Forecasting By Vector Autoregression Models

Target is the second biggest retail company after Walmart. Native New Yorker, George Draper Dayton first built a company named Dayton Dry Goods Company in 1902 in the Minneapolis area which is now known as target headquarter. Walmart faced the out of stock issue problem last year and now their biggest competitor, Target, also has faced the same problem this year. Target has a problem keeping the availability of the product in their stores in Canada. It resulted in a huge loss of money and closing down their stores. The CEO of Target said that this is a serious problem and must been solved.

The extracted data used includes monthly returns from January 1972 to July 2011. The assets are selected so that the portfolio contains the largest, most liquid, and most tradable assets. The choice of such a variety of assets across several markets was used in order to generate a large cross sectional dispersion in average return. It helped to reveal new factor exposure and define a general framework of the correlated value and momentum effects in various asset classes.

After that time, VaR has become embraced by the Governing Bodies overseeing the world bank. In essence, VaR measures the probability of loss or gains related to capital stock. Furthermore, the understanding of the chance of loss or gain can be extended over the economic life of a project (Cecilia-Nicoleta et al.,

To avoid spurious results, unit root tests using Augmented Dickey-Fuller (ADF) (Dickey and Fuller, 1981) and Philips-Perron (PP) are performed to determine the time-series properties of the variables employed in the analysis. Two or more variables are said to be co-integrated when they exhibit long run equilibrium (relationship) if they share common trend(s).Therefore Auto-regressive distributed lag bounds approach (ARDL) is used to test it. The choice is based on several

Yes. This is a magazine. A snapshot in time. A static depiction of a single point -- a moment that has passed since it you opened it and began reading these very words.

The United States is the leading economy across the globe and experienced several tribulations in the recent past following the 2008 global recession. Despite these recent challenges, there are expectations among policymakers and financial experts that the country will experience solid economic growth. Actually, financial analysts have stated that the U.S. economy will be characterized by increased consumer spending, increased investments by businesses, reduced rate of unemployment, and reduction in government cut. Some analysts have also stated that the country’s economy will strengthen in 2014 with an average of 2.7 percent or more. However, these predictions can only be understood through an analysis of the current macroeconomic

Consumer expenditures rising during this term will move the aggregate demand curve to the right as increased spending increases demand. This BLS report indicates that the next term should show statistical aggregate demand increases, and according to the Classical model perspective encourages a laissez-faire approach concerning correction of the long-term economic factors (Colander, 2010). The Classical model works perfectly as consumer expenditures are trending on the rise when factoring consumer expenditures. Until a more apparent downturn shows itself, the invisible hand should continue to work naturally.

Forecasting numbers and data is an extremely important role in policy making. Economic forecasting plays an integral key role for the decision-making process, helping governments and policy makers to devise major policies and strategies. Many times, there will be an abundant amount of statistical forecasting being done in order to forecast various economic indicators, however the complexity of them changes a lot across different measures. Often times their will be many different sources that have published relatable economic data which conclude of different forecasts for major macroeconomic variables.

The Congressional Budget Office (2012) assesses short-term impact of fiscal policy by relying on the predicted impact of changes in monetary policy by the Federal Reserve and the products of Macroeconomic Advisors and HIS Global Insight (Reichling and Whalen, 2012). The models used by these organizations assume economic output depends on labor supply, technology, available capital, and demand for goods and services. The validity of this model depends largely on the assumption that economic cycles recur with minor changes only.

I supplement these data with macroeconomic time series with information on equity and credit market conditions, pulled from Federal Reserve Economic Data (FRED). Specifically, I use the Wilshire 5000 Total Market Index and CBOE Volatility Index (VIX) to capture equity market conditions, the TED Spread and Moody’s Seasoned Baa Corporate Bond Minus Federal Funds Rate Spread to capture credit market conditions, and the Consumer Price Index for All Urban Consumers to control for inflation.

Also, this estimation allows the using of a lagged dependent variable to check whether there is a possible cointegration in the economic variables, with the possibility of one variable forecasting another (Campbell & Shiller, 1988). Including the lagged dependent variable can reduce the occurrence of autocorrelation arising

The first macroeconomic variable that we believe will affect Apple, Incorporated is the global forecast for Gross Domestic Product for 2013. The global forecast for GDP is 3.3% for 2013 according to Global Macroeconomic Outlook. This is

Forecasting involves using past data to generate a number, set of numbers, or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning.

The economic forecasting staff for Merrill Finch developed probability estimates for the state of the economy, and the security analysts have developed software to estimate the rate of return on each of these alternatives under each state of the economy. A chart showing the results of the analysis is in Appendix A of this report.

Business forecasting is the process of studying historical performance for the purpose of using the information gained to project future business conditions so that decisions can be made today that will assist in the achievement of certain goals. Forecasting involves taking historical date and using it to project future data with a mathematical model. Forecasts are extensively used to support business decisions and direct the work of operations managers. In this paper I will introduce different types of forecasting techniques.