The thesis aims at identifying the reasons behind the heterogeneous inflation performance of countries across Central and Eastern Europe. The impact of a large number of variables is being assessed in a dynamic panel data model covering 20 countries over the period 2003-2013. The empirical results suggest that cross-country differences in inflation are attributed to the structure of the economy, to the capital deepening effects and openness. Along with the structural factors, cyclical positions also prove to be of particular importance in explaining inflation across the region, since during the last decade most of the Central and Eastern European countries have experienced fast GDP growth, a credit boom and increased domestic demand that in turn fueled inflation.

This thesis focuses on the development of credit to the private sector in the Central and Eastern European (CEE) countries. Although the speed of credit growth in these countries has recently slowed down as the consequence of the global financial crisis, the overall increase in credit to the private sector over the past decades has been immense. As a result, the thesis examines whether this substantial increase in credit is linked to the convergence of the CEE countries towards the equilibrium or whether it represents an excessive credit growth that could threaten the macroeconomic and financial stability in these countries. We estimate the equilibrium credit levels for 11 transition countries by applying a dynamic panel data model. Since in-sample approach may bias the estimation results we perform the estimates out-of-sample using a panel of selected developed EU countries as a benchmark. The difference between the actual and estimated credit-to-GDP ratios serves as a measure of private credit excessiveness. The results indicate a slightly excessive or close to the equilibrium credit-to-GDP ratios in Bulgaria, Estonia, and Latvia prior to the financial crisis. With regard to the significant decline in GDP during the crisis this measure of credit excessiveness in these countries have further increased.

This thesis consists of three chapters on economic and econometric applications of Bayesian parameter estimation and model comparison. The first two chapters study the incidental parameter problem mainly under a linear autoregressive (AR) panel data model with fixed effect. The first chapter investigates the problem from a model comparison perspective. The major finding in the first chapter is that consistency in parameter estimation and model selection are interrelated. The reparameterization of the fixed effect parameter proposed by Lancaster (2002) may not provide a valid solution to the incidental parameter problem if the wrong set of exogenous regressors are included. To estimate the model consistently and to measure its goodness of fit, the Bayes factor is found to be more preferable for model comparson than the Bayesian information criterion based on the biased maximum likelihood estimates. When the model uncertainty is substantial, Bayesian model averaging is recommended. The method is applied to study the relationship between financial development and economic growth. The second chapter proposes a correction function approach to solve the incidental parameter problem. It is discovered that the correction function exists for the linear AR panel model of order p when the model is stationary with strictly exogenous regressors. MCMC algorithms are developed for parameter estimation and to calculate the Bayes factor for model comparison. The last chapter studies how stock return's predictability and model uncertainty affect a rational buy-and-hold investor's decision to allocate her wealth for different lengths of investment horizons in the UK market. The FTSE All-Share Index is treated as the risky asset, and the UK Treasury bill as the riskless asset in forming the investor's portfolio. Bayesian methods are employed to identify the most powerful predictors by accounting for model uncertainty. It is found that though stock return predictability is weak, it can still affect the investor's optimal portfolio decisions over different investment horizons.

