Time Series Model

This paper assesses the relative merits of panel time series models in forecasting sovereign default. It explores the contentious issue of whether controlling for time-series and country heterogeneity is important in forecasting emerging market default. For this purpose, it uses conventional inference methods alongside forecasting performance statistics based on both statistical- and economic-loss functions. Since sovereign debt states are rather

Motivated by economic-theory concepts—the Fisher hypothesis and the theory of the term structure—we consider a small set of simple bivariate closed-loop time-series models for the prediction of price inflation and of long- and short-term interest rates. The set includes vector autoregressions (VAR) in levels and in differences, a cointegrated VAR, and a non-linear VAR with threshold cointegration based on data from Germany, Japan, UK, and the U.S. Following a traditional comparative evaluation of predictive accuracy, we subject all structures to a mutual validation using parametric bootstrapping. Ultimately, we utilize the recently developed technique of Mallows model averaging to explore the potential of improving upon the predictions through combinations. While the simulations confirm the traded wisdom that VARs in differences optimize one-step prediction and that error correction helps at larger horizons, the model-averaging experiments point at problems in allotting an adequate ...

This paper examines the sources of fluctuations in inflation and output in two leading transitioneconomy candidates for admission to the European Union (EU), Poland and Hungary. Using a rational expectations, dynamic open economy aggregate supply- aggregate demand model, we consider real oil price, supply, balance of payments, demand, and monetary disturbances incorporating important features of transition economies such as balance

Movies are among the most prominent contributors to the global entertainment industry today, and they are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial Neural Network. The models stated above were compared on a variety of factors, including their accuracy on the training and validation datasets as well as the testing dataset, the availability of new movie characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered that certain characteristics have a greater impact on the likelihood of a film's success than others. For example, the existence of the genre action may have a significant impact on the forecasts, although another genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best performing model of all the models discussed.

On 17 February 2003, London introduced a pioneering congestion-charging scheme. The impact on traffic was sudden and dramatic. For example, car movements within the congestion-charging zone (21 km2) were reduced by about 30%. However, the impact on other activities, such as retail business within the charged zone, is taking longer to become evident. Many unusual events happened in close succession in 2003 making it difficult to isolate the effect of any individual event on the retail sales. This paper describes a detailed study on the impact of the congestion charge (CC) on the John Lewis Oxford Street (JLOS) store, which is one of the biggest retail stores within the charged zone. The impact is estimated using descriptive analysis, forecasting methods and econometric models. The analysis is based on weekly sales data for the period January 2000 to January 2004 (three years before the CC and about one year afterwards) relating to JLOS and five other John Lewis stores in and around London. A seasonal autoregressive integrated moving average (SARIMA) model was fitted, showing that actual sales match predicted sales up to 19 January 2003 for the JLOS store but that actual sales fall consistently below predicted sales thereafter. Clearly, something changed for the JLOS store during the week after 19 January 2003 with no sign of recovery to the end of the time series. In order to isolate the effects of a range of factors on sales, econometric models were fitted. The drop in sales at the JLOS attributable to the CC was found to be 5.5% by the time-series model and 8.2% by the panel model.