This document analyzes structural vs cyclical unemployment in the U.S. using unemployment and job opening data from 1950-2014. It finds:
1) Unemployment is highly dependent on past levels of unemployment and can be modeled as an autoregressive process.
2) There was a structural shift in the Beveridge curve around 2009, indicating increased frictions in the labor market.
3) While a structural shift occurred, the curve has recently moved back toward previous levels, suggesting the high unemployment may be prolonged cyclical effects rather than permanent structural changes.
Risk Managers: How to Create Great Stress Tests (and How to Not)Daniel Satchkov
Risk managers are increasingly tasked with creation of forward looking stress tests. The problem that many encounter is that results are not reasonable, especially when they combine shocks to different factors, such as interest rates and equity indices or spreads and commodities. We show the most important source of such problem, disregard for the relative volatility of different factors being shocked. Shock Plausibility measure helps avoid this problem by quantifying coherence based on past crises.
A new model of perfectly competitive monopolist competition when products and factors are a perfectly differentiated continuum of points. General Equilibrium is found to be a soliton (a continuum of fixed points), not a single fixed point as presently conceived in the literature. Further, the general equilibrium core surface is found to be a tension minimizing minimal surface. An application of mathematical economics and welfare economics to be submitted to Economic Letters. Copyright, September 7, 2019, Richard
Anthony Baum, Santa Barbara. California, USA, which copyright will be relinquished upon publication.
Risk Managers: How to Create Great Stress Tests (and How to Not)Daniel Satchkov
Risk managers are increasingly tasked with creation of forward looking stress tests. The problem that many encounter is that results are not reasonable, especially when they combine shocks to different factors, such as interest rates and equity indices or spreads and commodities. We show the most important source of such problem, disregard for the relative volatility of different factors being shocked. Shock Plausibility measure helps avoid this problem by quantifying coherence based on past crises.
A new model of perfectly competitive monopolist competition when products and factors are a perfectly differentiated continuum of points. General Equilibrium is found to be a soliton (a continuum of fixed points), not a single fixed point as presently conceived in the literature. Further, the general equilibrium core surface is found to be a tension minimizing minimal surface. An application of mathematical economics and welfare economics to be submitted to Economic Letters. Copyright, September 7, 2019, Richard
Anthony Baum, Santa Barbara. California, USA, which copyright will be relinquished upon publication.
This paper is a methodological exercices presenting the results obtained from the estimation of the growth convergence equation using different methodologies.
A dynamic balanced panel data is estimated using: OLS, WithinGroup, HsiaoAnderson, First Difference, GMM with endogenous and GMM with predetermined instruments. An unbalanced panel is also realized for OLS, WG and FD.
Results are discused in light of Monte Carlo studies.
The paper highlights the role that speculation plays in making stock price fluctuation chaotic. The positive feedback produce by speculative behavior determines the general dynamics of stock prices. The price dynamics is described by a logistic equation. This logistic equation is also known as Verhulst equation. This equation was originally developed to describe the dynamic behavior of population of an organism. A discrete form of the Verhulst equation called as Ricker model is done to simulate the price dynamics. The simulation of the iterative process in the Ricker model demonstrates that speculation can produce chaos. By varying the value of the parameter describing speculation, the price dynamics becomes chaotic for sufficiently high degree of speculation. The extreme sensitivity to initial condition of a chaotic system produced the so-called “butterfly effect”. A simulation of the butterfly effect is done using two exactly identical discrete logistic equations. The equations differed only in their initial values by a very minute amount. It shows how two exactly identical dynamical systems quickly behave very differently even if the difference in their initial conditions is so infinitesimally small. The implication of the butterfly effect in doing experiments in the physical world is analyzed. The presence of butterfly effect in a chaotic system raises the issue of measurement errors in the conduct of physical experiments. No matter how accurate the scientific device used in the experiment, it is still subject to measurement errors. Butterfly effect tremendously magnifies the measurement errors over a short span of time. This implies that long-term prediction in a chaotic system is impossible
Application of Weighted Least Squares Regression in Forecastingpaperpublications3
Abstract: This work models the loss of properties from fire outbreak in Ogun State using Simple Weighted Least Square Regression. The study covers (secondary) data on fire outbreak and monetary value of properties loss across the twenty (20) Local Government Areas of Ogun state for the year 2010. Data collected were analyzed electronically using SPSS 21.0. Results from the analysis reveal that there is a very strong positive relationship between the number of fire outbreak and the loss of properties; this relationship is significant. Fire outbreak exerts significant influence on loss of properties and it accounts for approximately 91.2% of the loss of properties in the state.
