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M.Comm. / The aim of this study is the econometric improvement of the stochastic equations of an econometric model with specific reference made to the explanation and incorporation of stationarity and cointegration testing. The study is based on an existing macroeconometric forecasting model. The focus of the study is not on the improvement of the specification of individual equations per se, but rather on the econometric improvement thereof, therefore changes to the specification of individual equations have only been made in cases where test results strongly recommended it. The RAU-model had previously been exposed to neither structural stability-, stationarity-, nor cointegration testing and therefore both the explanation and implementation of these tests have been included in the study. It is, however, important to note that the main purpose of both stationarity and co-integration testing is not to substitute nonstationary data with data which is proven to be stationary, but rather to identify nonstationary and non-cointegrationary data for future improvement and enhancement of the RAU model. Following the completion of the abovementioned tests, parameters have been estimated for the individual equations of the three sectors of the RAU-model (i.e. the Real-, Balance of payments-, and the Monetary sectors). Thereafter the results have been evaluated on the basis of the economic-, statistic-, and econometric evaluation criteria. In cases where econometric inconsistencies arose from the violation of the assumptions underlying the econometric tests, appropriate transformation processes have been applied in an attempt to resolve the problem. Thereafter, tests have been carried out to determine the forecasting ability of the model as well as to compare the model results with the a priori results. In general, the aim of the study, to econometrically improve the stochastic equations of the RAU model, has been achieved on the basis of overall better regression- and evaluation results that have been obtained. Following the completion of the study, a new approach to econometric modelbuilding, which makes provision for the inclusion of both stationarity- and cointegration testing, is proposed.

by Ho Yan Wing Bosco. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 89-91). / Abstract also in Chinese. / ACKNOWLEDGMENTS --- p.iii / LIST OF TABLES --- p.iv / LIST OF ILLUSTRATIONS --- p.v / CHAPTER / Chapter I --- INTRODUCTION --- p.1 / Chapter II --- GENERAL DESCRIPTION OF THE ECONOMY OF HONG KONG --- p.7 / Chapter III --- METHODOLOGY AND THE SPECIFICATION OF THE ERC MODEL --- p.11 / Unit-Root Tests / Co-integration and Error Correction Model / The ERC Model / Chapter IV --- THE ESTIMATED MODEL --- p.44 / Chapter V --- POLICY SIMULATIONS AND THE DYNAMIC MULTIPLIER ANALYSIS --- p.62 / Chapter VI --- CONCLUSION --- p.69 / TABLES --- p.71 / ILLUSTRATIONS --- p.82 / BIBLIOGRAPHY --- p.89

本文探讨了国家制度环境对出口企业以及出口企业动态的影响。目前关于制度对贸易总值影响的研究已引起了越来越多的关注。然而，关于制度环境对贸易动态影响的研究是非常有限的。所有现有的研究都是基于对某一国家的分析。本文采用世界银行出口企业动态数据库，该数据库包含45个出口国以及200多个进口国的多国面板数据。研究表明，有效的制度环境会增加两国间的出口企业的数量以及出口企业的存活率。良好的制度环境会减少双方违反合同的可能性同时减少对合同执行以及经济法律体制的不确定性。其次，研究表明,与出口国不同，进口国的制度环境对出口企业的平均出口值有负面的影响。进口国有效的制度环境吸引了一些边际生产者进入出口市场，从而降低了整体企业出口的平均值。最后，本文发现制度环境的提升会减少出口企业的市场进入率。有效的制度环境会提高出口市场的稳定性，减少市场内部的流动率。 / This paper studies the effect of institutional quality on exporter behavior and export dynamics. There is a growing interest in the study on the effect of institutions on aggregate trade volume in the recent literature. However, the analysis of institutional effect on the dynamics of trade is relatively limited. Besides, all the existing studies that analyze the dynamic effect of institutions are based on exporting firms in a single country. We use the Exporter Dynamics Database from World Bank that contains rich panel of cross-country data involving 45 exporting countries and more than 200 importing countries in the world. First, we find that there are more exporting firms and the survival rate of the exporting firms is higher in better institutional environment. The good contracting environment makes the breach of the contract more difficult and reduces the uncertainty about contract enforcement and general economic legal system. Secondly, we show that institutions in the importing country have a negative effect on average export value, which is different from the exporting country. The effective institutions in the importing country attract some marginal producers in the export market which reduces the average export value per firm. Finally, entry rate of the exporting firms reduces with the quality of the country’s institutions. The good institutional environment increases market stability and reduces the turnover rate in the export market. / Detailed summary in vernacular field only. / Liu, Xiaojie. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 59-63). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese. / Chapter 1. --- Introduction --- p.5 / Chapter 1. --- Related Literature --- p.8 / Chapter 2. --- Data Analysis --- p.12 / Chapter 3. --- Empirical Analysis --- p.17 / Chapter 4. --- Discussion of the Results --- p.20 / Chapter 5.1 --- Extensive Margin --- p.21 / Chapter 5.2 --- Intensive Margin --- p.23 / Chapter 5.3 --- Entry Rate --- p.24 / Chapter 5.4 --- Survival Rate --- p.26 / Chapter 5. --- Econometric Issues --- p.29 / Chapter 6.1 --- Omitted Variable Bias --- p.29 / Chapter 6.2 --- Endogeneity --- p.30 / Chapter 7. --- Sensitivity Analysis and Robustness Checks --- p.32 / Chapter 7.1 --- Sensitivity to Alternative Samples --- p.32 / Chapter 7.2 --- Using Alternative Measures of Institutional Quality --- p.33 / Chapter 8. --- Conclusion --- p.35 / Tablesand Figures --- p.38 / References --- p.59

