation regions  
with account of sectoral structure of GRP .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 24

Labour econoMics

AlexanderLarin,AndreyMaksimov,DariaChernova
The elasticity of labor supply in Russia  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

OlgaKadreva
The influence of quantity and age of children on working women’ salaries . . . . . . . . . . . 62

ElenaKotyrlo
Space‑time dynamics of fertility and commuting  . . . . . . . . . . . . . . . . . . . . . . . . 78

MarinaLifshits
Forecasting of the global migration situation based  
on the analysis of net migration in the countries . . . . . . . . . . . . . . . . . . . . . . . . . 96

Finance

EkaterinaRumyantseva,KirillFurmanov
Modeling mortgage survival .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 123

A.Ndoricimpa,N.E.Osoro,A.Kidane

5

Applied econometrics / ПРИКЛАДНАЯ ЭКОНОМЕТРИКА

Macroeconomics 
Макроэкономика

2016, 41

Прикладная эконометрика, 2016, т. 41, с. 5–23.
Applied Econometrics, 2016, v. 41, pp. 5–23.

A. Ndoricimpa, N. E. Osoro, A. Kidane1

Threshold effects of inflation on economic growth 
in selected African regional economic 
communities: Evidence from a dynamic panel 
threshold modeling

The objective of this study is to estimate inflation threshold and examine its impact on the 
inflation-growth nexus in selected African regional economic communities. While a number 
of empirical studies exist in this area for developing countries, they bundle up countries 
from Asia, Africa and Latin America which do not have the same inflation experiences. This 
study therefore focuses on Africa. However, since African regional groupings themselves 
have different inflation experiences, non-linearity in the relationship between inflation and 
growth is explored within each grouping separately. The study uses dynamic panel threshold 
modeling recently suggested by Kremer et al. (2013) which extends the non-dynamic panel 
threshold model of Hansen (1999) and the cross-sectional threshold model of Caner and 
Hansen (2004). The results indicate that the estimated inflation threshold is different across 
the regional economic communities. Nonlinearity in inflation-growth nexus seems to hold in 
CEMAC, COMESA and SADC while it is questioned in WAEMU and WAMZ. For CEMAC, 
COMESA and SADC, the findings indicate that inflation above the threshold is harmful 
to growth. Some correlations are established in this study but further analysis is needed 
to suggest a policy.

Keywords: inflation threshold; growth; dynamic panel threshold regression; Africa.

JeL classification: C23; O40; E31.

1. introduction
W

hile the classic view is that inflation is generally a bad thing since it wastes resources 
(Altig, 2003), the relationship between inflation and growth is traditionally linear; it 
is either positive or negative depending on whether money is a substitute for capital 
(Mundell, 1965; Tobin, 1965) or complementary to capital (Stockman, 1981; Fischer, 1983). The 
latter supports a negative impact of inflation on growth and the former upholding a positive impact. 
In contrast, Fischer (1993) suggests that the relationship between inflation and growth is rather 
non‑linear; the relationship is positive below a certain threshold of inflation, and negative above it.

1 Ndoricimpa Arcade — University of Burundi, Bujumbura, Burundi; arcade_ndoricimpa@yahoo.com.
 
Osoro Nehemiah E. — University of Dar es Salaam, Dar es Salaam, Tanzania; osoro@udsm.ac.tz.
 
Kidane Asmerom — University of Dar es Salaam, Dar es Salaam, Tanzania; akidane@udsm.ac.tz. 

