Papers Containing Keywords(s): 'forecast'
The following papers contain search terms that you selected. From the papers listed below, you can navigate to the PDF, the profile page for that working paper, or see all the working papers written by an author. You can also explore tags, keywords, and authors that occur frequently within these papers.
See Working Papers by Tag(s), Keywords(s), Author(s), or Search Text
Click here to search again
Frequently Occurring Concepts within this Search
Authors
No authors occur more than twice in this search.
Viewing papers 1 through 10 of 10
-
Working PaperFatal Errors: The Mortality Value of Accurate Weather Forecasts
June 2023
Working Paper Number:
CES-23-30
We provide the first revealed preference estimates of the benefits of routine weather forecasts. The benefits come from how people use advance information to reduce mor tality from heat and cold. Theoretically, more accurate forecasts reduce mortality if and only if mortality risk is convex in forecast errors. We test for such convexity using data on the universe of mortality events and weather forecasts for a twelve-year period in the U.S. Results show that erroneously mild forecasts increase mortality whereas erro neously extreme forecasts do not reduce mortality. Making forecasts 50% more accurate would save 2,200 lives per year. The public would be willing to pay $112 billion to make forecasts 50% more accurate over the remainder of the century, of which $22 billion reflects how forecasts facilitate adaptation to climate change.View Full Paper PDF
-
Working PaperBuilding the Census Bureau Index of Economic Activity (IDEA)
March 2023
Working Paper Number:
CES-23-15
The Census Bureau Index of Economic Activity (IDEA) is constructed from 15 of the Census Bureau's primary monthly economic time series. The index is intended to provide a single time series reflecting, to the extent possible, the variation over time in the whole set of component series. The component series provide monthly measures of activity in retail and wholesale trade, manufacturing, construction, international trade, and business formations. Most of the input series are Principal Federal Economic Indicators. The index is constructed by applying the method of principal components analysis (PCA) to the time series of monthly growth rates of the seasonally adjusted component series, after standardizing the growth rates to series with mean zero and variance 1. Similar PCA approaches have been used for the construction of other economic indices, including the Chicago Fed National Activity Index issued by the Federal Reserve Bank of Chicago, and the Weekly Economic Index issued by the Federal Reserve Bank of New York. While the IDEA is constructed from time series of monthly data, it is calculated and published every business day, and so is updated whenever a new monthly value is released for any of its component series. Since release dates of data values for a given month vary across the component series, with slight variations in the monthly release date for any one component series, updates to the index are frequent. It is unavoidably the case that, at almost all updates, some of the component series lack observations for the current (most recent) data month. To address this situation, component series that are one month behind are predicted (nowcast) for the current index month, using a multivariate autoregressive time series model. This report discusses the input series to the index, the construction of the index by PCA, and the nowcasting procedure used. The report then examines some properties of the index and its relation to quarterly U.S. Gross Domestic Product and to some monthly non-Census Bureau economic indicators.View Full Paper PDF
-
Working PaperBusiness-Level Expectations and Uncertainty
December 2020
Working Paper Number:
CES-20-41
The Census Bureau's 2015 Management and Organizational Practices Survey (MOPS) utilized innovative methodology to collect five-point forecast distributions over own future shipments, employment, and capital and materials expenditures for 35,000 U.S. manufacturing plants. First and second moments of these plant-level forecast distributions covary strongly with first and second moments, respectively, of historical outcomes. The first moment of the distribution provides a measure of business' expectations for future outcomes, while the second moment provides a measure of business' subjective uncertainty over those outcomes. This subjective uncertainty measure correlates positively with financial risk measures. Drawing on the Annual Survey of Manufactures and the Census of Manufactures for the corresponding realizations, we find that subjective expectations are highly predictive of actual outcomes and, in fact, more predictive than statistical models fit to historical data. When respondents express greater subjective uncertainty about future outcomes at their plants, their forecasts are less accurate. However, managers supply overly precise forecast distributions in that implied confidence intervals for sales growth rates are much narrower than the distribution of actual outcomes. Finally, we develop evidence that greater use of predictive computing and structured management practices at the plant and a more decentralized decision-making process (across plants in the same firm) are associated with better forecast accuracy.View Full Paper PDF
-
Working PaperSlow to Hire, Quick to Fire: Employment Dynamics with Asymmetric Responses to News
January 2017
Working Paper Number:
CES-17-15
Concave hiring rules imply that firms respond more to bad shocks than to good shocks. They provide a united explanation for several seemingly unrelated facts about employment growth in macro and micro data. In particular, they generate countercyclical movement in both aggregate conditional 'macro' volatility and cross-sectional 'micro' volatility as well as negative skewness in the cross section and in the time series at different level of aggregation. Concave establishment level responses of employment growth to TFP shocks estimated from Census data induce significant skewness, movements in volatility and amplification of bad aggregate shocks.View Full Paper PDF
-
Working PaperSlow to Hire, Quick to Fire: Employment Dynamics with Asymmetric Responses to News
January 2015
Working Paper Number:
CES-15-02
We study the distribution of employment growth when hiring responds more to bad shocks than to good shocks. Such a concave hiring rule endogenously generates higher moments observed in establishment-level Census data for both the cross section and the time series. In particular, both aggregate conditional volatility ("macro-volatility") and the cross-sectional dispersion of employment growth ("micro-volatility") are countercyclical. Moreover, employment growth is negatively skewed in the cross section and time series, while TFP is not. The estimated response of employment growth to TFP innovations is su ciently concave to induce signi cant skewness as well as movements in volatility of employment growth.View Full Paper PDF
