Economic Growth Features in Developing Countries: the case of the Kyrgyz Republic

117 p.Following the idea that economic growth differs from country to country, the thesis is addressed to contribute to the understanding of economic growth features in the Kyrgyz Republic from different perspectives. Thus, the first chapter focuses on the role of agricultural development and assesses the possibility to implement an agro-based cluster in the country. The second chapter applies the growth diagnostics methodology to identify country-specific constraints to growth; and the third chapter, based on the considerations of new institutional economics, is addressed to analyze the performance of central bank by applying the Taylor rule. Our findings, in the broader context, signal about the institutional weakness of the country. Thus, the results of the first chapter indicate the importance of the market-regulating institutions as for the successful agro-cluster implementation; the growth diagnostics approach identifies the weakness of the market-creating institutions as the most binding obstacle for economic growth in the country; lastly, the last part emphasizes the essential role of the market-stabilizing institutions and the role of central bank in particular

Central Banks’ Dual Role Dilemma And Monetary Policy Constraints: Reserve-Constrained Economy Perspective

The past three decades have witnessed an increasing importance and an enormous transformation in the reserve management function of central banks in reserve-constrained economies. Traditionally, central bank reserve management tended to be characterized by a simple custodial mandate of liquidity maintenance required for supporting exchange rate policy, meeting public imports, and servicing foreign debts of the governments. Over time, however, the reserve management function of central banks moved beyond the sole liquidity mandate, to include a portfolio management role with a strong emphasis on generating returns on reserves. This transformation has generated a sharp trade-off between the liquidity and portfolio management roles, creating what we call a “dual role dilemma” in the reserve management function of central banks. This thesis explores the underlying forces behind this novel dual role dilemma in central banks of reserve-constrained economies, and some of the key factors that exacerbate the dilemma. The thesis also examines the implications of this dilemma for monetary policy, specifically for reserve-constrained economies which have adopted inflation targeting regime. The thesis begins laying an excellent analytical framework for analyzing and understanding the forces underlying the dual role dilemma. It joins together the perspective of the international monetary asymmetry (anchored on the US dollar) and the global productive structure hierarchy (with reserve-constrained economies heavily rely on primary commodities). It then, provides empirical evidence of the existence of this dilemma based on a detailed qualitative study, combining insights from semi-structured interviews and focus group discussions with central bankers in three Sub-Saharan African countries: Botswana, Uganda and Zambia. This empirical evidence allows us to trace the historical roots and institutional characteristics that create and exacerbate the dual role dilemma in reserve-constrained economies. The thesis’ main results show that the dilemma has its roots in four adverse conditions faced by reserved-constrained economies: (a) their excessively volatile exchange rates and acute loss of monetary policy, (b) their chronically high external obligations, (c) their scarcity of reserves associated with the heavy reliance on primary commodities which faced extreme climatic vulnerability and price volatility, and (iv) the severe erosion of their reserve capital arising from ultra-low yields on reserve assets. Moreover, institutional characteristics, particularly weak fiscal policies, and financial fragilities are shown to exacerbate the dilemma in these countries. Having provided theoretical and empirical support for the existence of the dual role dilemma faced by central banks in reserve-constrained economies, the thesis then proceeds to examine its monetary policy implications from both a theoretical and empirical perspectives. Theoretically, an innovative model is developed that integrates the reserve management objective function (which captures both the liquidity and portfolio management roles) into a monetary policy rule. This model is tested empirically in macro panel econometric approaches using database on 13 Sub-Saharan African countries over the period 2000-2019. This empirical analysis controls for the effects of climatic vulnerability, exchange rate volatility and external forces. The main result from the empirical estimation shows that the presence of the dual role dilemma, reinforced by extreme climatic and external vulnerability exerts severe constraints for monetary policy operations. Overall, this thesis makes important contributions to the literature of reserve management and monetary policy in developing countries. In contrast to the existing academic literature, which only considers the custodial mandate of central banking management function, this thesis shows that central banks in reserve-constrained economies are forced to accept both the custodial and portfolio management roles as integral parts of their functions. In this dual mandate, central banks engage in portfolio decisions in an effort to balance liquidity and return objectives required for the growth and preservation of reserves capital to enable their reserve-constrained economies meet the ever-growing external obligations. The thesis further shows that this dual role dilemma, which is located in the deep hierarchies in the world’s international monetary system and its productive structure, reinforced by climatic vulnerability creates serious constraints for monetary policy. This thesis concludes by considering several policy conclusions that help to address the reserve scarcity dilemma, including the following. First, productivity-enhancing strategies that permit reserve-constrained economies to climb up the hierarchy of the global productive structure. Second, reducing dependence on the US dollars or currency at the center of the international monetary system. Third, designing a coherent and stable macroeconomic policy including appropriate monetary policy designs and exchange rate policies rooted in the structural subordinated position of SSA at the bottom of the hierarchies in the world’s international monetary system and its productive structure. Fourth, improvement in institutional arrangements surrounding external debt accumulation and its management, mainly by fiscal authorities, including eliminating corruption and lack of transparency and accountability in fiscal policy. Finally, coherent national strategies for combating climate change must be taken into consideration by both monetary and fiscal policy for Sub Saharan African (SSA) countries