Макроэкономика 
Macroeconomics

ПРИКЛАДНАЯ ЭКОНОМЕТРИКА / Applied econometrics
2016, 41

The primary objective of most central banks is to control inflation by keeping it low so as to 
mitigate or altogether eliminate its direct and indirect adverse effects on the economic activity 
and growth. In addition, economists often say that inflation is harmful to growth when it is too 
high (Friedman, 1977; Fischer, Modigliani, 1978). The question is, how low an inflation level is 
too low or how high is too high? Moreover, African countries in their regional economic com‑
munities have set convergence criteria concerning inflation [COMESA (5%), EAC (5%), SADC 
(3%), CEMAC (3%), WAEMU (3%) and WAMZ (single digit)]2; one would wonder how opti‑
mal these targets are. At which level should monetary authorities in these regions set inflation to 
avoid its adverse effects on growth? This is the question explored in this study. This study seeks 
to explore nonlinearity in the relationship between inflation and growth in five African regional 
economic communities, namely CEMAC, COMESA, SADC, WAEMU and WAMZ. However, 
as Kremer et al. (2013) point out, the established nonlinear relationship between inflation and 
growth does not necessarily reflect causality but rather correlation.
Some studies have tried to explain nonlinearity in the relationship between inflation and eco‑
nomic growth. Using the «adverse selection mechanism» in credit market, Choi et al. (1996) ex‑
plain how inflation affects positively growth unless it exceeds some threshold level. Their idea 
is that in a financial market, there are borrowers and lenders where the financial system plays 
the role of channeling funds from lenders to borrowers. They argue that if inflation increases, it 
discourages the lenders since the real rate of return on assets is reduced and has an effect of re‑
ducing funds available for lending. At the same time, the rise in inflation encourages the borrow‑
ers and there will be more people wanting to borrow, among them new borrowers who are just 
profiting the situation (who were not initially interested in borrowing), and have therefore higher 
default risk. This creates the problem of adverse selection for financial institutions called credit 
market rationing, since banks will not provide credits for new borrowers who have higher de‑
fault, hence fewer loans are given. Consequently, an increase in inflation causes lower economic 
growth. However, when inflation is low, Choi et al. (1996) claim that an increase in inflation will 
not lead to adverse selection mechanism but instead Mundell–Tobin effect will take place caus‑
ing substitution between money and capital. Economic growth will therefore be enhanced. The 
bottom line is that for low levels of inflation, the model shows that inflation promotes growth but 
for high levels of inflation, inflation is detrimental to growth because of credit rationing.
Examining the impact of inflation threshold on economic growth has been an area of consider‑
able research using different methodologies in the past decades. Sarel (1996), using panel data on 
87 developed and developing countries finds a threshold level of inflation at 8%. Ghosh and Phillips 
(1998) on a larger sample than the one used by Sarel (1996), find a threshold level of inflation of 
2.5% while Khan and Senhadji (2001) find a threshold inflation of 1% for industrial countries and 
11% for developing countries. Drukker et al. (2005), using a panel data model estimated a threshold 
inflation at 19.16% for non‑industrialized countries and two threshold points at 2.57% and 12.61% 
for industrial countries. Bick (2010), on a balanced panel of 40 developing countries using non‑
dynamic panel threshold regression of Hansen (1999) finds a threshold inflation of 19.16% with 
no regime intercepts and 12.03% by allowing regime intercepts. Most recently, Ibarra and Trupkin 
(2011) using Panel Smooth Transition Regression find a threshold inflation of 4.1% for industrial 
countries and 19.1% for non‑industrial countries. In both groups of countries, the impact of infla‑
tion and growth is negative in both inflation regimes but statistically significant only in high infla‑

2 The description of these regional economic communities are in Table A1 (in Appendix).

A.Ndoricimpa,N.E.Osoro,A.Kidane

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Macroeconomics 
Макроэкономика

2016, 41

tion‑regime (when inflation is above the threshold). Seleteng et al. (2013) also using Panel Smooth 
Transition Regression on SADC countries found a threshold inflation at 18.9%. The effect of in‑
flation is negative in both regimes but only statistically significant above the inflation threshold.
It is important to note that most of the panel studies in this area use either the non‑dynam‑
ic panel threshold regression of Hansen (1999) or non‑dynamic Panel Smooth Transition Re‑
gression (PSTR) of Gonzalez et al. (2005). However, Kremer et al. (2013) argue that the ex‑
isting studies using panel data on the threshold effects of inflation on growth might have some 
shortcomings since initial income as an important variable in growth models is either not in‑
cluded among the control variables or when included the endogeneity problem it causes is not 
taken into account (see for instance, (Khan, Senhadji, 2001; Drukker et al., 2005; Bick, 2010; 
Seleteng et al., 2013)), a result of which can be misleading in the threshold estimation. Kremer 
et al. (2013) therefore propose a methodology, dynamic panel threshold regression, which ad‑
dresses that potential problem by building on Hansen (1999), Caner and Hansen (2004). Apply‑
ing dynamic panel threshold regression in analyzing the threshold inflation effect on growth, 
their findings reveal a threshold inflation of 2.53% for industrial countries and 17.22% for non‑
industrial countries. For industrial countries, the relationship is significantly positive below the 
threshold and significantly negative above the threshold. However for non‑industrial countries, 
the relationship is negative in both regimes but statistically significant only above the threshold.
This study follows Kremer et al. (2013) and adopts dynamic panel threshold regression in 
estimating the threshold level of inflation and analyzing its impact on inflation‑growth nexus in 
Africa. While the existing studies on this topic on developing countries combine countries from 
Asia, Africa, Latin America (see for instance (Khan, Senhadji, 2001; Drukker et al., 2005; Bick 
2010; Ibarra, Trupkin, 2011; Kremer et al., 2013)), this study focuses only on Africa. In fact, 
the bundling up of countries which do not have the same inflation experiences can be mislead‑
ing when estimating the inflation threshold. In addition, as Table A2 (in Appendix) shows, Afri‑
can regional groupings within themselves have different inflation experiences with  COMESA, 
SADC and WAMZ exhibiting highest average inflation rates as compared to CEMAC and 
 WAEMU which experienced relatively lower inflation rates. With such disparities in the levels 
of inflation across those groupings, this study explores nonlinearity in the relationship between 
inflation and growth within each economic grouping separately. In addition, while some previous 
studies determine exogenously the level of inflation threshold (see for instance (Fischer, 1993; 
Bruno, Easterly, 1998)), the level of inflation threshold is endogenously determined in this study.
The rest of the paper is organized as follows. Section 2 gives some background information 
on inflation, growth and economic development in the selected African regional economic com‑
munities. Section 3 highlights the methodology and data used. Section 4 presents the empirical 
results and discussion. Section 5 makes comparison with other previous studies’ findings and 
section 6 gives the concluding remarks.