This slide deck describes how CBO used a Markov-switching model to assess the uncertainty of the economic forecast presented in CBO’s Current View of the Economy in 2023 and 2024 and the Budgetary Implications (November 2022).
We will test whether :
a) Sequential Deep Neural Networks (DNNs) can predict the stock market (S&P 500) better than OLS regression;
b) DNNs using smooth Rectified Linear activation functions perform better than the ones using Sigmoid (Logit) activation functions.
Presentation by U. Devrim Demirel, CBO's Fiscal Policy Studies Unit Chief, and James Otterson at the 28th International Conference of The Society for Computational Economics.
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
In this paper we present a heterodox open-economy macroeconomic model aiming to establish an alternative view to the "New Consensus" model and analyze the determinants of long-run inflation, the monetary policy transmission channels, the costs of such policy and some barriers to its implementation.
The open-economy New Consensus model with inflation targeting is based on the following theoretical structure: (i) the potential output is determined according to the neoclassical theory of value and distribution; (ii) output depends on the real interest rate and the real exchange rate (iii) the Phillips curve is accelerationist (iv) the exchange rate determination depends on the uncovered interest rate parity in the short run and on the purchasing power parity in the long run; (v) a Taylor rule.
The main results of this model are well known. There is no trade-off between inflation and productive capacity, since the later is independent of the effective output; and such policy can always be applied, because it is always possible to the Monetary Authority to fix the real interest rate in line with the natural rate of interest.
The alternative model proposed follows the same simplified scheme of the New Consensus model, but altering significantly some theoretical assumptions. (i) First, the potential output or productive capacity of the economy follows the long-run expected effective demand. We use the Sraffian supermultiplier to model the demand led growth of productive capacity. (ii) the output growth rate depends on the real interest rate (through the effect on autonomous spending) and the real exchange rate (through the effect on exports), (iii) the Phillips curve is non accelerationist (partial inertia hypothesis) and depends on the role of nominal exchange rate, on the imported inflation and on the degree of distributive conflict, (iv) the nominal exchange rate depends on the interest rate differential and is subject to speculation, and (v) a Taylor rule.
We analyze the alternative model in terms of analytic solution and computer simulations. The main results of this model is that the long-run inflation will depend on imported inflation, on the distributive conflict and on the inertia degree in the economy; demand shocks influences inflation only in the short run, so the main channel to control inflation by MA is by controlling the nominal exchange rate appreciation through the maintenance of an interest rate differential with the rest of the world.
From the cost of policy standpoint, the results also differ from that proposed by the New Consensus. First, we show that the policy of inflation control is not neutral in terms of growth rate of productive capacity. This means that a higher inflation targeting or a lower imported inflation ultimately lead to a higher growth rate of productive capacity (so the external constraint can appear in the form of higher imported inflation); moreover, as the policy of inflation control depends largely on a p
This paper is a methodological exercices presenting the results obtained from the estimation of the growth convergence equation using different methodologies.
A dynamic balanced panel data is estimated using: OLS, WithinGroup, HsiaoAnderson, First Difference, GMM with endogenous and GMM with predetermined instruments. An unbalanced panel is also realized for OLS, WG and FD.
Results are discused in light of Monte Carlo studies.
