Estimating 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
Abstract
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.
Document Tags and Keywords
Keywords
Keywords are automatically generated using KeyBERT, a powerful and innovative
keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant
keywords.
By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the
text, highlighting the most significant topics and trends. This approach not only enhances searchability but
provides connections that go beyond potentially domain-specific author-defined keywords.
:
estimation,
econometric,
estimating,
aggregation,
quarterly,
aggregate,
trend,
regression,
autoregressive,
forecast
Tags
Tags are automatically generated using a pretrained language model from spaCy, which excels at
several tasks, including entity tagging.
The model is able to label words and phrases by part-of-speech,
including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are
identified to contain references to specific institutions, datasets, and other organizations.
:
Bureau of Labor Statistics,
Center for Economic Studies,
Bureau of Economic Analysis,
Establishment Micro Properties,
American Immigration Council,
Federal Reserve Bank
Similar Working Papers
Similarity between working papers are determined by an unsupervised neural
network model
know as Doc2Vec.
Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the
capture of semantic meaning in a way that relates to the context of words within the document. The model learns to
associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as
document classification, clustering, and similarity detection by preserving the order and structure of words. The
document vectors are compared using cosine similarity/distance to determine the most similar working papers.
Papers identified with 🔥 are in the top 20% of similarity.
The 10 most similar working papers to the working paper 'Estimating A Multivariate Arma Model with Mixed-Frequency Data: An Application to Forecasting U.S. GNP at Monthly Intervals' are listed below in order of similarity.
-
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 Applications as a Leading Economic Indicator?
May 2021
Working Paper Number:
CES-21-09R
How are applications to start new businesses related to aggregate economic activity? This paper explores the properties of three monthly business application series from the U.S. Census Bureau's Business Formation Statistics as economic indicators: all business applications, business applications that are relatively likely to turn into new employer businesses ('likely employers'), and the residual series -- business applications that have a relatively low rate of becoming employers ('likely non-employers'). Growth in applications for likely employers significantly leads total nonfarm employment growth and has a strong positive correlation with it. Furthermore, growth in applications for likely employers leads growth in most of the monthly Principal Federal Economic Indicators (PFEIs). Motivated by our findings, we estimate a dynamic factor model (DFM) to forecast nonfarm employment growth over a 12-month period using the PFEIs and the likely employers series. The latter improves the model's forecast, especially in the years following the turning points of the Great Recession and the COVID-19 pandemic. Overall, applications for likely employers are a strong leading indicator of monthly PFEIs and aggregate economic activity, whereas applications for likely non-employers provide early information about changes in increasingly prevalent self-employment activity in the U.S. economy.View Full Paper PDF
-
Working PaperThe Myth of Decline: A New Perspective on the Supply Chain and Changing Inventory-Sales Ratios
October 2004
Working Paper Number:
CES-04-18
There is a widely held perception that improved supply chain practices and new technologies have led to declines in the inventory-sales ratio. Our empirical analyses of 87 inventory-sales ratios in 45 manufacturing, wholesale distribution, and retail trade industries casts doubt on assumptions of widespread declines in these ratios. We find that less than half of the ratios showed statistically significant declines during the 12 year period from January 1992 through December 2003. Information technology may indeed have improved inventory management, but this improvement is not reflected in inventory-sales ratio data for many U.S. industries. Our detailed case study of the pharmaceutical supply chain also offers additional insights by showing how relevant technological investments led to an extended period in which inventory-to-sales ratios increased.View Full Paper PDF
-
Working PaperThe Contribution Of Establishment Births And Deaths To Employment Growth
April 1998
Working Paper Number:
CES-98-05
The purpose of this paper is to examine how establishment births and deaths contribute to job creation, job destruction, and net employment growth at different frequencies of measurement. The longitudinal data are constructed from quarterly unemployment insurance microdata, and are essentially a census of establishments in all industries. Defining establishment births and deaths turns out to be an exercise in how to use cross-sectional administrative data for longitudinal research purposes. The analysis of job flows indicates that the frame is relatively small but certainly non-trivial, whereas births and deaths account for roughly half of all jobs created and destroyed on a triennial time frame. Net Employment GrowthView Full Paper PDF
-
Working PaperJOB-TO-JOB (J2J) Flows: New Labor Market Statistics From Linked Employer-Employee Data
September 2014
Working Paper Number:
CES-14-34
Flows of workers across jobs are a principal mechanism by which labor markets allocate workers to optimize productivity. While these job flows are both large and economically important, they represent a significant gap in available economic statistics. A soon to be released data product from the U.S. Census Bureau will fill this gap. The Job-to-Job (J2J) flow statistics provide estimates of worker flows across jobs, across different geographic labor markets, by worker and firm characteristics, including direct job-to-job flows as well as job changes with intervening nonemployment. In this paper, we describe the creation of the public-use data product on job-to-job flows. The data underlying the statistics are the matched employer-employee data from the U.S. Census Bureau's Longitudinal Employer-Household Dynamics program. We describe definitional issues and the identification strategy for tracing worker movements between employers in administrative data. We then compare our data with related series and discuss similarities and differences. Lastly, we describe disclosure avoidance techniques for the public use file, and our methodology for estimating national statistics when there is partially missing geography.View Full Paper PDF
-
Working PaperLabor Reallocation, Employment, and Earnings: Vector Autoregression Evidence
January 2017
Working Paper Number:
CES-17-11R