Economic Growth Features in Developing Countries: the case of the Kyrgyz Republic

117 p.Following the idea that economic growth differs from country to country, the thesis is addressed to contribute to the understanding of economic growth features in the Kyrgyz Republic from different perspectives. Thus, the first chapter focuses on the role of agricultural development and assesses the possibility to implement an agro-based cluster in the country. The second chapter applies the growth diagnostics methodology to identify country-specific constraints to growth; and the third chapter, based on the considerations of new institutional economics, is addressed to analyze the performance of central bank by applying the Taylor rule. Our findings, in the broader context, signal about the institutional weakness of the country. Thus, the results of the first chapter indicate the importance of the market-regulating institutions as for the successful agro-cluster implementation; the growth diagnostics approach identifies the weakness of the market-creating institutions as the most binding obstacle for economic growth in the country; lastly, the last part emphasizes the essential role of the market-stabilizing institutions and the role of central bank in particular

Identifikacija strukture neuronske mreže u mjerenju očekivane inflacije

Neuronske mreže (NM) je prikladno koristiti u analizi vremenskih nizova u uvjetima narušenih pretpostavki, tj. u uvjetima „nenormalnosti“ i nelinearnosti. Svrha rada je istražiti nedostatke NM te predložiti načine kojima se ti nedostaci mogu otkloniti s ciljem identifikacije strukture NM koja će se najbolje prilagoditi inflacijskim očekivanjima. Polazi se od teorijskog modela koji uključuje karakteristike inflacije potražnje i inflacije troškova. Pri tom se koriste varijable tržišta rada, financijske varijable, eksterni faktori te inflacija iz prethodnog razdoblja. Istraživanje se provodi na agregiranoj razini zemalja euro zone u periodu od siječnja 1999. do siječnja 2017. godine. Na temelju procijenjenih 90 jednosmjernih NM i 450 Jordanovih NM, koje se razlikuju u promjenjivim parametrima (broj iteracija, stopa učenja, interval početnih vrijednosti težina, broj skrivenih neurona, vrijednost težine kontekstne jedinice), dobiveni su pokazatelji prikladnosti za svaku NM (prosječna kvadratna pogreška - MSE i Akaikeov informacijski kriterij - AIC) koji se odnose na dva perioda: „unutar uzorka“ (engl. in the sample) i „izvan uzorka“ (engl. out of sample). Istovremeno rangiranje NM na poduzorcima „in-the-sample“ i „out-of-sample“ bilo prema MSE ili prema AIC ne dovodi do podudarnosti rangova i odabira prikladne NM jer najbolja NM na poduzorku „in-the-sample“, na temelju kriterija MSE i/ili AIC, često daje na poduzorku „out-of-sample“ visoke vrijednosti oba pokazatelja prikladnosti, i obrnuto. Stoga, da bi se došlo do najboljeg kompromisnog rješenja koristi se PROMETHEE metoda za odabir prikladne NM. Usporedbom „najbolje“ jednosmjerne – FNN(4,5,1) i „najbolje“ Jordanove NM – JNN(4,3,1) zaključuje se da je u približno jednakim uvjetima potrebno manje neurona u skrivenom sloju Jordanove NM (3) u odnosu na jednosmjernu (5), čime se potvrđuje da je Jordanova NM parsimonijska u odnosu na jednosmjernu, uz ne postojanje problema predeterminiranosti modela. Na taj način se dokazuje prva pomoćna hipoteza. Također, odabrana Jordanova NM ima bolju prediktivnu sposobnost u odnosu na jednosmjernu, čime se potvrđuje i druga pomoćna hipoteza istraživanja. Konačno se analizira konvergencija kamatnih stopa, uključivanjem inflatornih očekivanja procijenjenih Jordanovom NM u preformulirani oblik Taylorovog pravila.Neural networks (NNs) are appropriate to use in time series analysis under conditions of the unfulfilled assumptions, i.e. under conditions of non-normality and nonlinearity. The purpose of the paper is to investigate the disadvantaged of NNs and to propose ways to address these shortcomings with the aim of identifying the NN structure that will best adapt to inflation expectations. The research is based on a theoretical model that includes the characteristics of demand-pull and cost-push inflation, i.e. it uses labour market variable, financial variable, external factor and lagged inflation. The research is conducted at the aggregate level of euro area countries in the period from January 1999 to January 2017. Based on the estimated 90 feedforward NNs and 450 Jordan NNs, which differ in variable parameters (number of iterations, learning rate, initial weight value intervals, number of hidden neurons, weight value of the context unit), the model adequacy indicators for each NN (Mean Squared Error - MSE and Akaike Information Criterion - AIC) are calculated for two periods: “in-the-sample” and “out of sample”. Simultaneously ranking NNs on the “in-the-sample” and “out of sample” subsamples either according to MSE or AIC does not lead to the matching of rankings and the selection of a suitable NN because the “best” NN in the “in-the-sample”, based on MSE and/or AIC criteria, often has high “out of sample” values of both indicators, and vice versa. Therefore, in order to achieve the best compromise solution, a PROMETHEE method is used to select a suitable NN. By comparing the “best” feedforward NN - FNN(4,5,1) and the “best” Jordan NN - JNN(4,3,1) it is concluded that under approximately equal conditions less neurons in the hidden layer of Jordan NN (3) is required than in feedforward NN (5), confirming that Jordan NN is parsimonious compared to feedforward, without the existence of the overfitting problem. Thus, the first auxiliary hypothesis is proved. Moreover, the selected Jordan NN has a better predictive ability than the feedforward, which confirms second auxiliary hypothesis of the research. Finally, interest rate convergence is analysed, incorporating inflation expectations estimated by Jordan NN into the reformulated Taylor rule