The paper highlights the role that speculation plays in making stock price fluctuation chaotic. The positive feedback produce by speculative behavior determines the general dynamics of stock prices. The price dynamics is described by a logistic equation. This logistic equation is also known as Verhulst equation. This equation was originally developed to describe the dynamic behavior of population of an organism. A discrete form of the Verhulst equation called as Ricker model is done to simulate the price dynamics. The simulation of the iterative process in the Ricker model demonstrates that speculation can produce chaos. By varying the value of the parameter describing speculation, the price dynamics becomes chaotic for sufficiently high degree of speculation. The extreme sensitivity to initial condition of a chaotic system produced the so-called “butterfly effect”. A simulation of the butterfly effect is done using two exactly identical discrete logistic equations. The equations differed only in their initial values by a very minute amount. It shows how two exactly identical dynamical systems quickly behave very differently even if the difference in their initial conditions is so infinitesimally small. The implication of the butterfly effect in doing experiments in the physical world is analyzed. The presence of butterfly effect in a chaotic system raises the issue of measurement errors in the conduct of physical experiments. No matter how accurate the scientific device used in the experiment, it is still subject to measurement errors. Butterfly effect tremendously magnifies the measurement errors over a short span of time. This implies that long-term prediction in a chaotic system is impossible
Application of Weighted Least Squares Regression in Forecastingpaperpublications3
Abstract: This work models the loss of properties from fire outbreak in Ogun State using Simple Weighted Least Square Regression. The study covers (secondary) data on fire outbreak and monetary value of properties loss across the twenty (20) Local Government Areas of Ogun state for the year 2010. Data collected were analyzed electronically using SPSS 21.0. Results from the analysis reveal that there is a very strong positive relationship between the number of fire outbreak and the loss of properties; this relationship is significant. Fire outbreak exerts significant influence on loss of properties and it accounts for approximately 91.2% of the loss of properties in the state.
This slide deck describes how CBO used a Markov-switching model to assess the uncertainty of the economic forecast presented in CBO’s Current View of the Economy in 2023 and 2024 and the Budgetary Implications (November 2022).
We will test whether :
a) Sequential Deep Neural Networks (DNNs) can predict the stock market (S&P 500) better than OLS regression;
b) DNNs using smooth Rectified Linear activation functions perform better than the ones using Sigmoid (Logit) activation functions.
Presentation by U. Devrim Demirel, CBO's Fiscal Policy Studies Unit Chief, and James Otterson at the 28th International Conference of The Society for Computational Economics.
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
In this paper we present a heterodox open-economy macroeconomic model aiming to establish an alternative view to the "New Consensus" model and analyze the determinants of long-run inflation, the monetary policy transmission channels, the costs of such policy and some barriers to its implementation.
The open-economy New Consensus model with inflation targeting is based on the following theoretical structure: (i) the potential output is determined according to the neoclassical theory of value and distribution; (ii) output depends on the real interest rate and the real exchange rate (iii) the Phillips curve is accelerationist (iv) the exchange rate determination depends on the uncovered interest rate parity in the short run and on the purchasing power parity in the long run; (v) a Taylor rule.
The main results of this model are well known. There is no trade-off between inflation and productive capacity, since the later is independent of the effective output; and such policy can always be applied, because it is always possible to the Monetary Authority to fix the real interest rate in line with the natural rate of interest.
The alternative model proposed follows the same simplified scheme of the New Consensus model, but altering significantly some theoretical assumptions. (i) First, the potential output or productive capacity of the economy follows the long-run expected effective demand. We use the Sraffian supermultiplier to model the demand led growth of productive capacity. (ii) the output growth rate depends on the real interest rate (through the effect on autonomous spending) and the real exchange rate (through the effect on exports), (iii) the Phillips curve is non accelerationist (partial inertia hypothesis) and depends on the role of nominal exchange rate, on the imported inflation and on the degree of distributive conflict, (iv) the nominal exchange rate depends on the interest rate differential and is subject to speculation, and (v) a Taylor rule.
We analyze the alternative model in terms of analytic solution and computer simulations. The main results of this model is that the long-run inflation will depend on imported inflation, on the distributive conflict and on the inertia degree in the economy; demand shocks influences inflation only in the short run, so the main channel to control inflation by MA is by controlling the nominal exchange rate appreciation through the maintenance of an interest rate differential with the rest of the world.
From the cost of policy standpoint, the results also differ from that proposed by the New Consensus. First, we show that the policy of inflation control is not neutral in terms of growth rate of productive capacity. This means that a higher inflation targeting or a lower imported inflation ultimately lead to a higher growth rate of productive capacity (so the external constraint can appear in the form of higher imported inflation); moreover, as the policy of inflation control depends largely on a p
1. “The New Normal”: Structural vs.Cyclical U.S.Unemployment
Econometrics II
May 2, 2014
Joshua T. Rome
2. One topicat the forefrontof U.S.economicdevelopmentoverthe pastseveral yearssince the
financial crisishasbeenunemployment. The catalystof thisdiscourse isnomystery.Unemployment
impactsmanyAmericansandthe relationshipwithGDPisempiricallyverifiable (thesocalled“Okun’s
law”). The mainquestionregardingthe persistentlyhigh levelsof unemployment iswhetherthishas
beena structural or prolongedcyclical effect. Thispaperseekstounderstandthe recentchangesinthe
unemploymentrate inthe contextof the previoussixtyyearsof unemploymentdatathatisavailable.
