A dynamic factor analysis of business cycle on firm

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A dynamic factor analysis of business cycle on firm

Find out more. Finally, they tested the predictive power of the term spread on the NBER recession indicator. Based on the average lead time established in prior research, they estimated a lag of 12 months as the time it takes for mainstream markets to price in yield-curve inversions. The shortest recession, which lasted six months, started in Januaryand the longest recession, which lasted 18 months, started in December The median recession length was 10 months.

Thus, the authors measured the cumulative returns for 10 months following the start of an NBER-designated recession, and then took the average of the cumulative returns for each factor across the 10 recessions. The term spread between three-month Treasury bills and year Treasury notes is used to determine when an inversion starts—e.

There have been eight inversions and seven recessions since An inverted yield curve has successfully forecasted, within six quarters, six of those seven recessions. A false positive occurred in when an inversion was not followed by a recession within six quarters.

The most recent inversion, in Januaryalso resulted in another false positive. It was followed by a recession—but in Decembermore than six quarters later. While both false positives did not successfully predict a recession within six quarters, it was nevertheless true that, in both cases, the economy slowed. As you can see, the best performer in a recession is CMA, the investment factor, which delivered an The second-best performer in a recession was HML, the value factor, which delivered a The third-best performer in a recession is MOM, the momentum factor, which delivered an In fact, momentum, though not the highest performer, remains consistently positive across all four stages.

The market-beta factor underperforms in a recession, but far outperforms all the other factors in each of the other three stages, delivering outsized cumulative returns in comparison. The most important takeaway should be that, because different factors outperform at different stages, diversification across factors—not concentrating risk in a single factor—is the prudent strategy.

We can see the benefits of diversifying across factors in the following table, which shows the correlation of returns among the six factors across all economic stages. With the exception of the correlation between the value and investment factors, the correlations are all low-to-negative.

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However, correlations are not static. They change depending on the economic regime. This demonstrates the benefits to value investors of adding exposure to the profitability factor. In early-stage recovery, the negative correlation of Similarly, during that stage, the low correlation of 0. Finally, the Finally, in very-late-stage recovery, the momentum factor has three of its relationships switch signs.To browse Academia.

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Skip to main content. Log In Sign Up. Download Free PDF. The US business cycle, dynamic factor analysis vs.

a dynamic factor analysis of business cycle on firm

Martin Uebele. Business Cycle, Dynamic Factor Analysis vs. We employ dynamic factor analysis as an alternative to reconstructed national accounts. While we can generate evidence of postwar moderation relative to pre, this evidence is not robust to structural change, imple- mented by time-varying factor loadings. However, we find moderation in the nominal series, and reproduce the standard moderation since the s. Keywords: U. Martin Uebele acknowledges financial support from DekaBank.

Contact: a. While there is broad agreement on the business cycle turning points, the issue of volatility is still not fully resolved, as different available estimates yield contradictory results.

Was the wartime boom of the early s really so strong? And has the U. Researchers have disagreed on the severity of the downturn after World War I as well as on the other two questions. This view was challenged in a series of papers by Romer, who argued that postwar sta- bilization relative to the decades before World War I was an artifact of the historical output and unemployment data.

Most of the debate evolved around two rivaling such series and their implications for U. Balke and Gordonmodified a popular GNP series originating from the Commerce Department, for which they produced a widely used quarterly interpolation.

No document with DOI "10.1.1.634.1958"

The high volatility of this series before World War I, compared to the rather moderate fluctuations of postwar GNP, is what shaped conventional wisdom in the s. Romerchallenged this view based on a revision of the alternative series of Kendrickwhich she argued was less prone to spurious volatility. However, her own calculations have been criticized for depending on assumptions which are not empirically testable given the lack of historical GNP data, see Lebergott The present paper offers an alternative but complementary approach to measur- ing the volatility of the U.

a dynamic factor analysis of business cycle on firm

We draw on the growing literature on diffusion indices using a term of Stock and Watson of economic activity, which are distilled from a large panel of disaggregate time se- ries using dynamic factor analysis DFA. Stock and Watson developed an unobserved component model for disaggregate series representing the U.

The same issues loom large with historical data.

Disaggregate series are often abundant for historical periods, but usually do not match national accounting categories well, and the information needed for proper aggregation is incomplete. As a consequence, proxies have to be used, which can be controversial as mentioned above.Dynamic factor models explicitly model the transition dynamics of the unobserved factors, and so are often applied to time-series data.

Although the estimation and use of coincident indices for example the Index of Coincident Economic Indicators pre-dates dynamic factor models, in several influential papers Stock and Watsonused a dynamic factor model to provide a theoretical foundation for them. Below, we follow the treatment found in Kim and Nelsonof the Stock and Watson model, to formulate a dynamic factor model, estimate its parameters via maximum likelihood, and create a coincident index.

