As compared to short-term forecasting (e.g., 1 day), it is often challenging to accurately forecast the volume of precipitation in a medium-term horizon (e.g., 1 week). As a result, fluctuations in water inflow can trigger generation shortage and electricity price spikes in a power system with major or predominant hydro resources. In this paper, we study a two-stage robust scheduling approach for a hydrothermal power system. We consider water inflow uncertainty and employ a vector autoregressive (VAR) model to represent its seasonality and accordingly construct an uncertainty set in the robust optimization approach. We design a Benders' decomposition algorithm to solve this problem. Results are presented for the proposed approach on a real-world case study.

Master of Science / Department of Statistics / Juan Du / Space and time are often vital components of research data sets. Accounting for and utilizing the space and time information in statistical models become beneficial when the response variable in question is proved to have a space and time dependence. This work focuses on the modeling and analysis of crop yield over space and time. Specifically, two different yield data sets were used. The first yield and environmental data set was collected across selected counties in Kansas from yield performance tests conducted for multiple years. The second yield data set was a survey data set collected by USDA across the US from 1900-2009. The objectives of our study were to investigate crop yield trends in space and time, quantify the variability in yield explained by genetics and space-time (environment) factors, and study how spatio-temporal information could be incorporated and also utilized in modeling and forecasting yield. Based on the format of these data sets, trend of irrigated and dryland crops was analyzed by employing time series statistical techniques. Some traditional linear regressions and smoothing techniques are first used to obtain the yield function. These models were then improved by incorporating time and space information either as explanatory variables or as auto- or cross- correlations adjusted in the residual covariance structures. In addition, a multivariate time series modeling approach was conducted to demonstrate how the space and time correlation information can be utilized to model and forecast yield and related variables. The conclusion from this research clearly emphasizes the importance of space and time components of data sets in research analysis. That is partly because they can often adjust (make up) for those underlying variables and factor effects that are not measured or not well understood.

This paper explores the relationship between household income inequality and macroeconomic uncertainty in the United States. Using a novel large-scale macroeconometric model, we shed light on regional disparities of inequality responses to a national uncertainty shock. The results suggest that income inequality decreases in most states, with a pronounced degree of heterogeneity in terms of the dynamic responses. By contrast, some few states, mostly located in the Midwest, display increasing levels of income inequality over time. Forecast error variance and historical decompositions highlight the importance of uncertainty shocks in explaining income inequality in most regions considered. Finally, we explain differences in the responses of income inequality by means of a simple regression analysis. These regressions reveal that the income composition as well as labor market fundamentals determine the directional pattern of the dynamic responses. / Series: Working Papers in Regional Science

Before a government makes economic policies, it must first fully understand the causality between energy consumption and economic growth. This study uses Chow Test, Unit Root Test, Co-integration Test, Vector Autoregressive Model, Vector Error Correction Model, Granger Causality Test, Impulse Response Function and Variance Decomposition to examine whether the relationships between energy consumption and economic growth for Taiwan and Korea had changed after the Asian Financial Crisis of 1997, in order to understand whether their economic policies have changed in response. Taiwan¡¦s energy consumption and GDP had one-way effect ¡V that is, her energy consumption affected GDP but not vice versa ¡V while that of South Korea exhibited a two-way relationship. However, after the Crisis, such relationship for Taiwan had changed to that of two-way. The relationship between energy consumption and GDP for South Korea remained two-way after the Crisis.

Trade deficit, financial deficit, and current account deficit of the United States have all been problems deeply concerned by economists and politicians in recent decades. Since the third season of 2000, a recession of the United States and the whole world has gradually started to appear. In addition, as a result of the 9/11 terrorist attacks and the war in Iraq the stock market has begun to decline significantly. In order to promote the recovery of its economy, the federal government determines to adopt the expanded financial policy which will most likely in the end cause its financial deficit more serious. The main purpose of this paper is to investigate the factors that influence the current account deficit of the United States. Because the study considers foreign variables that related researches ignore, we choose five variables as follows: regional output differential, regional interest rate differential, terms of trade, regional real effective exchange rate, and current account. Therefore, we adopt the Unit Root Test, the Granger Causality Test, the Co-integrating Test, and SVAR (Structural Vector Autoregressive) model to run RATS and E-views. It is the finding of empirical result that the United States government considers terms of trade and current account that can't be quantized of the first importance rather than the exchange rate factor that general research is thought. This is one of the contributions of the study.

In this paper, we explore the relationship between state-level household income inequality and macroeconomic uncertainty in the United States. Using a novel large-scale macroeconometric model, we shed light on regional disparities of inequality responses to a national uncertainty shock. The results suggest that income inequality decreases in most states, with a pronounced degree of heterogeneity in terms of shapes and magnitudes of the dynamic responses. By contrast, some few states, mostly located in the West and South census region, display increasing levels of income inequality over time. We find that this directional pattern in responses is mainly driven by the income composition and labor market fundamentals. In addition, forecast error variance decompositions allow for a quantitative assessment of the importance of uncertainty shocks in explaining income inequality. The findings highlight that volatility shocks account for a considerable fraction of forecast error variance for most states considered. Finally, a regression-based analysis sheds light on the driving forces behind differences in state-specific inequality responses. / Series: Working Papers in Regional Science

The degree work is focused on analysis of inflation with help of suitable econometric models. Inflation with it's forms and possibilities of measuring is described at the beginning of the paper. There is mentioned an importance of monitoring and analysing inflation in view of Czech national bank. Consequently there are described characteristics of time series, which are important from viewpoint of construction of econometric models. Next part of this paper is focused on characterization of econometrics models. At first there is vector autoregression model, in this connection there is discussed the essence of Granger causality and impulse reaction. There are also noticed both error correction model and vector error correction model. The empirical part of degree work involves the use of these models on selected macroeconomic time series of the Czech republic. The objective is to analyze the relationship between inflation and other individual macroeconomic quantities. There is established the optimal vector autoregressive model and the results of Granger causality and impulse reaction are interpretated. Both error correction model and vector error correction model examining cointegration are also applied.

Abstract   In this report analysis of foreign exchange rates time series are performed. First, triangular arbitrage is detected and eliminated from data series using linear algebra tools. Then Vector Autoregressive processes are calibrated and used to replicate dynamics of exchange rates as well as to forecast time series. Finally, optimal portfolio of currencies with minimal Expected Shortfall is formed using one time period ahead forecasts

This paper examines the effects of monetary policy on macroeconomic variables in Botswana as a developing small macro-economy using the Markov-switching structural vector autoregressive (MS-SVAR) framework, utilising time-series data from 1994: Q1 to 2019: Q4. The study makes use of bank rate (interest rate), inflation and output gap. The first model is a structural vector autoregressive (VAR) model that takes the form employed by Rudebusch and Svensson (1999), whilst the second one makes use of the same structure but includes Markov switching in the policy rule (i.e., Markov switching SVAR). Regime-switching models can effectively describe the data generating process when considering both in-sample and out of sample evaluations compared to the linear models, which submerge the structural changes that have occurred in the economy over the years. The results from the SVAR shows that monetary policy has a symmetric impact on the output gap and inflation. Therefore, it can be noted that non-linearities in the structural model do not necessarily imply asymmetric effects of shocks. Furthermore, the MS-SVAR shows that the Central Bank of Botswana responds differently to policy shocks in different regimes. This underscores the importance of regime-switching features in providing a more accurate description of the economy.

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