The firstportionof the analysiswill be composedof lookingatthe patternof the unemploymentrate as
a standalone time series. Followingasolidunderstanding of the historictrend;the unemploymentrate
will be comparedwithtotal jobopeningsforfurthercluestothe nature of the recentunemployment
levels.
Analyzing the data
The firststepin derivingarelationshipbetweenthissetof variablesistosimplyobserve the dataover
time;andto analyze if there are any
trends,seasonalityorotherdiscernible
patterns. We beginbylookingat time
graphs of our mainvariablesbeginning
withunemployment. Aswithmany
macroeconomictime series
unemploymentisstronglytrended,
showingcyclical peaksandtroughs,
althoughvaryingindurationaswell asmagnitude. Itisclear thatlevel of unemploymenttodaystrongly
dependsonthe level of unemploymentyesterday. If modeledasafirstorder autoregressivemodel:
𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑡 = 𝐵0 + 𝐵1 𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑡−1 + 𝜀 𝑡
3. The model appearsto be verysignificant,withthe overall level of significance of the F-Statistic
at zero withanR-squaredof almost94%. Unfortunatelythesespurious resultsare anartifact of the high
levelsof autocorrelation. Because ourmodel includesarighthandside dependentvariable any
autocorrelationcausesestimatesof the regressiontobe inconsistentandinvalid;unfortunatelyaswell
the Durbin-Watsonteststatisticwillnotbe validandcannotbe used. Thuswe will use Durbin’s‘h’test
whichisdesignedforexactlythissituation. We firstcompute the DurbinWatsonteststatistic:
DW=.9878363 and from thisstatisticwe can backout our estimate for rho. 𝑃̂ = 1 −
𝐷𝑊
2
andwe
compute thisvalue tobe .50608185. ℎ =
𝑃̂√
𝑛∗
(1−𝑛∗)𝑆𝐸.𝐵̂
ℎ = .51√
256
(255).000240
= 32.715 >
2.56 thus we rejectthe null hypothesisthat
there isno autocorrelationforthismodel
specification. If apartial Correlogramis used,
whichis basedon the partial autocorrelation
functionintroducedbyBox-Jenkins,we canclearlysee thatthe firsttwolags of the model lie outside of
the 95% confidence interval andthusare notrandom. The coefficientonthe original model alsomight
4. seemtosuggesta unitroot as it isveryclose to one (.96); andthe dickeyfullertestfora unitroot would
be appropriate totest forthis. The resultforthe dickeyfullertestwiththree laggedtermsandatrendis
significantwithap-value of .0385; thuswe rejectthe null hypothesisof arandom-walk.
The processof correctlyspecifyingthe model reliedonsimilarbutunrelatedintuition of how
businesscycleswork. Inthe 1950’s manyeconomistsdescribedthe businesscycle intermsof alag on
investmentdecisiondependingonthe growthof the economy. The resultingspecificationforthis
phenomenonwasa“secondorderdifference equation”where the dependentvariable wasafunctionof
the difference of the laggedfirstdifference of thatvariable. Ingeneral the systemcanresultin
dampeningoranti-dampeningeffectsorstable peaksandtroughs;andof course applicable toother
fields. Basedonthisintuitionthe model forunemploymentisestimated usingaportion of the sample
(observations 1through50) witha laggedtermand a laggedfirstdifference term.
𝑈𝑁𝑅𝐴𝑇𝐸𝑡 = 𝐵0 + 𝐵1 𝑈𝑁𝑅𝐴𝑇𝐸𝑡−1 + 𝐵2( 𝑈𝑁𝑅𝐴𝑇𝐸𝑡−1 − 𝑈𝑁𝑅𝐴𝑇𝐸𝑡−2)+ 𝜀 𝑡
Thismodel isverysignificantwithlow RMSEwhichisgoodfor prediction. Generatingaprediction
variable we cantestthe model we cantestthe model forthe remainingportionof the sample from
5. 1962-2014. The resultsof this out-of-sampleforecastare verytightlyfitaroundthe actual datawitha
meanresidual of .1621 as the difference betweenthe actual unemployment.
