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Forecasting Analytics Who Should Take This Course: Data Scientists, data analysts, sales forecasters, marketing managers, accountants, economists, financial analysts. Forecasting methods and analytical tools. The OECD’s forecasts combine expert judgement with a variety of existing and new information relevant to current and prospective developments. Here, a combination of model- based analyses and statistical indicator models play an important role in . With this new information, and using the previous set of projections as a starting point, the effects of the new elements and revised judgments are typically assessed on the basis of model simulations using the NIGEM global model and short- term indicator models. Thus the likely impact of combined and individual changes in assumptions and new information on key aggregates can be assessed in consistent fashion for each of the major economies and economic groupings. These results are mechanical and therefore intended to be no more than a guide to the informed judgments of country and topic experts on the underlying “forces acting”. The use of indicator models. For the euro area and individual G7 economies, the near- term assessment also takes particular account of projections from a suite of statistical models using high frequency indicators to provide estimates of near- term quarterly GDP growth, typically for the current and next quarter or so. This analysis builds on the work of S. These models typically combine information from both . The procedures are relatively automated and can be run whenever major monthly data are released, allowing up dating and choice of model according to the information set available. The most important gains from using the indicator approach are found to be for current- quarter forecasts made at or immediately after the start of the quarter in question, where estimated indicator models appear to outperform autoregressive time series models, both in terms of size of error and directional accuracy. ![]() The main gains from using a monthly approach arise once one month of data is available for the quarter being forecast, typically two to three months before the publication of the first official outturn estimate for GDP. For one- quarter- ahead projections, the performance of the estimated indicator models are only noticeably better than simpler time series models once one or two months of information become available for the quarter preceding that being forecast. Modest gains are nonetheless to be made in terms of directional accuracy from using the indicator models. Statistical indicator models are nonetheless limited in their ability to forecast quarterly GDP growth. Even with a complete set of monthly indicators for the quarter, the 7. GDP growth in that quarter lie in the range from 0. Forecasting errors can also arise for a variety of reasons, including revisions to the initial published data and inaccuracies in the projections of the incoming monthly data. Regular indicator model- based estimates of GDP now feed into both routine Economic Outlook assessment exercises and interim analyses and forecast updates released to the press on a routine basis. While the OECD's world trade forecast is built as the aggregation of individual country import and export forecasts, additional tools are used to assess the short term evolution of world trade and its consistency with the GDP growth projection. This approach includes a bridge equation model based on a limited set of variables (world industrial production, export orders for the G6 economies, 2 technology indicators, oil prices and the Baltic dry index) and a dynamic factor model using an extended dataset (including a larger number of monthly series at world and country levels), see Guichard and Rusticelli (2. These models are used routinely during forecasting rounds and also for interim analyses. Secondly, a global equation linking world trade growth to world GDP growth is used to assess the consistency of world trade and world GDP forecasts drawing on the work of Cheung and Guichard (2. The latter are typically constructed for each country using a set of 5- 1. GDP or, more typically, industrial production. Both techniques have different roles to play in the OECD’s assessment methods. Key variables and relationships. In making the overall assessment of current and future economic performance in individual countries, a number of key variables and relationships are examined, broadly along the following lines: Domestic expenditures. Projections of private consumption and saving rates typically take into account real disposable income, household wealth, changes in the rate of inflation, monetary and financial conditions, and leading indicators of consumer confidence and retail sales. Business fixed investment is mainly assessed in relation to non- financial (sales, output and capacity utilisation) and financial (cash flow, monetary conditions and interest rates) variables. Business sentiment and survey information is also taken into account. Projections for residential construction typically take account of demographic trends, housing stocks, real income and financial conditions, but also draw on cyclical indicators for the construction sector. Projections of stockbuilding are usually made with reference to relevant stock- output and stock- sales ratios in relation to normal trends. Employment and other labour market trends are generally assessed on the basis of actual and projected activity. Important additional considerations relate to productivity trends, capacity constraints and costs. Unemployment rate projections are derived from employment and labour supply projections, with the latter assessed on the basis of demographic trends and participation rate assumptions. Wage and earnings assessments take into account a number of key factors, such as the pattern of current wage settlements data as a leading indicator. The assessment of domestic prices and inflation trends depends on unit costs, the strength of demand reflected by output gaps and foreign prices. Output gaps. The output gap is measured as the difference between actual and estimated potential GDP, in volume terms and in per cent of potential GDP. Output gaps are difficult to estimate and subject to substantial margins of error given that potential output and structural unemployment rates