2. inflation, growth and economic development  
in african regional economic communities

In the recent years, the degree of economic integration in Africa has increased; however coun‑
tries within the regional economic communities (i. e. CEMAC, COMESA, SADC,  WAEMU, 
WAMZ, etc.) are still divergent in terms of the level of income per capita, inflation rate, eco‑

Макроэкономика 
Macroeconomics

ПРИКЛАДНАЯ ЭКОНОМЕТРИКА / Applied econometrics
2016, 41

nomic growth, etc. As Table A2 indicates, inflation experiences have been different across the 
five regional economic communities considered, with CEMAC and WAEMU experiencing lower 
inflation rates than COMESA, SADC and WAMZ. Over the sample period, the average inflation 
rate for CEMAC is 3.3% with the lowest average inflation rate observed in Central African Re‑
public (3.2%) while the highest was recorded in Cameroon (6.3%). For WAEMU, the average 
inflation is 5.3%, with the highest average inflation found in Guinea Bissau (13.3%) and the low‑
est in Niger (– 0.002%). In fact, apart from Guinea Bissau, the rest of the countries in  WAEMU 
have an average inflation around 1% while Mali and Niger have negative inflation rates. For 
COMESA, the average inflation rate is 19.6%, the highest average is found in Democratic Re‑
public of Congo (85.3%), followed by Sudan and Uganda with average inflation rate respec‑
tively of 27.2 and 25.1%, and while the lowest average inflation is found in Libya (3.3%). For 
SADC, the average inflation rate is 41.8%, with the highest rate recorded in Angola (313.4%), 
while the lowest is in Seychelles (6.9%). For WAMZ, the average inflation rate was 13.8% with 
the highest inflation found in Ghana (24.1%) and the lowest in Guinea (1.7%).
Differences in inflation across these economic communities can be explained by the differ‑
ence in monetary policy regimes pursued. Countries in CEMAC and WAEMU for example be‑
long to the CFA3 zone with a common currency, CFA franc, which is pegged to the Euro (In‑
ternational Monetary Fund, 2013). Monetary policy in these regions, CEMAC and WAEMU, 
is therefore conducted by the regional respective central banks (Banque des États de l’Afrique 
Centrale, BEAC, and Banque Centrale des États de l’Afrique de l’Ouest, BCEAO) with a fixed 
exchange regime in order to keep inflation low (International Monetary Fund, 2005, 2009). For 
COMESA, SADC and WAMZ, apart from Ghana4 and South Africa5, which are pursuing an in‑
flation‑targeting regime, the rest of the countries are following a monetary targeting regime by 
managing a floating exchange rate.
Similarly, growth experiences have also been different across these communities. Over the 
sample period, growth of real per capita GDP varied from one community to the other with 
an average growth rate of 2.5% for CEMAC, 0.9% for COMESA, 1.5% for SADC, 0.4% for 
WAEMU and 0.1% for WAMZ. The highest mean growth of per capita GDP in CEMAC was 
recorded in Equatorial Guinea (12.2%) and the lowest in Central African Republic (– 1.2%). For 
COMESA, the highest mean growth is found in Mauritius (4.1%) while the lowest is found in 
Democratic Republic of Congo (– 3.0%). The highest mean growth in SADC is found in Bot‑
swana (4.7%) and the lowest in Democratic Republic of Congo (– 3.0%). In WAEMU, Mali re‑
corded the highest average growth (2.3%) and Niger the lowest (– 1.4%). For WAMZ, Nigeria is 
leading with an average growth rate of 1.4% while Liberia has the lowest growth rate (– 2.6%).
Similarly, the level of economic development is also quite different across the five regional 
economic communities as shown by the level of real GDP per capita (see Table A5 in Appen‑
dix). For the period 1980–2011, CEMAC has the highest average real GDP per capita (USD 
2776.7), followed by SADC (USD 2281.4) and COMESA (USD 1817.2). WAMZ has the low‑
est real GDP per capita (USD 445.6). Within each regional community, the level of develop‑
ment seems to be diverse across countries. For CEMAC for instance, the level of real GDP per 