There are twomainsourcesof uncertaintyregardingprediction;inthismodel aswell asany
other. If the true valuesof all of the modelscoefficients wereknown;the rootmeansquarederror
wouldbe the standarddeviationof the forecastwhichwe couldassume the errortermisnormally
distributedaboutthe mean. Itisnot possible toknow the true value of the coefficients,becausefor
each sample thatisdrawn;new coefficientswillbe estimated. Tocompute the variance of the estimate
Var(є) we compute Var(𝑦 − 𝑦̂) whichequalsVar(𝑦̂)+Var(y) –2Cov(𝑦̂, 𝑦).
Mathematically;the implicationsof thismodel are numerous. Firstlythe modelimpliesalong-
run unemploymentrate of 5% basedon the data. Thiscan easilybe shownby solvingforthe steady
state where 𝑥 𝑡 = 𝑥 𝑡−1 = 𝑥 𝑡−2
Thus: 𝑥∗ = .75 + .85𝑥∗ + .55( 𝑥∗ − 𝑥∗)
Resultingin: .15𝑥∗ = .75
𝐷𝑖𝑣𝑖𝑑𝑒 𝑏𝑦 .15
⇒ 𝑥∗ = 5
Thismodel wasestimated
fromthe sample from
1950-1962, all other
estimatesare out-of -
sample forecasts;from
whichwe observe thatthe
model fitsthe datavery
closely.
6. Economicallythismaynotbe the mostgranularapproach to estimatinganequilibrium
unemploymentlevel;butthe figure doesappeartobe reasonable. IndeedSt.LouisFederal reserve data
estimatesNROUthe natural rate of unemploymentwithameanvalue of 5.55% anda value exactly
equal to5% fromthe years2000-2009. A more accurate predictionof the longrun“natural”level of
unemploymentshouldtake intoaccountdemographicsof the population. Anadditional implicationof
thismodel specificationisthat the coefficientof the laggedfirstdifference at.55 is that the specification
isunstable;butwithdampeningeffect. Inotherwordsany shocksto the systemwill resultinsmaller
and smallerdifference fromthe steadystate level;the systemisdynamicallystable.
For the nonmathematical non-statisticalreaderwhatwe learnfromthismodel isthe
dependencyof the currentlevel of unemploymentonpreviouslevelsof unemployment. Predictionwith
thismodel issomethingakintopredictingthe weather tomorrow bylookingoutthe window today;
brightand sunnyandseventywe cansay withsome certaintythatit ishighlyunlikelytosnow.With
unemploymentatagivenlevel we canmake similarobservations. Thismodel alsomakesanother
implicitpredictionof alongrun rate of unemploymentof five percent,althoughthe accuracyof this
predictioncanbe greatlyimprovedwithothereconomicdatasuchas populationdemographics.
Nowthat we understandthe nature of the unemploymentdataovertime we shiftouranalysis
towardsthe recenttrend. The unemploymentrate hadnot reachedheightsof levelsabove 8% since the
1980’s, and yearslateraftera recoveryinequitypricesthe unemploymentrate isstill atitshighestlevel
0
2
4
6
8
10
12
0 10 20 30 40 50 60
Thisdiagramillustratesthe dynamic
stabilityof the model specifiedfrom
the regression. The firstfew
observationsare takenfromthe
actual data;the restare the
predictionsbasedonthe model.
Randomshocksare exogenously
introducedat32-38 and40 to
illustrate the dynamicstability.
7. inat leastone decade. Inorderto understandthistrendwe mustturnto the Beveridgecurve that
relatesthe numberof nonfarmpayroll openingstothe unemploymentrate.
