Understanding and comparing factor-based forecasts

by Boivin, Jean

Publisher: National Bureau of Economic Research in Cambridge, MA

Written in English
Published: Downloads: 484
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Subjects:

  • Economic forecasting -- Statistical methods.

Edition Notes

Forecasting Factor and Smart Beta Returns (Hint: History Is Worse than Useless) Rob Arnott, Noah Beck, and Vitali Kalesnik, PhD In a series of articles we published in , 1 we show that relative .   Nowcasting norwegian GDP: the role of asset prices in a small open economy Ng S () Understanding and comparing factor-based forecasts. Int J Central Bank 1(3): – Google Scholar; Boivin J, Ng S () Are more data always better for factor analysis?. Vassalou M () Can book Cited by: CHAPTER 7 • Demand Forecasting in a Supply Chain harder to forecast revenue for a given product with the same degree of accuracy. The key difference among the three forecasts is the degree of File Size: 1MB. ARIMA models provide another approach to time series forecasting. Exponential smoothing and ARIMA models are the two most widely used approaches to time series forecasting, and provide .

when xt−1 is based on the observed lagged behavior of real oil prices, U.S. nominal interest rates, or U.S. GDP growth rates. Those tests for predictability are summarized by the p-value associated with the . By defining and monitoring standard product costs, you can measure the company's current manufacturing performance and compare it to the standard (target) costs. Product costing provides .   Budgeting and financial forecasting should work in tandem with each other. For example, both short-term and long-term financial forecasts could be used to help create and update a Author: Steven Nickolas. Comparing and Contrasting Forecasting Methods Companies use forecasting to help decide how to best spend funds for the next year, to predict if expansion is needed, to plan for how much of each product .

Compare and contrast forecasting methods (e.g. seasonal, Delphi, technological, time series). Explain how a 'Cable TV Company' uses one or more of these methods to forecast demand under conditions . understanding insurers’ financial information and its implications for the risk and value of insurance companies. To facilitate an informed use of insurers’ financial reports, this manuscript reviews the .

Understanding and comparing factor-based forecasts by Boivin, Jean Download PDF EPUB FB2

Understanding and Comparing Factor-Based Forecasts [Boivin, Jean, Ng, Serena, International Journal of Central Banking] on *FREE* shipping on qualifying offers. Understanding and Comparing Factor-Based ForecastsCited by: vestors, or consumers, are often based on the forecasts of relevant macroeconomic variables.

The accuracy of these forecasts can thus have important repercussions. Understanding and comparing factor-based forecasts book theory, the optimal forecast of. The idea is to use the common factors estimated from a large panel of data to help forecast the series of interest.

This paper assesses the extent to which the forecasts are influenced by (i) how the factors are estimated and/or (ii) how the forecasts. Boivin, Jean and Ng, Serena (): Understanding and Comparing Factor-Based Forecasts. Published in: International Journal of Central Banking, Vol.

Volume, No. Number 3 (1 December Cited by: The idea is to use the common factors estimated from a large panel of data to help forecast the series of interest. This paper assesses the extent to which the forecasts are influenced by (i) how the factors are estimated, and/or (ii) how the forecasts.

Understanding and Comparing Factor-Based Forecasts. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Understanding and Comparing Factor-Based Forecasts.

Jean Boivin (Columbia University and NBER) and Serena Ng (University of Michigan) Forecasting using "diffusion indices" has received a good. The idea is to use the common factors estimated from a large panel of data to help forecast the series of interest.

This paper assesses the extent to which the forecasts are influenced by (i) how the factors are estimated and/or (ii) how the forecasts Author: Jean Boivin and Serena Ng.

Portfolio Risk Analysis provides an insightful and thorough overview of financial risk modeling, with an emphasis on practical applications, empirical reality, and historical perspective.

Beginning with mean Cited by: Understanding and Comparing Factor-Based Forecasts. Jean Boivin and Serena Ng () MPRA Paper from University Library of Munich, Germany.

Abstract: Forecasting using "diffusion indices" has Cited by: 2 Forecasting the Unpredictable: A Review of Forecasts on Terrorism ( ) security foresight studies have beco me widely used in policymaking. While preparing your financial projections, it’s most important to be as realistic as possible.