The coincident index is created by considering the comovements in four macroeconomic variables versions of these variables are available on FRED ; the ID of the series used below is given in parentheses :. In all cases, the data is at the monthly frequency and has been seasonally adjusted; the time-frame considered is - Stock and Watson report that for their datasets, they could not reject the null hypothesis of a unit root in each series so the series are integratedbut they did not find strong evidence that the series were co-integrated.

As a result, they suggest estimating the model using the first differences of the logs of the variables, demeaned and standardized. The variance of the factor error term is set to the identity matrix to ensure identification of the unobserved factors. This model can be cast into state space form, and the unobserved factor estimated via the Kalman filter.

The likelihood can be evaluated as a byproduct of the filtering recursions, and maximum likelihood estimation used to estimate the parameters. The specific dynamic factor model in this application has 1 unobserved factor which is assumed to follow an AR 2 process.

This model can be formulated using the DynamicFactor model built-in to statsmodels. In particular, we have the following specification:. AR 2 processes. Once the model is created, the parameters can be estimated via maximum likelihood; this is done using the fit method. Note : recall that we have demeaned and standardized the data; this will be important in interpreting the results that follow.

Aside : in their empirical example, Kim and Nelson actually consider a slightly different model in which the employment variable is allowed to also depend on lagged values of the factor - this model does not fit into the built-in DynamicFactor class, but can be accommodated by using a subclass to implement the required new parameters and restrictions - see Appendix A, below.

Multivariate models can have a relatively large number of parameters, and it may be difficult to escape from local minima to find the maximized likelihood. In an attempt to mitigate this problem, I perform an initial maximization step from the model-defined starting parameters using the modified Powell method available in Scipy see the minimize documentation for more information.

The resulting parameters are then used as starting parameters in the standard LBFGS optimization method. Once the model has been estimated, there are two components that we can use for analysis or inference:.

One reason for this difficulty is due to identification issues between the factor loadings and the unobserved factors. One easy-to-see identification issue is the sign of the loadings and the factors: an equivalent model to the one displayed below would result from reversing the signs of all factor loadings and the unobserved factor. Here, one of the easy-to-interpret implications in this model is the persistence of the unobserved factor: we find that exhibits substantial persistence.To browse Academia.

Skip to main content. Log In Sign Up. Download Free PDF. The US business cycle, dynamic factor analysis vs. Martin Uebele. Albrecht Ritschl. Samad Sarferaz. The U. Business Cycle, Dynamic Factor Analysis vs. We employ a Bayesian dynamic factor model to obtain aggregate and sectoral economic activity indices.

While we can generate evidence of postwar moderation relative to pre, this evidence is not robust to structural change, implemented by time-varying factor loadings.

We do find evidence of moderation in the nominal series, however, and reproduce the standard result of moderation since the s. Keywords: U. Martin Uebele acknowledges financial support from DekaBank. Contact: a. While there is broad agreement on the business cycle turning points, the issue of volatility is still not fully resolved, as different available estimates yield contradictory results.

Was wartime prosperity in the mids really so strong? And has the U. Researchers have disagreed on the severity of the downturn after World War I as well as on the other two questions. This view was challenged in a series of papers by Romer, who argued that postwar sta- bilization relative to the decades before World War I was an artifact of the historical output and unemployment data.

Most of the debate evolved around two rivaling such series and their implications for U. Balke and Gordonmodified a popular GNP series originating from the Commerce Department, for which they produced a widely used quarterly interpolation.

The high volatility of this series before World War I, compared to the rather moderate fluctuations of postwar GNP, is what shaped conventional wisdom about postwar moderation in the s. Romerchallenged this view based on a revision of the alternative series of Kendrickwhich she argued was less prone to spurious volatility.

However, her own calculations have been criticized for depending on assumptions which were deemed not to be empirically testable given the lack of historical GNP data, see Lebergott The present paper offers an alternative but complementary approach to measur- ing the volatility of the U. We draw on the growing literature on diffusion indices using a term of Stock and Watson of economic activity, which are distilled from a large panel of disaggregate time se- ries using dynamic factor analysis DFA.

Stock and Watson developed an unobserved component model for disaggregate series representing the U. The same issues loom large with histori- cal data.

Disaggregate series are often abundant for historical periods, but usually do not match national accounting categories well, and the information needed for proper aggregation is incomplete. As a consequence, proxies have to be used, which can be controversial as mentioned above. The DFA approach replaces the questionable aggregation techniques used in the construction of HNAs with a statistical aggregator.

Series that would be of limited use in reconstructing HNAs can now be exploited for their business cycle indicator characteristics, i. To our knowledge, this approach was first applied to turning point analysis as an alternative to HNA estimates by Gerlach and Gerlach-Kristen for Switzerland between the s and the Great Depression of the s.