Analysis of the labor market has given increasing attention to the reallocation of jobs across employers and workers across jobs. However, whether and how job reallocation and labor market 'churn' affects the health of the labor market remains an open question. In this paper, we present time series evidence for the U.S. 1993-2013 and consider the relationship between labor reallocation, employment, and earnings using a vector autoregression (VAR) framework. We find that an increase in labor market churn by 1 percentage point predicts that, in the next quarter, employment will increase by 100 to 560 thousand jobs, lowering the unemployment rate by 0.05 to 0.25 percentage points. Job destruction does not predict future changes in employment but a 1 percentage point increase in job destruction leads to an increase in future unemployment 0.14 to 0.42 percentage points. We find mixed results on the relationship between labor reallocation rates and earnings: we nd that, especially for earnings derived from administrative records data, a 1 percentage point increase to either job destruction or churn leads to increased earnings of less than 2 percent. Results vary substantially depending on the earnings measure we use, and so the evidence inconsistent on whether productivity-enhancing aspects of churn and job destruction provide earnings gains for workers in aggregate. Our findings on churn leading to increased employment and a lower unemployment rate are consistent with models of replacement hiring and vacancy chains.View Full Paper PDF
-
Working PaperTransitions in Welfare Participation and Female Headship
February 2004
Working Paper Number:
CES-04-01
This study uses data from the 1990, 1992, 1993, and 1996 panels of the Survey of Income and Program Participation to examine how welfare policies and local economic conditions contribute to women's transitions into and out of female headship and into and out of welfare participation. It also examines whether welfare participation is directly associated with longer spells of headship. The study employs a simultaneous hazards approach that accounts for unobserved heterogeneity in all of its transition models and for the endogeneity of welfare participation in its headship model. The estimation results indicate that welfare participation significantly reduces the chances of leaving female headship. The estimates also reveal that more generous welfare benefits contribute indirectly to headship by increasing the chances that mothers will enter welfare. More generous Earned Income Tax Credit benefits are associated with longer spells of headship, nonheadship, and welfare participation and nonparticipation. Other measures of welfare policies, including indicators for the adoption of welfare waivers and the implementation of Temporary Assistance for Needy Families programs, are generally not significantly associated with headship or welfare receipt. Better economic opportunities are estimated to increase headship but reduce welfare participation among unmarried mothers.View Full Paper PDF
-
Working PaperDynamically Consistent Noise Infusion and Partially Synthetic Data as Confidentiality Protection Measures for Related Time Series
July 2012
Working Paper Number:
CES-12-13
The Census Bureau's Quarterly Workforce Indicators (QWI) provide detailed quarterly statistics on employment measures such as worker and job flows, tabulated by worker characteristics in various combinations. The data are released for several levels of NAICS industries and geography, the lowest aggregation of the latter being counties. Disclosure avoidance methods are required to protect the information about individuals and businesses that contribute to the underlying data. The QWI disclosure avoidance mechanism we describe here relies heavily on the use of noise infusion through a permanent multiplicative noise distortion factor, used for magnitudes, counts, differences and ratios. There is minimal suppression and no complementary suppressions. To our knowledge, the release in 2003 of the QWI was the first large-scale use of noise infusion in any official statistical product. We show that the released statistics are analytically valid along several critical dimensions { measures are unbiased and time series properties are preserved. We provide an analysis of the degree to which confidentiality is protected. Furthermore, we show how the judicious use of synthetic data, injected into the tabulation process, can completely eliminate suppressions, maintain analytical validity, and increase the protection of the underlying confidential data.View Full Paper PDF
-
Working PaperEstimation of Job-to-Job Flow Rates under Partially Missing Geography
September 2012
Working Paper Number:
CES-12-29
Integration of data from different regions presents challenges for the calculation of entitylevel longitudinal statistics with a strong geographic component: for example, movements between employers, migration, business dynamics, and health statistics. In this paper, we consider the estimation of worker-level employment statistics when the geographies (in our application, US states) over which such measures are defined are partially missing. We focus on the recent pilot set of job-to-job flow statistics produced by the US Census Bureau's Longitudinal Employer- Household Dynamics (LEHD) program, which measure the frequency of worker movements between jobs and into and out of nonemployment. LEHD's coverage of the labor force gradually increases during the 1990s and 2000s because some states have a longer time series than others, so employment transitions involving missing states are only partially or not at all observed. We propose and implement a method for estimating national-level job-to-job flow statistics that involves dropping observed states to recover the relationship between missing states and directly tabulated job-to-job flow rates. Using the estimated relationship between the observable characteristics of the missing states and changes in the employment measures, we provide estimates of the rates of job-to-job, and job-to-nonemployment, job-to-nonemploymentto- job flows were all states uniformly available.View Full Paper PDF
-
Working PaperSorting Between and Within Industries: A Testable Model of Assortative Matching
January 2017
Working Paper Number:
CES-17-43
We test Shimer's (2005) theory of the sorting of workers between and within industrial sectors based on directed search with coordination frictions, deliberately maintaining its static general equilibrium framework. We fit the model to sector-specific wage, vacancy and output data, including publicly-available statistics that characterize the distribution of worker and employer wage heterogeneity across sectors. Our empirical method is general and can be applied to a broad class of assignment models. The results indicate that industries are the loci of sorting-more productive workers are employed in more productive industries. The evidence confirm that strong assortative matching can be present even when worker and employer components of wage heterogeneity are weakly correlated.View Full Paper PDF