Identifikacija strukture neuronske mreže u mjerenju očekivane inflacije

Neuronske mreže (NM) je prikladno koristiti u analizi vremenskih nizova u uvjetima narušenih pretpostavki, tj. u uvjetima „nenormalnosti“ i nelinearnosti. Svrha rada je istražiti nedostatke NM te predložiti načine kojima se ti nedostaci mogu otkloniti s ciljem identifikacije strukture NM koja će se najbolje prilagoditi inflacijskim očekivanjima. Polazi se od teorijskog modela koji uključuje karakteristike inflacije potražnje i inflacije troškova. Pri tom se koriste varijable tržišta rada, financijske varijable, eksterni faktori te inflacija iz prethodnog razdoblja. Istraživanje se provodi na agregiranoj razini zemalja euro zone u periodu od siječnja 1999. do siječnja 2017. godine. Na temelju procijenjenih 90 jednosmjernih NM i 450 Jordanovih NM, koje se razlikuju u promjenjivim parametrima (broj iteracija, stopa učenja, interval početnih vrijednosti težina, broj skrivenih neurona, vrijednost težine kontekstne jedinice), dobiveni su pokazatelji prikladnosti za svaku NM (prosječna kvadratna pogreška - MSE i Akaikeov informacijski kriterij - AIC) koji se odnose na dva perioda: „unutar uzorka“ (engl. in the sample) i „izvan uzorka“ (engl. out of sample). Istovremeno rangiranje NM na poduzorcima „in-the-sample“ i „out-of-sample“ bilo prema MSE ili prema AIC ne dovodi do podudarnosti rangova i odabira prikladne NM jer najbolja NM na poduzorku „in-the-sample“, na temelju kriterija MSE i/ili AIC, često daje na poduzorku „out-of-sample“ visoke vrijednosti oba pokazatelja prikladnosti, i obrnuto. Stoga, da bi se došlo do najboljeg kompromisnog rješenja koristi se PROMETHEE metoda za odabir prikladne NM. Usporedbom „najbolje“ jednosmjerne – FNN(4,5,1) i „najbolje“ Jordanove NM – JNN(4,3,1) zaključuje se da je u približno jednakim uvjetima potrebno manje neurona u skrivenom sloju Jordanove NM (3) u odnosu na jednosmjernu (5), čime se potvrđuje da je Jordanova NM parsimonijska u odnosu na jednosmjernu, uz ne postojanje problema predeterminiranosti modela. Na taj način se dokazuje prva pomoćna hipoteza. Također, odabrana Jordanova NM ima bolju prediktivnu sposobnost u odnosu na jednosmjernu, čime se potvrđuje i druga pomoćna hipoteza istraživanja. Konačno se analizira konvergencija kamatnih stopa, uključivanjem inflatornih očekivanja procijenjenih Jordanovom NM u preformulirani oblik Taylorovog pravila.Neural networks (NNs) are appropriate to use in time series analysis under conditions of the unfulfilled assumptions, i.e. under conditions of non-normality and nonlinearity. The purpose of the paper is to investigate the disadvantaged of NNs and to propose ways to address these shortcomings with the aim of identifying the NN structure that will best adapt to inflation expectations. The research is based on a theoretical model that includes the characteristics of demand-pull and cost-push inflation, i.e. it uses labour market variable, financial variable, external factor and lagged inflation. The research is conducted at the aggregate level of euro area countries in the period from January 1999 to January 2017. Based on the estimated 90 feedforward NNs and 450 Jordan NNs, which differ in variable parameters (number of iterations, learning rate, initial weight value intervals, number of hidden neurons, weight value of the context unit), the model adequacy indicators for each NN (Mean Squared Error - MSE and Akaike Information Criterion - AIC) are calculated for two periods: “in-the-sample” and “out of sample”. Simultaneously ranking NNs on the “in-the-sample” and “out of sample” subsamples either according to MSE or AIC does not lead to the matching of rankings and the selection of a suitable NN because the “best” NN in the “in-the-sample”, based on MSE and/or AIC criteria, often has high “out of sample” values of both indicators, and vice versa. Therefore, in order to achieve the best compromise solution, a PROMETHEE method is used to select a suitable NN. By comparing the “best” feedforward NN - FNN(4,5,1) and the “best” Jordan NN - JNN(4,3,1) it is concluded that under approximately equal conditions less neurons in the hidden layer of Jordan NN (3) is required than in feedforward NN (5), confirming that Jordan NN is parsimonious compared to feedforward, without the existence of the overfitting problem. Thus, the first auxiliary hypothesis is proved. Moreover, the selected Jordan NN has a better predictive ability than the feedforward, which confirms second auxiliary hypothesis of the research. Finally, interest rate convergence is analysed, incorporating inflation expectations estimated by Jordan NN into the reformulated Taylor rule