3 CFA stands for Communauté Financière Africaine (African Financial Community) in West African CFA zone and 
Coopération financière en Afrique centrale (Financial Cooperation in Central Africa) in Central African CFA zone. 

4 Ghana adopted inflation‑targeting framework for its monetary policy in May 2007.

5 Inflation‑targeting framework in South Africa started in February 2000.

A.Ndoricimpa,N.E.Osoro,A.Kidane

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2016, 41

capita in Gabon is USD 7755.3 (the highest in CEMAC) while for Central African Republic, it 
is only USD 405.3 (the lowest in CEMAC). The same observation can be made for COMESA 
and SADC where Seychelles has a real GDP per capita of USD 9980.9 while Ethiopia, Burundi 
and Democratic Republic of Congo have USD 157.5, USD 181.9 and USD 208.7 respective‑
ly. In WAEMU and WAMZ however, although the levels of real GDP per capital are not the 
same across countries, they are at least comparable. According to Thanh (2015), differences 
in initial output conditional characteristics may have an impact on the relation between infla‑
tion and growth.
Figure 2 presents the panel series for growth, inflation6 and initial income7 for CEMAC, 
COMESA, SADC, WAEMU and WAMZ. There seems to be no trend in the panel series, but 
fluctuations are observed especially for growth and inflation. The link between these variables 
is not easy to detect but for some observations, one can see that an increase in inflation is asso‑
ciated with an increase in growth while for some other observations, an increase in inflation is 
associated with a decrease in growth. Similar observation can be made for the relationship be‑
tween initial income and growth.

3. Methodology and data

Dynamic Panel Threshold Regression initiated by Kremer et al. (2013) is adopted in this pa‑
per to estimate the inflation threshold level and to examine its impact on the relationship between 
inflation and long‑run growth in African regional economic communities.
In order to examine the impact of the inflation threshold on the relationship between inflation 
and economic growth (growth rate of Real GDP per capita) by taking into account some control 
variables including initial income (lagged Real GDP per capita) which is an endogenous vari‑
able, we use the Dynamic Panel Threshold Model initiated by Kremer et al. (2013) which is an 
extension of the Non‑Dynamic Panel Threshold Model of Hansen (1999) and the cross‑sectional 
threshold model of Caner and Hansen (2004).
The Panel Threshold Model estimated is written as follows:

 
1
2
I(
)
I(
)
it
i
it
it
it
it
it
y
z
q
z
q


=   b
 g  b
 g e , 
(1)

where 
1,...,
i
N
=
; 
1,...,
t
T
=
; 
i  are country individual effects; 
ity  is the dependent variable; 
it
q  
is the threshold variable; g  is the common threshold value; I(·) is the indicator function; 
itz  is a 
vector of the control variables including exogenous variables 
1it
z  which are uncorrelated with the 
error term 
ite , and endogenous variables 
2it
z , correlated with the error term 
ite . The error term 
ite  
is identically and independently distributed, that is
2
(0,
)

ite
s
. In estimating the model (1), instru‑
mental variables 
itx  (including 
1it
z ) are needed in GMM estimation. In this dynamic model, the 
individual effects are eliminated using the forward orthogonal deviations transformation suggested 
by Arellano and Bover (1995) which ensures that the error terms are not autocorrelated and that 
the cross‑sectional threshold model of Caner and Hansen (2004) is applied to the dynamic panel 
model. Basically the procedure of estimation goes as follows. 