人民幣是否可能成為另一個重要的國際貨幣，甚至挑戰美元的國際地位？此即本論文的問題意識。對此，本論文進一步提出三個研究問題：一是如何測量當前的人民幣國際化程度？二是如何測量當前的人民幣資本開放程度？三是資本開放對於人民幣國際化程度的影響為何？ 為此，本研究利用主成分分析（PCA），以建構人民幣國際化程度（CIDI）與人民幣資本帳開放程度（CAOI）。其次再利用動態追蹤資料模型──系統一般動差估計法（SGMM），以檢證各項人民幣綜合競爭力對於貨幣國際化程度的影響。最後，本研究進一步梳理人民幣資本帳開放的進程，並結合上述所有實證分析的結果，進而預估漸進資本開放下人民幣國際化的前景。研究對象包括人民幣在內的33種國際貨幣，研究時間則起自1999年歐元成立，迄於2009年。 本論文的發現三：一是，當前人民幣國際化程度進展相當快速。但截至2009年年底，人民幣國際化程度還很低，遠落後於美元、歐元、日圓，以及英鎊等主要國際貨幣。不僅如此，人民幣國際化程度也遜於俄羅斯盧布、巴西里拉，以及印度盧比等開發中國家所發行的貨幣。 二是，過去10年來，人民幣資本帳開放程度不升反降，截至2009年年底，人民幣的資本帳開放程度維持在零，這表示：人民幣是世界上管制最為嚴格的貨幣。相對而言，美元、歐元、日圓，以及英鎊的資本帳開放程度至少都在70％以上，特別是英鎊的資本帳開放程度更趨近於完全開放。 三是，根據SGMM的實證結果顯示，網路外部性、經濟規模、金融市場規模、貨幣穩定度，以及資本開放程度都是影響貨幣國際化程度的關鍵因素。在此基礎上，本研究利用發生機率（odds ratio），以計算不同資本開放情境下，人民幣成為前10大國際貨幣的可能性。結果顯示，如果人民幣的資本帳開放到73％左右，人民幣便可擠進前10大國際貨幣（發生機率為65.6％）。 不過，這只是最為保守的估計。原因有二：一是，隨者中國經濟實力的崛起，以及人民幣預期升值的脈絡下，國際市場對於人民幣的需求原本就很高。此時，人民幣資本帳如果能適時開放，則人民幣的國際持有將大幅增加。換言之，本研究沒有考量到，各貨幣競爭力因素與資本開放程度之間的加乘效果。 二是，資本開放不僅直接對貨幣國際化程度產生影響，也會透過擴大金融市場規模與網路外部性等其他貨幣競爭力因素，間接對貨幣國際化程度造成影響。這間接效果，本研究也沒有考量到。因此，可以預期的是，只要人民幣資本帳能夠漸進開放，人民幣國際化的前景將比本研究所預估的高出許多。 / This paper discusses whether the Renminbi (RMB) will become an international currency, even challenging to the U.S. dollar. In order to examine above question, this paper take the following three steps: 1. By using principal component analyses (PCA), this paper constructs two indices: currency internationalization degree index (CIDI) and capital account liberalization degree index (CAOI); 2. By using dynamic panel data model-system generalized method of moment (SGMM), this paper analyzes factors affect the CIDI, including economic and trade size, financial system, network externalities, confidence in the currency’s value, and CAOI; 3. According to the PCA and SGMM results, this paper calculates the odds ratio of RMB becoming important international currency. The reserch achieved the following results. First, the degree of internationalization of the RMB progress very fast, but the RMB CIDI is still very low, its CIDI far behinds the dollar, euro, Japanese yen, and pounds. Second, over the past 10 years, RMB CAOI is not increased but decreased. Its CAOI is at zero in 2009, this means that: the RMB is the most stringent controls in the world currency. In contrast, U.S. dollars, euros, yen, and pound CAOI are at least in more than 70%. Third, according to the SGMM results, economic size, financial system, network externalities, confidence in the currency’s value, and CAOI are key factors affect the CIDI. Based on this output, this paper forecasted that if the RMB CAOI is open to about 73%, RMB could be squeezed into the top 10 of the international currency. (The odds ratio is 65.6%) It is noteworthy that this is only the lowest estimates. This is because that this paper did not consider the interaction effects of each currency competitiveness factors and CAOI. Therefore, if RMB CAOI continues open, the prospect of RMB CIDI is much higher than estimated by this paper.

The aim of the master thesis is to empirically analyze if there is a support for new classics or new Keynesians as a dominant theory of short-run aggregate supply curve. The analysis is based on dynamic panel data model for 38 countries and period between 1970 and 2014. Because the results show some evidence on negative significance of level of inflation in contrast with its variability, I conclude that there is support for the new Keynesian theory. I focus on examination of the panel data assumptions such as the stationarity of explanatory variables, existence of the individual or random effects, validity of homogeneity of slope coefficients and mainly the cross-sectional dependence of error terms. After testing for these assumptions, I choose the most suitable method of estimation for dynamic panel data models. I use these methods for analyzing both linear and non-linear specification of the given model. As a result, we can see that the selection of right estimation method plays a great role in final outcomes. I also check model robustness by including changes of real oil price as a proxy variable for the supply shock in the economy.

