Stock and watson diffusion index

notion of diffusion indexes developed at the National Bureau of Economic Research (NBER), Stock and Watson called their method the diffusion index (DI) forecast. This DI forecast approach can be justified under the framework of the dynamic factor model originally considered by Sargent and Sims (1977) and Geweke (1977). Diffusion Pharmaceuticals Inc. Common Stock (DFFN) Stock Quotes - Nasdaq offers stock quotes & market activity data for US and global markets. This paper extends the diffusion index (DI) forecast approach of Stock and Watson (1998, 2002) to the case of possibly nonlinear dynamic factor models. When the number of series is large, a two-step procedure based on the method of principal components is useful since it allows wide variety of nonlinearity in the factors.

This paper extends the diffusion index (DI) forecast approach of Stock and Watson (1998, 2002) to the case of possibly nonlinear dynamic factor models. When the number of series is large, a two-step procedure based on the principal components method is useful since it allows the wide variety of the nonlinearity in the factors. mate a time series of factors (the diffusion indexes),8Fb t9 T tD1. Second, the estimators†O h1 ‚O h4L5andƒO h4L5are obtained by regressingytC1onto a constant,Fb tandyt(and lags). The forecastofyh TChis then formed as† Oh C‚O h4L5Fb T ƒh4L5yT. Stock and Watson (1998) developed theoretical results for this two-step procedure applied to (2.3) and (2.4). We use the Stock-Watson diffusion index methodology to summarize the information contained in a wide set of monthly series (published in the Statistical Bulletin of the Bank of Spain) by means of a reduced number of factors. If the diffusion index is falling as the stock index falls, that helps confirm the downtrend. It means fewer stocks are advancing, which makes sense since the stock index is declining (and the stocks within the stock index are declining). If the stock index is falling, and the diffusion index starts rising, James H. Stock is Professor, Kennedy School of Government, Harvard University, Cambridge, MA 02138, and the National Bureau of Economic Research (E-mail: james-stock@harvard.edu). Mark W. Watson is Profes- sor, Department of Economics and Woodrow Wilson School, Princeton

Following the ideas presented by Stock and Watson (1998), a linear diffusion index (DI) model to produce one step ahead forecasts can be represented as:.

Welcome! Welcome to the Companion Website for Stock and Watson's Introduction to Econometrics, Third Edition and Introduction to Econometrics, Third Edition Update! Please use the links on the left to access the student resources. This site contains: Data for Empirical Exercises and Test Bank (new data provided for the Third Edition Update) SWfore: Stock-Watson Diffusion Index Forecasts In MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models Description Usage Arguments Details Value Author(s) References Stock, James H. and Watson, Mark W., Diffusion Indexes (August 1998). NBER Working Paper No. w6702. If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Stock J, Watson MW. Macroeconomic Forecasting Using Diffusion Indexes. Journal of Business and Economic Statistics. 2002;20 (2) :147–162. notion of diffusion indexes developed at the National Bureau of Economic Research (NBER), Stock and Watson called their method the diffusion index (DI) forecast. This DI forecast approach can be justified under the framework of the dynamic factor model originally considered by Sargent and Sims (1977) and Geweke (1977).

Diffusion Indexes James H. Stock, Mark W. Watson. NBER Working Paper No. 6702 Issued in August 1998 NBER Program(s):Economic Fluctuations and Growth Program This paper considers forecasting a single time series using more predictors than there are time series observations.

27 Jun 2019 In the stock market, a rising diffusion index means there is an increasing number of stocks moving higher, which is positive for stock indexes 

In this paper we propose a new methodology in improving the Diffusion Index forecasting model (Stock and Watson, 2002a, 2002b) using hard thresholding with robust KVB statistic for regression hypothesis tests (Kiefer et al., 2000). The new method yields promising results in the context of long forecasting horizons and existence

6 Jul 2009 The diffusion index referred here should not be confused with the so-called Stock -Watson diffusion index which uses dynamic factor models to  12 Jan 2017 mension Reduction, Diffusion Index. data, as outlined in the early work of Stock and Watson (1998) [69] and in subsequent studies by. information. The diffusion index model is a dynamic factor model, exploited by. Stock and Watson (2002a) for forecasting in (e.g.) the form: yt = β. . Ft−1 + εt−1,. series into composite indexes of leading, coincident, and lagging economic indicators. In response to these criticisms, Stock and Watson (1989) first introduced claims, or the diffusion index of slower deliveries are stationary or have only 

When yt is a scalar, (5) and (1) become the diffusion index forecasting model of. Stock & Watson (2002b). Clearly, each xit is a noisy predictor for yt+h. Because 

Stock and Watson: Macroeconomic Forecasting Using Diffusion Indexes. 149. United States. The complete dataset spans 1959:1 to 1998:12. Four of these eight  Published: Stock, James H. and Mark W. Watson. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business and Economic Statistics, 2002,  8 Aug 2000 Watson. Princeton University - Woodrow Wilson School of Public and International Affairs; National Bureau of Economic Research (NBER). Date  1 Jan 2012 Macroeconomic Forecasting Using Diffusion Indexes. James H Stock  Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc. Handle: RePEc:nbr:nberwo  In this section, following the classical paper of Stock and Watson (2002b) , we present in details the empirical performance of the diffusion index method, which   Diffusion indexes based on dynamic factors have recently been advocated by Stock and Watson. (1998), and further used to perform forecasting tests by the same 

Stock and Watson: Macroeconomic Forecasting Using Diffusion Indexes 153. Table 3. Simulated Out-of-Sample Forecasting Results: Price Inflation, 12-Month Horizon CPI Consumption deflator CPI exc. food & energy Producer price index Forecast method Rel. MSE & Rel. Diffusion Indexes James H. Stock, Mark W. Watson. NBER Working Paper No. 6702 Issued in August 1998 NBER Program(s):Economic Fluctuations and Growth Program This paper considers forecasting a single time series using more predictors than there are time series observations. Stock, James H. and Watson, Mark W., Diffusion Indexes (August 1998). NBER Working Paper No. w6702. If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday.