The Current Account and Balance of Payments: Implications towards the Real Exchange Rate, GDP Growth, Asset Prices and Stability Abstract in English In the aftermath of the global financial crisis, large global imbalances among countries' current account have been cited as a possible cause. The real exchange rate emerges as a key policy tool among countries within the research literature and in practice to manage a country's Balance of Payments indicators, despite mixed evidence. In this master thesis, we will construct a vector error correction model for cointegrating relationships utilizing the Johansen's test, using time series data for the US, UK, and Australia for the period 1973-2018. We examined the relationships between the real exchange rate, the current account, the financial account, net reserves, the interest rate, and openness to trade as well as employing Granger Causality tests. In the US, we found relationships between net reserves and both the current account and the real exchange rate. In the UK, the interest rate and the real exchange rate have a cointegrating relationship. In both the UK and Australia, we found a cointegrating relationship with the real exchange rate and current account. We examine the various theoretical and practical approaches towards viewing the Balance of Payments...

The gross domestic product (GDP) is an essential measure of the state of economic activity and serves as a crucial tool for policymakers, investors, or businesses. However, the official GDP estimate in the Czech Republic is only available with a lag of approximately 60 days, and the Czech National Bank (CNB) announces its GDP forecast once in each quarter. This thesis focuses on predicting GDP growth in the current quarter, referred to as nowcasting. I employ several methods to nowcast the real GDP growth in the Czech Republic in a pseudo-real-time setting and compare their performance. Additionally, I investigate the possibility of creating an ensemble model by using a weighted average of several nowcasting models. The results suggest that the Dynamic Factor Model (DFM) performs best in the GDP nowcasting task, and its predictive accuracy is comparable with the official CNB nowcast. Furthermore, the model averaging process yields accuracy close to the best individual model while addressing model uncertainty. The GDP nowcast of the DFM will be made available to the public in real-time on a website and updated with a daily frequency.

The global financial crisis that started in the USA affected several countries around the world. This study focuses on only three countries; the USA, Sweden, and Italy, which are examples of economies with three different labor market models. The purpose of this study is to investigate if and in that case how Okun's law was affected by the global financial crisis in the three countries’ labor market models and if there are any differences in the correlations before and after the global financial crisis. Okun’s difference version was used in this study. Quarterly time series data was used in this study during the time period 1985-2019. The Chow test was used to test the hypothesis. The results show that the global financial crisis affected Okun’s law after the crisis in all three countries. The USA, Sweden, and Italy were affected differently

Foreign direct investment (FDI) is played a vital role for boosting up the economies of developing countries. Hence, it is necessary to know the factors that determines the flows of FDI in the developing countries. This study has attempted to investigate how different factors affect the inflow of foreign direct investment in South Asian Countries. To attain the objective this study has collected data on the respective variables for 45 years and considered seven countries. The relationship between different economic variables and their overall impact on FDI inflows have been examined through various panel models like basic pooled OLS estimation, entity fixed effect model, time fixed effect estimation and random effect model. The outcome of this study is that GDP of the country is the main factor behind the FDI inflows in South Asian countries.

The main aim of this thesis is to investigate the relationship between political stability, inflation, unemployment, GDP per capita, and vice versa. Previously this question has been studied in the relationship between political stability and each of these economic variables individually. With this research we can analyse if the poor economic performance in some countries is caused by unstable political institutions, that is why we find it important. Here we are analysing and comparing all of the economical variables at the same time and analysing which ones show the strongest relationships, or if the relationships are significant or not significant. In previous studies, the measurements that were used for political stability were: government changes, cabinet changes, index of economic freedom, or polity state. However, in this paper, the political stability measurement used is the Political stability index and Absence of Violence/Terrorism, which measures the likelihood that the government will be destabilised. Additionally, differently, from previous studies, Granger causality is used to understand causality between political stability and economic development variables.

Transportation is one of the largest sectors contributing to CO2-emissions, and has doubled its emissions in 30 years. Despite this, studies of the environmental Kuznets curve (”EKC”) often focus on stationary industry emissions. Studies of the EKC have detected an N-shape, rather than an inverted U-curve, indicating that rich nation’s emissions, in fact, increase again after the downturn. Possibly, this could be explained by a trend for inhabitants of wealthy countries with high equality to purchase local products and potentially reverse a trend of dirty-industry emigration. Local production and movement of intermediate goods demand domestic goods transportation. To my knowledge, no previous research has studied how changes in GDP/capita, trade intensity and GINI-index are related to CO2-emissions from domestic goods transportation in wealthy countries with high equality. To study the relationship, mathematical tests using Panel data with Fixed Effects Regression were used. Five countries qualified for the tests, having both high equality (lowest GINI-index) and high GDP/capita, and were included in the study for the year interval 2000-2020. Test results showed a significant correlation between the following: (1) wealth coincides positively with CO2-emissions, (2) trade intensity coincides negatively with CO2-emissions and (3) GINI-index coincides positively with CO2-emissions. Methodologically, this study contributes with the estimator GDP/GINI-index, rather than GDP solely, which could be a better estimator for the richness of a country’s population. The mathematical test results indicate that domestic goods transportation could be a reason for the increased CO2-emissions from developed wealthy countries. This could be a development of the environmental Kuznets curve.