Cette thèse est organisée en trois chapitres. Les deux premiers proposent une approche régularisée pour l’estimation du modèle de données de panel dynamique : l’estimateur GMM et l’estimateur LIML. Le dernier chapitre de la thèse est une application de la méthode de régularisation à l’estimation des élasticités de l’offre de travail en utilisant des modèles de pseudo-données de panel. Dans un modèle de panel dynamique, le nombre de conditions de moments augmente rapidement avec la dimension temporelle du panel conduisant à une matrice de covariance des instruments de grande dimension. L’inversion d’une telle matrice pour calculer l’estimateur affecte négativement les propriétés de l’estimateur en échantillon fini. Comme solution à ce problème, nous proposons une approche par la régularisation qui consiste à utiliser une inverse généralisée de la matrice de covariance au lieu de son inverse classique. Trois techniques de régularisation sont utilisées : celle des composantes principales, celle de Tikhonov qui est basée sur le Ridge régression (aussi appelée Bayesian shrinkage) et enfin celle de Landweber Fridman qui est une méthode itérative. Toutes ces techniques introduisent un paramètre de régularisation qui est similaire au paramètre de lissage dans les régressions non paramétriques. Les propriétés en echantillon fini de l’estimateur régularisé dépend de ce paramètre qui doit être sélectionné parmis plusieurs valeurs potentielles. Dans le premier chapitre (co-écrit avec Marine Carrasco), nous proposons l’estimateur GMM régularisé du modèle de panel dynamique. Sous l’hypothèse que le nombre d’individus et de périodes du panel tendent vers l’infini, nous montrons que nos estimateurs sont convergents and assymtotiquement normaux. Nous dérivons une méthode empirique de sélection du paramètrede régularisation basée sur une expansion de second ordre du l’erreur quadratique moyenne et nous démontrons l’optimalité de cette procédure de sélection. Les simulations montrent que la régularisation améliore les propriétés de l ’estimateur GMM classique. Comme application empirique, nous avons analysé l’effet du développement financier sur la croissance économique. Dans le deuxième chapitre (co-écrit avec Marine Carrasco), nous nous intéressons à l’estimateur LIML régularisé du modèle de données de panel dynamique. L’estimateur LIML est connu pour avoir de meilleures propriétés en échantillon fini que l’estimateur GMM mais son utilisation devient problématique lorsque la dimension temporelle du panel devient large. Nous dérivons les propriétes assymtotiques de l’estimateur LIML régularisé sous l’hypothèse que le nombre d’individus et de périodes du panel tendent vers l’infini. Une procédure empirique de sélection du paramètre de régularisation est aussi proposée. Les bonnes performances de l’estimateur régularisé par rapport au LIML classique (non régularisé), au GMM classique ainsi que le GMM régularisé sont confirmées par des simulations. Dans le dernier chapitre, je considère l’estimation des élasticités d’offre de travail des hommes canadiens. L’hétérogéneité inobservée ainsi que les erreurs de mesures sur les salaires et les revenus sont connues pour engendrer de l’endogéneité quand on estime les modèles d’offre de travail. Une solution fréquente à ce problème d’endogéneité consiste à régrouper les données sur la base des carastéristiques observables et d’ éffectuer les moindres carrées pondérées sur les moyennes des goupes. Il a été démontré que cet estimateur est équivalent à l’estimateur des variables instrumentales sur les données individuelles avec les indicatrices de groupe comme instruments. Donc, en présence d’un grand nombre de groupe, cet estimateur souffre de biais en échantillon fini similaire à celui de l’estimateur des variables instrumentales quand le nombre d’instruments est élevé. Profitant de cette correspondance entre l’estimateur sur les données groupées et l’estimateur des variables instrumentales sur les données individuelles, nous proposons une approche régularisée à l’estimation du modèle. Cette approche conduit à des élasticités substantiellement différentes de ceux qu’on obtient en utilisant l’estimateur sur données groupées. / This thesis is organized in three chapters. The first two chapters propose a regularization approach to the estimation of two estimators of the dynamic panel data model : the Generalized Method of Moment (GMM) estimator and the Limited Information Maximum Likelihood (LIML) estimator. The last chapter of the thesis is an application of regularization to the estimation of labor supply elasticities using pseudo panel data models. In a dynamic panel data model, the number of moment conditions increases rapidly with the time dimension, resulting in a large dimensional covariance matrix of the instruments. Inverting this large dimensional matrix to compute the estimator leads to poor finite sample properties. To address this issue, we propose a regularization approach to the estimation of such models where a generalized inverse of the covariance matrix of the intruments is used instead of its usual inverse. Three regularization schemes are used : Principal components, Tikhonov which is based on Ridge regression (also called Bayesian shrinkage) and finally Landweber Fridman which is an iterative method. All these methods involve a regularization parameter which is similar to the smoothing parameter in nonparametric regressions. The finite sample properties of the regularized estimator depends on this parameter which needs to be selected between many potential values. In the first chapter (co-authored with Marine Carrasco), we propose the regularized GMM estimator of the dynamic panel data models. Under double asymptotics, we show that our regularized estimators are consistent and asymptotically normal provided that the regularization parameter goes to zero slower than the sample size goes to infinity. We derive a data driven selection of the regularization parameter based on an approximation of the higher-order Mean Square Error and show its optimality. The simulations confirm that regularization improves the properties of the usual GMM estimator. As empirical application, we investigate the effect of financial development on economic growth. In the second chapter (co-authored with Marine Carrasco), we propose the regularized LIML estimator of the dynamic panel data model. The LIML estimator is known to have better small sample properties than the GMM estimator but its implementation becomes problematic when the time dimension of the panel becomes large. We derive the asymptotic properties of the regularized LIML under double asymptotics. A data-driven procedure to select the parameter of regularization is proposed. The good performances of the regularized LIML estimator over the usual (not regularized) LIML estimator, the usual GMM estimator and the regularized GMM estimator are confirmed by the simulations. In the last chapter, I consider the estimation of the labor supply elasticities of Canadian men through a regularization approach. Unobserved heterogeneity and measurement errors on wage and income variables are known to cause endogeneity issues in the estimation of labor supply models. A popular solution to the endogeneity issue is to group data in categories based on observable characteristics and compute the weighted least squares at the group level. This grouping estimator has been proved to be equivalent to instrumental variables (IV) estimator on the individual level data using group dummies as intruments. Hence, in presence of large number of groups, the grouping estimator exhibites a small bias similar to the one of the IV estimator in presence of many instruments. I take advantage of the correspondance between grouping estimators and the IV estimator to propose a regularization approach to the estimation of the model. Using this approach leads to wage elasticities that are substantially different from those obtained through grouping estimators.