The aim of this thesis is the development and the application of econometric models with time-varying parameters in a policy environment. The popularity of these methods has run in parallel with advances in computing power, which has made feasible estimation methods that until the late '90s would have been unfeasible. Bayesian methods, in particular, benefitted from these technological advances, as sampling from complicated posterior distributions of the model parameters became less and less time-consuming. Building on the seminal work by Carter and Kohn (1994) and Jacquier, Polson, and Rossi (1994), bayesian algorithms for estimating Vector Autoregressions (VARs) with drifting coefficients and volatility were independently derived by Cogley and Sargent (2005) and Primiceri (2005). Despite their increased popularity, bayesian methods still suffer from some limitations, from both a theoretical and a practical viewpoint. First, they typically assume that parameters evolve as independent driftless random walks. It is therefore unclear whether the output that one obtains from these estimators is accurate when the model parameters are generated by a different stochastic process. Second, some computational limitations remain as only a limited number of time series can be jointly modeled in this environment. These shortcomings have prompted a new line of research that uses non-parametric methods to estimate random time-varying coefficients models. Giraitis, Kapetanios, and Yates (2014) develop kernel estimators for autoregressive models with random time-varying coefficients and derive the conditions under which such estimators consistently recover the true path of the model coefficients. The method has been suitably adapted by Giraitis, Kapetanios, and Yates (2012) to a multivariate context. In this thesis I make use of both bayesian and non-parametric methods, adapting them (and in some cases extending them) to answer some of the research questions that, as a Central Bank economist, I have been tackling in the past five years. The variety of empirical exercises proposed throughout the work testifies the wide range of applicability of these models, be it in the area of macroeconomic forecasting (both at short and long horizons) or in the investigation of structural change in the relationship among macroeconomic variables. The first chapter develops a mixed frequency dynamic factor model in which the disturbances of both the latent common factor and of the idiosyncratic components have time varying stochastic volatility. The model is used to investigate business cycle dynamics in the euro area, and to perform point and density forecast. The main result is that introducing stochastic volatility in the model contributes to an improvement in both point and density forecast accuracy. Chapter 2 introduces a nonparametric estimation method for a large Vector Autoregression (VAR) with time-varying parameters. The estimators and their asymptotic distributions are available in closed form. This makes the method computationally efficient and capable of handling information sets as large as those typically handled by factor models and Factor Augmented VARs (FAVAR). When applied to the problem of forecasting key macroeconomic variables, the method outperforms constant parameter benchmarks and large Bayesian VARs with time-varying parameters. The tool is also used for structural analysis to study the time-varying effects of oil price innovations on sectorial U.S. industrial output. Chapter 3 uses a bayesian VAR to provide novel evidence on changes in the relationship between the real price of oil and real exports in the euro area. By combining robust predictions on the sign of the impulse responses obtained from a theoretical model with restrictions on the slope of the oil demand and oil supply curves, oil supply and foreign productivity shocks are identified. The main finding is that from the 1980s onwards the relationship between oil prices and euro area exports has become less negative conditional on oil supply shortfalls and more positive conditional on foreign productivity shocks. A general equilibrium model is used to shed some light on the plausible reasons for these changes. Chapter 4 investigates the failure of conventional constant parameter models in anticipating the sharp fall in inflation in the euro area in 2013- 2014. This forecasting failure can be partly attributed to a break in the elasticity of inflation to the output gap. Using structural break tests and non-parametric time varying parameter models this study shows that this elasticity has indeed increased substantially after 2013. Two structural interpretations of this finding are offered. The first is that the increase in the cyclicality of inflation has stemmed from lower nominal rigidities or weaker strategic complementarity in price setting. A second possibility is that real time output gap estimates are understating the amount of spare capacity in the economy. I estimate that, in order to reconcile the observed fall in inflation with the historical correlation between consumer prices and the business cycle, the output gap should be wider by around one third.