Greenhouse Gas emission increase is largely attributed to carbon dioxide emissions as the major gas causing climate change and atmospheric warming. According to Environmental Kuznets Curve Theory (EKC), the increase in economic growth is expected to reduce the environmental pollution from carbon dioxide emission caused at the beginning stages of economic growth. In this thesis, I examined the impact of economic growth on carbon dioxide emission. The key hypothesis tested in this study is the Environmental Kuznets Curve hypothesis. Data from 1967 to 2016 from over 15 countries in North and South America, published by the World Bank were used. Since EKC posits a non-linear relationship between economic growth (GDP/capita) and Carbon dioxide emission, I used a quadratic component in the regression model. I analyzed the data using the OLS regression as my baseline model. Each country is unique in many respects that are hard to capture by a set of variables in econometrics model. This poses a challenge to estimating an unbiased estimate. Using panel data model allowed controlling for time invariant unobserved country-specific factors that could bias the estimates. I estimated a fixed effect panel regression to examine the relationship between carbon dioxide emissions and economic growth is primarily measured with Gross Domestic Product (GDP) per capita. The results of the fixed effect panel regression showed that all variables are significant, except export and inflation which were not significant. OLS could not solve the issue of heterogeneity among the variables. Estimating country-specific fixed effects model eliminates unobserved heterogeneity across countries and, therefore provides relatively unbiased estimates compared to OLS estimates. The positive correlation between Total CO2 emissions, CO2 emissions from Solid, and CO2 emissions from gas and GDP per capita suggests that carbon dioxide emissions increase as GDP/ capita increases before the turning point. The negative correlation between Total CO2 emissions, CO2 emissions from Solid, and CO2 emissions from gas and GDP per capita squared suggests that there is a polynomial (quadratic) form which is like that of inverted U-shape of the EKC curve. The coefficient, although it is very small, suggests the impact of the negative relationship after the turning point at the vertex of EKC curve is fractional. As expected, the result indicates a higher population causes an increase in total CO2 emissions. The result from CO2 emissions from liquid shows a negative relationship between the dependent variable CO2 emissions from liquid and the independent variable GDP per capita at the highest level of significance. This result is different from that of total carbon dioxide emissions, CO2 emissions from Solid, and CO2 emissions from gas. Carbon emission from liquid looks different from carbon emissions from solid and gas. There are high and constant emission throughout all the years and in all countries used in the analysis. EKC hypothesis is proven to be true for total carbon dioxide emissions, carbon dioxide emission from solid and gas. The hypothesized correlation between GDPs per capita square and CO2 emissions is statistically supported for Total CO2 emission, CO2 emission from solid and CO2 emission from gas. CO2 emissions from Solid, and CO2 emissions from gas and GDP per capita squared suggest that there is a polynomial (quadratic) form which is like that of inverted U-shape of the EKC curve. This proves that EKC model is proven to be true for my data. Policies like population policies can help in increasing growth in GDP per capita and reducing growth in the amount of carbon dioxide emissions. Population policies could play a significant role aimed at mitigating and reducing climate change.

So far, numerous studies have been devoted to investigate the relationship between happiness and income by asking the question whether economic growth has a positive impact on happiness. However, the reversed relationship from happiness to economic output has received much less attention in the literature. This paper attempts to investigate such relationship by using data from the European Value Survey that contains subjective reported well-being (happiness, or life satisfaction) values across 47 European countries from 1981 to 2009. Gender imbalance is used as an instrument for happiness in order to disentangle the causal effect of happiness on income. Based on a derived Solow model, where labor efficiency is assumed to be positively affected by worker happiness, regression analyses suggest that the sense of happiness does have a positive and highly significant impact on GDP per worker. Robustness tests further show that the result also holds for life satisfaction. According to the results, the author recommends governments to use well-being oriented index, along with GDP to measure the overall economy.

The objective of this research concerns identifying whether or not there is a relationship between oil price increases in a given quarter and the likelihood of a recession in the subsequent quarter. The data used is gathered from the St. Louis Fed's Fred II, the National Bureau of Economic Research, and the Energy Information Administration to generate modified variables. These variables are tested using a qualitative dependent variable, recession, in a binary choice model. The findings validated the assumption that oil prices do have a correlation with recessions, and that the relationship is a direct one. Based on the model, an increase in the price of oil will positively affect the likelihood of a "recession" outcome versus the alternative, "no recession". It is anticipated that the results will inspire future research into the causes and effects of oil price surges, as well as the determinants of economic contractions in the future based on policy decisions and economic decision-making practices in the present.

This paper serves to address the effect of time on the sales of clothing retail, from 2010 to May 2019. The data was retrieved from the US Census, where N=113 observations were used, which were plotted to observe their trends. Once outliers and transformations were performed, the best model was fit, and diagnostic review occurred. Inspections for seasonality and forecasting was also conducted. The final model came out to be an ARIMA (2,0,1). Slight seasonality was present, but not enough to drastically influence the trends. Our results serve to highlight the economic growth of clothing retail sales for the past 8 years, cementing the significance of the production economy's stability. The quarterly GDP data was collected in order to find out the relationship with the differenced clothing data. Some observations of GDP data were affected by the clothing data before removing the seasonality. After removing the seasonality, the clothing expense is white noise and not predictable from the historical GDP.