Time series analysis

Determinants of Gross Domestic Saving in Ethiopia: a time series analysis Kidane Badeg Contact: Kidane Badeg MoFED P.o.Box 1905, Addis Ababa , Ethiopia Email: kbadeg@mofed.gov.et Abstract The study conduct a time series analysis of the determinants of gross domestic saving in Ethiopia using co-integration and error correction econometric modeling(ECM), and employed data for the period of 1971-2009 collected from National bank of Ethiopia (NBE), MOFED, CSA and world bank on annual base. The

INTRODUCTION At the core of the stop and frisk policy as utilized by the New York Police Department is racial profiling. Racial profiling has a significant and often controversial place in the history of policing in the United States. Racial profiling can be loosely defined as the use of race as a key determinant in law enforcement decisions to stop, interrogate, and/or detain citizens (Weitzer & Tuch, 2002). Laws in the United States have helped to procure and ensure race based decisions in law

How to create an EcOS database? In EcOS, data and metadata are stored in time series database. An EcOS database has some key characteristics: • An EcOS database acts as a container for data, metadata, and objects. • The database structure is governed by its attributes. • Since it is a time-series database, each series stored is tied to a time dimension. • Each time series has a default scale. This video shows how to create an EcOS database. Step 1. Planning. Before constructing a database in EcOS

distribution function. Time Series Analysis - ARIMA models - Basic Definitions and Theorems about ARIMA models marginal distribution function of a time series (V.I.1-1) Before analyzing the structure of a time series model one must make sure that the time series are stationary with respect to the variance and with respect to the mean. First, we will assume statistical stationarity of all time series (later on, this restriction will be relaxed). Statistical stationarity of a time series implies that the

might be best for analyzing the data, if you were to collect a sample. "Inferential statistics is drawing conclusions of large data sets called a population" (Jaggia & Kelly, 2014). To analyze inferential statistics, I use regression analysis. "Regression analysis is used to examine the relationship between two or more variables" (Jaggia & Kelly, 2014).

planning. Many times, this unique approach is used not only to provide a baseline, but also to offer a prediction into the corporation 's future. In the functional areas of finance and accounting, forecasts provide the basis for budgetary planning and cost control. Marketing relies on sales forecasting to plan new products, compensate sales personnel, and

QUASI-EXPERIMENT 1 Quasi-Experiment: Interrupted Time Series 5 Quasi-Experiment: Interrupted Time Series Name Institution Professor Course Date Quasi-Experiment: Interrupted Time Series Introduction Principally, quasi-experiments utilize the comparison of two existing set of groups to determine the maximum level of impact

A TREND ORIENTED POWER SYSTEM SECURITY ANALYSIS METHOD BASED ON LOAD PROFILE Power system security is an important aspect in the present generation. Power system security must also be economical. We can’t predict the fault at every time, so based on trend analysis method on load profile in this paper we are going to see the various methods which help for the protection of the power system. So we should carefully monitor the security. By these methods discussed in the paper we

A STUDY OF THE ECONOMIC FORECASTING OF NEW ONE FAMILY HOUSEHOLDS SOLD IN THE US – AN ANALYSIS Context and Objective of the Analysis The US housing industry has witnessed a downward trend post 2005 due to deteriorating macroeconomic conditions in the United States. The steep decline in the last 5 years has led to investigations on the future of the industry and understands the way forward for the industry. The report answers the following questions: How long is the fall in the industry going to

conjunction with experts and hypotheses rather than gathering information from other sources and analyzing them. At the same time, Time series and Casual relationship forecasting has similarities because each is dependent on another aspect. Though Time series focuses on the seasonal aspects of things, casual relationship could very well be dependent on the seasons. For example, the Time series forecasting is big on farmers for planning their crops; however bad weather would cause a casual relationship between