6 This is a semi‑log transformed inflation. 

7 Initial income is here captured by the logarithm of the lagged real GDP per capita. 

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ПРИКЛАДНАЯ ЭКОНОМЕТРИКА / Applied econometrics
2016, 41

First, the endogenous variable 
2it
z
 is estimated as a function of instruments 
itx  and the pre‑
dicted value of 
2ˆ it
z
 is obtained. Second, equation (1) is estimated using OLS by substituting 

2it
z
 with the predicted value 
2ˆ it
z
 from the first regression. The residual sum of squares derived 
from this equation is noted as 
( )
S g , where g  is the common threshold value to be estimated. 
The estimated optimal threshold value ˆg  is such that the residual sum of squares is minimum: 
ˆ
argmin
( )
n
S

g
g=
g . Third, after getting the estimated threshold value ˆg, the regression slope co‑
efficients are obtained by GMM using the instruments and the estimated threshold ˆg.
Applying Dynamic Panel Threshold Model in equation (1) to the analysis of the impact of 
inflation threshold on long‑run economic growth gives the following threshold model:

 
1
1
2
I(
)
I(
)
I(
)
it
i
it
it
it
it
it
it
it
gpcgdp
z
=   b p
p  g d
p  g  b p
p  g a
e





, 
(2)

where 
i  are country individual effects, 
it
gpcgdp  (growth rate of real GPD per capita) is the de‑
pendent variable,  it
p  (inflation) is the threshold variable and regime‑dependent regressor, 
itz is a 
vector of the regime‑independent regressors containing the endogenous variable, 
2it
z
 [initial in‑
come captured by lagged real GDP per capita 
1
it
pcgdp  ] and exogenous variables, 
1it
z  and 
1d  
is the regime intercept common to all cross‑sections. According to (Bick, 2010), estimating the 
threshold model without including the regime intercept if it is present in the data generating pro‑
cess can lead to a bias proportional to 
1d  since orthogonality of the regressors is not preserved 
anymore. 
1b  gives the marginal impact of inflation on long‑run growth when inflation is below 
the threshold and 
2
b  presents the marginal impact of inflation on long‑run growth when inflation 
is above the threshold. Since the regression slope coefficients are obtained using GMM estimation, 
as in (Arellano, Bover, 1995), the lags of the dependent variable 
2
3
,
,...,
it
it
it
p
pcgdp
pcgdp
pcgdp




are used as instruments.

The analysis in this study is based on unbalanced panels of African regional economic com‑
munities, CEMAC, COMESA, SADC, WAEMU and WAMZ for different periods depending on 
data availability. Sample of countries for each grouping and periods considered are in Table A3 
of the Appendix. Following (Khan, Senhadji, 2001; Drukker et al., 2005; Ibarra, Trupkin, 2011; 
Kremer et al., 2013), also as cited in a number of empirical growth literature, we use five‑year 
averages of the data. According to (Khan, Senhadji, 2001), using five‑year averages of data has 
an advantage because it helps to smooth out business cycle fluctuations and hence focus on the 
medium and long‑term relationship between inflation and growth. With five‑year averaged data, 
time dimension for each country of the sample is reported in Table A4 of the Appendix along 
with average inflation and average growth.
In examining the threshold inflation effect on economic growth, for purposes of comparison 
with other previous studies, we use as control variables population growth rate, investment‑
GDP ratio, initial income level measured as GDP per capita from the previous period, open‑
ness to trade measured as the ratio of the sum of exports and imports to GDP, the growth rate of 
the terms of trade measured by the ratio of exports over imports, the standard deviation of the 
terms of trade and the standard deviation of openness. The list and definition of variables used 
are in Table 1.
In order to make the distribution of the five‑year average of inflation much more symmetric, 
the following semi‑log transformation (since log transformation is not possible for negative in‑
flation rates) of inflation (see equation (3)) is used as in (Khan, Senhadji, 2001; Drukker et al., 

A.Ndoricimpa,N.E.Osoro,A.Kidane

11

Applied econometrics / ПРИКЛАДНАЯ ЭКОНОМЕТРИКА

Macroeconomics 
Макроэкономика

2016, 41

2005; Kremer et al., 2013). Indeed as Figure 1 shows, apart from CEMAC, the distribution of 
the five‑year average of inflation before semi‑log transformation is highly skewed while the 
semi‑log transformed inflation is much more symmetric.