In the figure we see the monthly dataof the two time seriesplottedagainsteachother fromthe
yearsDec 2000- Mar 2014 formingthe so-calledBeveridgeCurve. Itappearsobviousthatthere isa
structural differencebetweentwodifferenttime periods;visuallyone couldidentifytwolinesthatbest
fitthe data. Basedon intuitionthere isadownwardsloping
line whichwe specifyas (RestrictedModel):
𝐽𝑜𝑏 𝑉𝑎𝑐𝑎𝑛𝑐𝑦 𝑅𝑎𝑡𝑒𝑡 = 𝛽0 + 𝛽1 𝑈𝑁𝑅𝐴𝑇𝐸𝑡 + 𝜀 𝑡
Our coefficientof betaone isbelievedtobe negative; which
the regressionconfirms. Inallowingthe slopeandcoefficient
to be differentforthe twodifferentperiodswe definea
categorical dummyvariable 𝑑𝑖 thatistrue for all periodsafterAugust2009. The specificationof the
secondperiodmodel is:
𝐽𝑜𝑏 𝑉𝑎𝑐𝑎𝑛𝑐𝑦 𝑅𝑎𝑡𝑒𝑡 = ∝0+∝1 𝑈𝑁𝑅𝐴𝑇𝐸𝑡 + 𝜀 𝑡
In orderto create a more parsimoniousdefinitionof ourmodel inone equationwe mustdefinethe
differencesbetweenthe twoseparate modelcoefficients: Let 𝛿1 =∝0− 𝛽0 𝛿2 =∝1− 𝛽1
The unrestricted model: 𝐽𝑜𝑏 𝑉𝑎𝑐𝑎𝑛𝑐𝑦 𝑅𝑎𝑡𝑒𝑡 = 𝛽0 + 𝛽1 𝑈𝑁𝑅𝐴𝑇𝐸𝑡 + 𝛿1 𝑑 𝑖 + 𝛿2 𝑑𝑖 𝑈𝑁𝑅𝐴𝑇𝐸𝑡 + 𝜀 𝑡
Afterexecutingthe regressionof boththe restrictedandunrestrictedregressionswe are able to
performa chowtestof structural difference. The null hypothesisisthatthe unrestrictedmodelis ∝0=
𝛽0 𝑎𝑛𝑑 ∝1= 𝛽1while the alternative hypothesisisthatthe twoare in fact different.
ChowTest
( 𝑆𝑆𝐸𝑟𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑−𝑆𝑆𝐸 𝑢𝑛𝑟𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑)∗
1
𝑞
𝑅𝑒𝑠𝑡𝑟𝑖 𝑐 𝑡𝑖 𝑜 𝑛𝑠
𝑆𝑆𝐸 𝑢𝑛𝑟𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑∗
1
𝑛−𝑘
=
3.098836765
0.025153714
= 123.19 > 4.605
The sum of squarederrorforthe unrestrictedmodelis 3.89882567, with155 denominatordegreesof
freedom. The Restrictedsumof squarederroris 10.0964992 and the numberof restrictionsistwo
8. correspondingtothe numeratordegreesof freedom. Giventhese degreesof freedomthe chow testwe
can rejectthe null hypothesisthatthere isnostructural break at the 1% significance level.
The cause generallyattributedtothe shiftinthe Beveridgecurve isincreasedfrictionsin
matchingbetweenemployeesandemployers. Followingthe greatrecessionunemploymentbenefits
were greatlyextendedtobeyondone year;impeding searchersfromincreasingtheireffort,thus
decreasingmatchingefficiency. Structurallythe principallyaffectedindustryof the crisisfollowingthe
housingboomhadbeenthe constructionindustry. Thisindustryhasrelatively lessfrictioninmatching
compared to otherhigherskill industries thathave lesshirespervacancy suchas engineering. Inan
article fromthe IMF EconomicReviewHobijnandSahinattribute muchof this shiftbetweenthese two
factors1.
Hobijn’sandSahin’sstudywasa longitudinal analysisof thirteencountriesoverfifteenyears.
Interestingfindingsof theirresearch are thataside fromthe U.S. onlythree othercountriesexperienced
a rightwardshiftinthe Beveridge curve: Portugal,Spain,andthe UnitedKingdom. (The othercountries
analyzedinthe sample were Australia,Austria,Belgium, France,Germany,Japan,Netherlands,Norway
and Switzerland.) Whatthese three countrieshadincommonwasa disproportionate decline in
construction, acorollaryof the steepdeclinesinhousingprices. Inaddition,the researchwasputintoa
historical perspectivewithBeveridgeshiftsof the 70’s and 80’s that had reversedtheirrightwardshift
followingrecessions. The conclusionsof theiranalysis are thatthe rate of adjustmentof U.S.labor
relative toothercountriesisveryhigh,andgiventhat unemploymentinsurance increasesare not
permanent;the questioniswhenthiscyclical effectwill subside.