-
Working PaperHOW IMPORTANT ARE SECTORAL SHOCKS
September 2014
Working Paper Number:
CES-14-31
I quantify the contribution of sectoral shocks to business cycle fluctuations in aggregate output. I develop a multi-industry general equilibrium model in which each industry employs the material and capital goods produced by other sectors, and then estimate this model using data on U.S. industries sales, output prices, and input choices. Maximum likelihood estimates indicate that industry-specific shocks account for nearly two-thirds of the volatility of aggregate output, substantially larger than previously assessed. Identification of the relative importance of industry-specific shocks comes primarily from data on industries intermediate input purchases, data that earlier estimations of multi-industry models have ignored.View Full Paper PDF
-
Working PaperFLUCTUATIONS IN UNCERTAINTY
March 2014
Working Paper Number:
CES-14-17
This review article tries to answer four questions: (i) what are the stylized facts about uncertainty over time; (ii) why does uncertainty vary; (iii) do fluctuations in uncertainty matter; and (iv) did higher uncertainty worsen the Great Recession of 2007-2009? On the first question both macro and micro uncertainty appears to rise sharply in recessions. On the second question the types of exogenous shocks like wars, financial panics and oil price jumps that cause recessions appear to directly increase uncertainty, and uncertainty also appears to endogenously rise further during recessions. On the third question, the evidence suggests uncertainty is damaging for short-run investment and hiring, but there is some evidence it may stimulate longer-run innovation. Finally, in terms of the Great Recession, the large jump in uncertainty in 2008 potentially accounted for about one third of the drop in GDP.View Full Paper PDF
-
Working PaperJob Reallocation And The Business Cycle: New Facts An Old Debate
September 1998
Working Paper Number:
CES-98-11
This paper provides new facts on the nature of job reallocation over the business cycle, and addresses the question of whether reallocation causes recessions or recessions cause reallocation. Although we do not resolve the question of causality, two general findings emerge that advance our understanding of job reallocation and business cycles. First, much of the cyclical fluctuation in gross job flows occurs in larger plants with relatively moderate employment growth that tends to be transitory, especially at medium-term horizons (up to five years). Unusually large employment growth rates, especially plant startups and shutdowns, are primarily small-plant phenomena and tend to be permanent, less cyclical, and occur later in recessions. Further, high job flow rates occur primarily in plants previously experiencing sharp employment contractions or expansions. Second, key variables that should determine the allocation factors of production across plants and sectors do in fact appear to be related to gross job flows, particularly job destruction. Relative prices, productivity, and investment exhibit time series correlations with job reallocation that suggest that allocative driving forces may contribute significantly to business cycle fluctuations.View Full Paper PDF
-
Working PaperThe Choice of Input-Out Table Embedded in Regional Econometric Input-Out Models
January 1994
Working Paper Number:
CES-94-01
In this paper we investigate the role of input-output data source in the regional econometric input-output models. While there has been a great deal of experimentation focused on the accuracy of alternative methods for estimating regional input-output coefficients, little attention has been directed to the role of accuracy when the input-output system is nested within a broader accounting framework. The issues of accuracy were considered in two contexts, forecasting and impact analysis focusing on a model developed for the Chicago Region. We experimented with three input-output data sources: observed regional data, national input-output, and randomly generated input-output coefficients. The effects of different sources of input-output data on regional econometric input-output model revealed that there are significant differences in results obtained in impact analyses. However, the adjustment processes inherent in the econometric input-output system seem to mute the initial differences in input- output data when the model is used for forecasting. Since applications of these types of models involve both impact and forecasting exercises, there would still seem to be a strong motivation for basing the system on the most accurate set of input-output accounts.View Full Paper PDF
-
Working PaperEstimating A Multivariate Arma Model with Mixed-Frequency Data: An Application to Forecasting U.S. GNP at Monthly Intervals
July 1990
Working Paper Number:
CES-90-05
This paper develops and applies a method for directly estimating a multivariate, autoregressive moving-average (ARMA) model with mixed-frequency, time-series data. Unlike standard, single-frequency methods, the method does not require the data to be transformed to a single frequency (by temporally aggregating higher-frequency data to lower frequencies for interpolating lower-frequency data to higher frequencies) or the model to be restricted by frequency. Subject to computational constraints, the method can handle any number of variable and frequencies. In addition, variable can be treated as temporally aggregated and observed with errors and delays. The key to the method is to view lower-frequency data as periodically missing and to use the missing-data variant of the Kalman filter. In the application, a bivariate, ARMA model is estimated with monthly observations on total employment and quarterly observations on real GNP, in the U.S., for January 1958 to December 1978. The estimated model is, then, used to compute monthly forecasts of the variables for 1 to 12 months ahead, for January 1979 to December 1988. Compared with GNP forecasts, in particular, for similar periods produced by established econometric and time series models, present GNP forecasts are generally more accurate for 1 to 4 months ahead and about equally or slightly less accurate for 5 to 12 months ahead. The application, thus, shows that the present method is tractable and able to effectively exploit cross-frequency sample information, in ARMA estimate and forecasting, which standard methods cannot exploit at all.View Full Paper PDF
1
Total Results: 10