are generally unobservable variables. OECD work in this area generally follows a production function approach, taking into account the capital stock, changes in labour supply, factor productivities and underlying . The OECD’s NAIRU estimates are updated on the basis of the inflation modeling work described in Richardson et al (2. Turner et al. In making the forecasts, particular attention is paid to consistency at domestic and world levels, and to ensure that key accounting identities and relationships are observed, notably with respect to international trade and the balance of payments. Projections for export prices take account of domestic labour costs and import prices, as well as competitors' export prices, while import prices are derived as weighted averages of foreign and domestic prices. Investment income receipts and payments are set to reflect returns on stocks of external assets and liabilities, while international transfer debits and credit are exogenous, subject to consistency checks across countries. An important feature of the trade and balance of payments exercise is the need to ensure consistency across countries and regions and iterative procedures for maintaining balance at the world level. Agents can be assumed to look forward in some scenarios, but not in others. Financial markets are normally assumed to look forward and consumers are normally assumed to be myopic but react to changes in their (forward looking) financial wealth. Monetary policy is set according to rules, with defaults designed for speed. However, interest rate feedback rules can be changed, and their parameters adjusted. The structure of the NIGEM is designed to correspond to macroeconomic policy needs. Ni. GEM is a structured around the national income identity, can accommodate forward looking consumer behaviour and has many of the characteristics of a Dynamic Stochastic General Equilibrium (DSGE) model. Unlike a pure DSGE model, Ni. GEM is based on estimation using historical data. It thus strikes a balance between theory and data and enables using the NIGEM both for policy analysis and forecasting. Most countries in the OECD are modeled separately. The rest of the world is modeled through regional blocks: Latin America, Africa, East Asia, Developing Europe, OPEC and a Miscellaneous group mainly in West Asia. All models contain the determinants of domestic demand, export and import volumes, prices, current accounts and net assets, and the OECD countries are more complex than those of the non- OECD countries. A wide range of techniques are available for trying to understand the likely dynamics of new markets, running from simple extrapolation of current. Forecasting is the process of making predictions of the future based on past and. Statistical Forecasting. ![]() The core of each of these country models consists of a production function determining output in the long term; a wage- price block; a description of the government sector; consumption, personal income and wealth; international trade; and financial markets. We use a dynamic error- correction structure on the estimated equations, which allows the model to adjust gradually towards equilibrium in response to a shock. In some cases the speed of adjustment will depend on expectations as well as distance from equilibrium. Linkages in Ni. GEM take place through trade and competitiveness, interacting financial markets and international stocks of assets. The model is homogeneous in exchange rates, and exports demand equals imports across the world. Competitiveness acts as an important stabilising feedback on the model, as shifts in the domestic price level or the exchange rate feed into relative trade prices, allowing net trade to offset shifts in domestic demand. The use of econometric models in OECD's forecasting. Forecasting methods and analytical tools. Statistical indicator models are nonetheless limited in. Forecast Modeling capabilities in SAP APO vs. Tweets that mention Forecast Modeling capabilities in SAP APO vs. Link map of all the user guides and reference materials relevant for inventory forecasting and pricing optimization with Lokad. Statistical forecasting. Statistical Forecasting Methods. Multiple Regression Analysis: Used when two or more independent factors are involved-widely used for intermediate term. Statistical Methods for Forecasting Published Online. Summary; Statistical Methods for Forecasting Published Online. ![]() Fiscal analysis. 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In addition to fighting malware, the best antivirus programs now protect you from spyware, phishing, identity theft, threats delivered over USB and more. Although there are dozens of antivirus programs on the market, only a few consistently impress experts - - and it's usually a neck- and- neck race between two top companies, Bitdefender and Kaspersky. ![]() A listing of the order of events and other pertinent information for a public presentation. The presentation itself: a. Building an Anti-Virus engine by Markus Schmall last updated March 4, 2002: The article will describe the basic ideas, concepts, components and approaches involved in. This year, Bitdefender Antivirus Plus 2. Est. No annoying pop- ups, thanks to Autopilot mode. Rubenking's test at PCMag. Those include a file shredder, password manager and a Safe. Pay hardened web browser that provides an extra layer of protection for your online financial transactions. Phishing protection is outstanding. It addition to earning top honors at Tom's Guide, it earns the Editors' Choice award at PCMag. Finally, the antivirus engine used in both it and the company's full internet security suite delivers excellent antivirus protection and performance in independent tests at AV- Test. AV- Comparatives. Kaspersky Anti- Virus 2. Est. Kaspersky lacks some of Bitdefender's extra features, too - - there's no password manager, file shredder or hardened web browser for online banking and shopping. But at PCMag. com, Kaspersky does share the Editors' Choice award with Bitdefender - - although Kaspersky doesn't do as well blocking malware- hosting URLs (Kaspersky blocks 5. Bitdefender) or detecting phishing URLs (Bitdefender beats Kaspersky by 6 percentage points). Both Bitdefender and Kaspersky get mixed owner reviews, but not for failed antivirus protection. Owners say that works fine. 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