You don’t want to over- or underestimate the revenue your business will generate. It’s a good idea to have a trusted friend or business partner review your financial projections/5(6). Get this from a library. Understanding and comparing factor-based forecasts.

[Jean Boivin; Serena Ng; National Bureau of Economic Research.] -- "Forecasting using 'diffusion indices' has received a good. that are known through retrospective analysis to outperform the market average.

Two popular factors are the book value normalized by market capitalization (book. to-market) and the operating income Cited by: Understanding and Comparing Factor-Based Forecasts International Journal of Central Banking,1, (3) View citations () See also Working Paper () Monetary policy in a data-rich.

Companies use forecasting to help them develop business strategies. Financial and operational decisions are made based on economic conditions and how the future looks, albeit uncertain. Seminar paper from the year in the subject Business economics - Investment and Finance, grade: 9, Maastricht University (Economics and Sociology Faculty), 5 entries in the bibliography, language: English, abstract: In all aspects of our daily live, we seek to anticipate or forecast.

Given the estimates of the common factors, the nowcast of GDP can hence be computed by estimating the coefficients and of Eq.

(2) by OLS regression of GDP, on the quarterly common factors, using Cited by: forecast is being calculated, the 2nd term is two periods old, and so on up to the mth term. Hence, the “average age” of the data in the forecast is (m+1)/2.

This is the amount by which the forecasts will File Size: KB. Profit performance reports prepared for a business’s managers typically are called P&L (profit and loss) reports. These reports are prepared as frequently as managers need them, usually monthly or. A long-term forecast will provide valuable output to the management for their strategic business plan whereas short term forecast is generally is done for operational and day to day business needs.

A profit and loss statement (P&L), or income statement or statement of operations, is a financial report that provides a summary of a company's revenues, expenses, and profits/losses over a given period.

(3) Comparing variable-based and factor-based models (4) Taxonomy of forecast errors in factor models (5) Forecast failure in equilibrium-correction models (6) Model selection with both factors and variables (7) Forecasting US GDP and inflation Conclusions Castle, Clements & Hendry Forecasting by factors.

In addition to the final vintage of GDP used above, the forecasts are evaluated by comparing them with the GDP, as obtained 3 and 12 months after the initial release of the GDP.

Ng tanding and comparing factor-based forecasts Cited by: Evaluating and Comparing Possibly Misspeci–ed Forecasts Andrew J. Patton Duke University First version: 27 September This version: 27 March Abstract This paper considers the evaluation of forecasts. I compare the forecasts using MSFE, perform tests of predictive accuracy and forecast encompassing to determine whether unemployment rate is useful in predicting inflation rate.

Because. The main purpose of this research is to show the diversity of statistical methods that could be used to assess and compare forecasts accuracy. Some of the statistical approaches were not used before in literature to evaluate the forecasts accuracy.

The different methods applied to compare the accuracy of the USA inflation forecasts. Model Evaluation by Comparison of Model-Based Predictions and Measured Values Hugh G.

Gauch, Jr., J. Gene Hwang, and Gary W. Fick* ABSTRACT dictive accuracy of a model, even when such is the. To set the stage for using the mean model for forecasting, let’s review some of the most basic concepts of statistics. Let: X = a random variable, with its individual values denoted by x 1, x 2, etc.

N = size of File Size: KB. In this paper, simple regression estimates and factor-based models are utilised to produce forecasts for Bahrain quarterly gross domestic product growth. Using simulated out-of-sample experiments, we assess and compare Cited by: 1. I’ve been able to spend a short amount of time with Microsoft's new inch Surface Book 2.

That’s not enough time to get to grips with some of the finer details around the high-end. On a periodic basis, publicly traded companies are required to report fundamentals: financial data such as revenue, operating income, debt, among others.

These data points provide Cited by: I started out looking for a way to test the difference between MSPE between two models (Question here), when (thanks to @Richard Hardy) I ended up reading a paper of Diebold regarding the Diebold-Mariano test (Comparing .