Sarferaz and Uebele employ a Bayesian dynamic factor model to obtain an index of economic activity for 19th century Germany, comparing it to different rivaling HNA-based chronologies.Katharine G. Katz, Abraham, Katharine G. Steven J. Eric J. Mankiw, N Gregory, Gregory Mankiw, Krizan, Census Bureau.

Discussion Papers. John Haltiwanger, Louis, issue May, pages Lilien, David M, Reichlin, Lucrezia, Lucrezia Reichlin, Thomas J. Sims, Tom Doan, "undated". Haltiwanger, Ellen R. Rissman, Gallegati, M. You can help correct errors and omissions.

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Level 1 CFA Economics: Topics in Demand and Supply Analysis-Lecture 1

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Beverage technology pdf

By using quarterly firm level data relative to hundreds of US firms for 20 years, we investigate the number and the features of the underlying forces leading economic growth: evidence suggests the main shock to be the same across sectors and for the economy as a whole, thus more likely a demand shock than a technology shock.And even when finally sunk by a submarine while escorting a convoy in 1945, Shigure still had some luck left because she sank so slowly that the vast majority of the crew were able to survive.

Or at least more pre-dreadnoughts. I'd love the SMS Schleswig-Holstein and the austrian SMS Radetzky. So therefore, I'd predict that either HMS Hood or HMS Prince of Wales will show up alongside the RN battleships.

Secondly, I'd say that an Italian premium is probably headed our way too. Either Roma or Giulio Cesare, but I don't know many Italian ships, so idk. Why dont we get some lines out for the major superpowers at the time before we expand some to have multiple lines. France and Italy had some interesting ships. They could sell these in German and British packs and would probably be fairly cheap considering most would be lower tiers.

Or how about this lone oddity ship the Finnish "Vainamonien" coastal defense ship. I always thought this ship was so cool looking. Roma would be cool. I've never seen the Vainamonien before. I can see tier 5 or 6. Almost everything else viable mass produced destroyers from Britain or France about the same tier.

Her reason for being noteworthy is she was first ship sunk in WWII, Germans bombed her on September 1, 1939 as part of the opening attack.

Just most of Poland's line up is stuff the Brit's lent Poland followed by stuff the Russian's sold to Poland. Also what makes Blys differ is its guns aren't British they're Swiss Bofors 120mm. So Blys is unique and non of the other ORP ships other than the sister Grom will have those guns. Maybe an odd little cruiser instead.Later works, 1999's The Age of Spiritual Machines and 2005's The Singularity is Near outlined other theories including the rise of clouds of nano-robots (nanobots) called foglets and the development of Human Body 2.

Kurzweil's first book, The Age of Intelligent Machines was published in 1990. It forecast the demise of the Soviet Union due to new technologies such as cellular phones and fax machines disempowering authoritarian governments by removing state control over the flow of information.

A Dynamic Factor Analysis of Business Cycle on Firm-Level Data

He also stated that the Internet would explode not only in the number of users but in content as well, eventually granting users access "to international networks of libraries, data bases, and information services". The third and final section of the book is devoted to elucidating the specific course of technological advancements Kurzweil believes the world will experience over the next century. Titled "To Face the Future", the section is divided into four chapters respectively named "2009", "2019", "2029", and "2099".

For every chapter, Kurzweil issues predictions about what life and technology will be like in that year. The device was portable, but not the cheap, pocket-sized device of the prediction. While this book focuses on the future of technology and the human race as The Age of Intelligent Machines and The Age of Spiritual Machines did, Kurzweil makes very few concrete, short-term predictions in The Singularity Is Near, though longer-term visions abound.

Kurzweil predicted that, in 2005, supercomputers with the computational capacities to simulate protein folding will be introduced. In 2010, a supercomputer simulated protein folding for a very small protein at an atomic level over a period of a millisecond. The protein folded and unfolded, with the results closely matching experimental data. Chess Champion and International Grandmaster Larry Christiansen in a four-game match.

Another 3 are partially correct, 2 look like they are about 10 years off, and 1, which was tongue in cheek anyway, was just wrong. Kurzweil said in a 2006 C-SPAN2 interview that "nanotechnology-based" flying cars would be available in 20 years. Kurzweil believes, by the end of the 2020s, humans will be able to completely replace fossil fuels.

In the cover article of the December 2010 issue of IEEE Spectrum, John Rennie criticized Kurzweil's predictions: "On close examination, his clearest and most successful predictions often lack originality or profundity. And most of his predictions come with so many loopholes that they border on the unfalsifiable.

a dynamic factor analysis of business cycle on firm

Please help improve it or discuss these issues on the talk page. Please help by adding reliable sources. Contentious material about living persons that is unsourced or poorly sourced must be removed immediately, especially if potentially libelous or harmful. You can help to improve it by introducing citations that are more precise. The Age of Intelligent Machines. Cambridge, MA: MIT Press. We Blog The World.

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Retrieved April 16, 2012. The New York Times. Retrieved February 13, 2013. Growth of the Internet (PDF).


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