Identifikacija strukture neuronske mreže u mjerenju očekivane inflacije

Neuronske mreže (NM) je prikladno koristiti u analizi vremenskih nizova u uvjetima narušenih pretpostavki, tj. u uvjetima „nenormalnosti“ i nelinearnosti. Svrha rada je istražiti nedostatke NM te predložiti načine kojima se ti nedostaci mogu otkloniti s ciljem identifikacije strukture NM koja će se najbolje prilagoditi inflacijskim očekivanjima. Polazi se od teorijskog modela koji uključuje karakteristike inflacije potražnje i inflacije troškova. Pri tom se koriste varijable tržišta rada, financijske varijable, eksterni faktori te inflacija iz prethodnog razdoblja. Istraživanje se provodi na agregiranoj razini zemalja euro zone u periodu od siječnja 1999. do siječnja 2017. godine. Na temelju procijenjenih 90 jednosmjernih NM i 450 Jordanovih NM, koje se razlikuju u promjenjivim parametrima (broj iteracija, stopa učenja, interval početnih vrijednosti težina, broj skrivenih neurona, vrijednost težine kontekstne jedinice), dobiveni su pokazatelji prikladnosti za svaku NM (prosječna kvadratna pogreška - MSE i Akaikeov informacijski kriterij - AIC) koji se odnose na dva perioda: „unutar uzorka“ (engl. in the sample) i „izvan uzorka“ (engl. out of sample). Istovremeno rangiranje NM na poduzorcima „in-the-sample“ i „out-of-sample“ bilo prema MSE ili prema AIC ne dovodi do podudarnosti rangova i odabira prikladne NM jer najbolja NM na poduzorku „in-the-sample“, na temelju kriterija MSE i/ili AIC, često daje na poduzorku „out-of-sample“ visoke vrijednosti oba pokazatelja prikladnosti, i obrnuto. Stoga, da bi se došlo do najboljeg kompromisnog rješenja koristi se PROMETHEE metoda za odabir prikladne NM. Usporedbom „najbolje“ jednosmjerne – FNN(4,5,1) i „najbolje“ Jordanove NM – JNN(4,3,1) zaključuje se da je u približno jednakim uvjetima potrebno manje neurona u skrivenom sloju Jordanove NM (3) u odnosu na jednosmjernu (5), čime se potvrđuje da je Jordanova NM parsimonijska u odnosu na jednosmjernu, uz ne postojanje problema predeterminiranosti modela. Na taj način se dokazuje prva pomoćna hipoteza. Također, odabrana Jordanova NM ima bolju prediktivnu sposobnost u odnosu na jednosmjernu, čime se potvrđuje i druga pomoćna hipoteza istraživanja. Konačno se analizira konvergencija kamatnih stopa, uključivanjem inflatornih očekivanja procijenjenih Jordanovom NM u preformulirani oblik Taylorovog pravila.Neural networks (NNs) are appropriate to use in time series analysis under conditions of the unfulfilled assumptions, i.e. under conditions of non-normality and nonlinearity. The purpose of the paper is to investigate the disadvantaged of NNs and to propose ways to address these shortcomings with the aim of identifying the NN structure that will best adapt to inflation expectations. The research is based on a theoretical model that includes the characteristics of demand-pull and cost-push inflation, i.e. it uses labour market variable, financial variable, external factor and lagged inflation. The research is conducted at the aggregate level of euro area countries in the period from January 1999 to January 2017. Based on the estimated 90 feedforward NNs and 450 Jordan NNs, which differ in variable parameters (number of iterations, learning rate, initial weight value intervals, number of hidden neurons, weight value of the context unit), the model adequacy indicators for each NN (Mean Squared Error - MSE and Akaike Information Criterion - AIC) are calculated for two periods: “in-the-sample” and “out of sample”. Simultaneously ranking NNs on the “in-the-sample” and “out of sample” subsamples either according to MSE or AIC does not lead to the matching of rankings and the selection of a suitable NN because the “best” NN in the “in-the-sample”, based on MSE and/or AIC criteria, often has high “out of sample” values of both indicators, and vice versa. Therefore, in order to achieve the best compromise solution, a PROMETHEE method is used to select a suitable NN. By comparing the “best” feedforward NN - FNN(4,5,1) and the “best” Jordan NN - JNN(4,3,1) it is concluded that under approximately equal conditions less neurons in the hidden layer of Jordan NN (3) is required than in feedforward NN (5), confirming that Jordan NN is parsimonious compared to feedforward, without the existence of the overfitting problem. Thus, the first auxiliary hypothesis is proved. Moreover, the selected Jordan NN has a better predictive ability than the feedforward, which confirms second auxiliary hypothesis of the research. Finally, interest rate convergence is analysed, incorporating inflation expectations estimated by Jordan NN into the reformulated Taylor rule