Table 1. List and definition of variables

gpcgdp
Five‑year average of annual growth rate of Real GPD per capita in constant 2005 prices

p
Five‑year average of semi‑log transformed inflation (annual percentage change of the CPI Index) 

initial
Five‑year average of one period‑lagged real GDP per capita in 2005 constant prices

popgr
Five‑year average of annual growth rate of population

inv
Five‑year average of the investment GDP ratio (percentage of GDP) 

tot
Five‑year average of annual percentage change in the terms of trade (terms of trade measured 
by exports divided by imports) 

open
Five‑year average of log of openness, where openness is measured by the GDP ratio of the sum 
of exports and imports

stdtot
Five‑year standard deviation of the terms of trade, capturing the variability in the terms of trade

stdopen
Five‑year standard deviation of openness

In addition, according to (Ghosh, Phillips, 1998), the semi‑log transformation helps to avoid 
that regression results are distorted by a few extreme inflation observations.

 

1, if
1,

ln(
), if
1.

it
it

it
it

p 
p 

p=
p
p 


 
(3)

Annual data for the variables used were collected from different sources; growth rate of real 
GDP per capita, growth rate of population, real GDP per capita, exports and imports are from 
the UNCTAD online database, investment share of GDP is from Penn World Tables and infla‑
tion rates are from International Monetary Fund (IMF), online database.

4. empirical results and discussion

The estimation results are presented in Tables 2, 3, 4, 5 and 6 respectively for CEMAC, 
COMESA, SADC, WAEMU and WAMZ. All estimation results are from Matlab software, ver‑
sion 2011b, using a MATLAB code written by Kremer, Bick and Nautz (2013)8. The results 
presented give the estimated threshold value, the confidence interval of the estimated thresh‑
old, as well as the impact of inflation below and above the estimated threshold, and the impact 
of the control variables included in the regression which are regime‑independent. The estima‑
tion results for CEMAC in Table 2 suggest that the estimated threshold inflation for CEMAC is 
1.38% and the 95% confidence interval is [0.46, 1.61], which does not contain the 3% inflation 
rate set as a convergence criterion by the members countries.

8 Much appreciation to Kremer, Bick and Nautz for making their Matlab code for dynamic panel threshold regression 
available. 

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Table 2. Inflation threshold effect on economic growth for CEMAC

Estimated inflation threshold

g
1.38%

95% confidence interval
[0.46, 1.61] 

Impact of regime-dependent regressors

Inflation
Estimated coefficients
Standard errors

b1
0.340
0.904

b2
– 1.146**
0.485

Impact of regime-independent regressors

Estimated coefficients
Standard errors

initialit
– 18.384**
8.003

popgrit
–0.111
0.548

invit
0.629**
0.162

totit
–0.119
0.08

stdtotit
15.431
10.396

openit
–1.240
4.079

stdopenit
–20.657
15.894

d1
– 4.748*
2.509

Notes: *, ** indicate significance at 10 and 5% respectively. Number of observations in the low‑inflation regime is 29, 
and 19 in the high‑inflation regime.

The findings indicate that in CEMAC an inflation rate of more than 1.61% is already high. 
The results further suggest that with an inflation rate below 1.38%, the effect on growth is posi‑
tive 

1
0.340
b =
 but statistically not significant and when inflation is above 1.38%, the impact of 
inflation on long‑run growth is negative and statistically significant at 5%. The coefficient of in‑
flation for high inflation regime is 

2
1.146
b =
, indicating that a 1% increase of inflation above 
the inflation threshold leads to a decrease in long‑run growth by 1.146%. The coefficients of the 
control variables (regime‑independent variables) for CEMAC show that the variables such as 
initial income and investment ratio affect long‑run growth at 5% level and their sign coefficients 
are as expected. The regime intercept is also statistically significant at 10% level.
For COMESA, the results presented in Table 3 indicate that the estimated threshold infla‑
tion is 13.13% with a 95% confidence interval of [3.07, 13.23] and contains the 5% inflation 
target set as a convergence criteria set by country members. In both regimes (below and above 
the threshold inflation), the impact of inflation on long‑run growth is negative but only statisti‑
cally significant (at 5% level) when inflation is above 13.13%. The coefficient of inflation for 
high inflation regime is 

2
3.389
b =
, indicating that a 1% increase of inflation above the infla‑
tion threshold leads to a decrease in long‑run growth by 3.389%.
Among the control variables included in the equation, only the coefficient of the standard 
deviation of terms of trade (capturing the variability of terms of trade) is statistically signifi‑
cant at 5% and bears the expected sign (negative), indicating that the variability of the terms of 
trade negatively affects long‑run growth in COMESA. The regime intercept is also statistically 
significant at 5%.