Shiftingfocustothe latestiterationof the Beveridge Curve;althougharightwardshifthas
occurredwhichis consistentwith“structural unemployment”,the curve appearstobe on a path back to
itspreviousstructural levelslendingsupporttothe “prolongedcycle”argument. The observations are
connectedintime anddecreasinginunemploymentwhilstincreasinginNonfarmjobopenings. This
9. componentof the recenttrendiscyclical. But itis importanttonote that the trend isnot a straightline,
nor convex asthe Beveridge Curve isgenerallyportrayed. Thisnonlinearstructure resemblinga
downwardopeningparabolacanbe modeledby
regressingnonfarmjobopenings onafirstorder
‘UNRATE’ term witha squared ‘UNRATE’ term. The
coefficientof the squaredtermisexpectedtobe
negative because the curve isdownwardopening,and
giventhe tightbandof variationthe model is
expectedtobe verystatisticallysignificantwithahighr-squared.
Specification: 𝐽𝑜𝑏 𝑉𝑎𝑐𝑎𝑛𝑐𝑦 𝑅𝑎𝑡𝑒𝑡 = 𝛽0 + 𝛽1 𝑈𝑁𝑅𝐴𝑇𝐸𝑡 + 𝛽1 𝑈𝑁𝑅𝐴𝑇𝐸1
2
+ 𝜀 𝑡
The regressionof the newmodel satisfiedall of the apriori assumptionsbut withone unique concern,a
verystatisticallyinsignificantconstantterm. Afterconsiderationof the context;the unemploymentrate
isrelativelyfarfromzeroconsideringthe spacingof the observationsandquantityof observations. In
thiscontextthe lowsignificanceof the constanttermisunimportantandmerelyreflectsalarge distance
fromthe y axisfromthe observations. Economically the empiricallyderivedcurve lendsevidence tothe
prolongedbusinesscycle theoryregardingthe unemployment. Inthe contextof the previousBeveridge
10. curve ex ante the greatrecession,the model predictsthatwe are on a path that leadstothe old
structure of the Beveridge Curve. The currenttrendthuscan be decomposedintotwocomponents;a
currentcyclical trendof reducedunemploymentandincreasedjobopenings,andaleftwardshifttothe
olderlevelsof matchingefficiency. As
extensionsof unemploymentinsurance
are eliminatedandthe housingmarket
recovers,higherturnoverpositionswill
constitute alargercompositionof the
overall USjob marketandlabor market
efficiencywillslowlyreturn. Although
the economyhasrecovered
significantlysince the greatrecession,the recoveryhasbeenslow goingandiscontinuingtothisday.
Despite specious evidenceof apermanentstructural change,a“new normal”,the relativelyhigh
levelsof unemploymentinactualityare nothingmore thana painfullyslow businesscycle. The analysis
done by Hobijnand Sahindiffersinthattheirfocusisa cross-countryanalysis asopposedtoa specific
trendanalysis, butcomplementsthisresearchwithrichhistoricdataand cross-countrycomparisons.
Althoughdemographicfactorsplaya large role inshiftingstructuresof employment;the coincidenceof
such a change duringthisbusinesscycle isnotseentobe the dominatingfactor. In the comingyearsthe
data suggesta returnto the “old normal”as labormarketinefficienciestaperfromthe market. In
practice,onlytime will tellwhetherthe recessionchange hasbeenpermanent, neverthelesscurrent
researchindicatesasanguine outlook.
11. Works Cited
1. Hobijn,B.,& Şahin,A. (2013). Beveridge Curve ShiftsacrossCountriessince the Great
Recession.IMFEconomicReview,61(4),566-600. doi:10.1057/imfer.2013.18
2. Diamond,P.(2013). Cyclical Unemployment,Structural Unemployment.IMFEconomicReview,
61(3), 410-455.
3. Data Source:FRED, Federal Reserve EconomicData,Federal Reserve Bankof St.Louis:Civilian
UnemploymentRate [UNRATE];U.S.Departmentof Labor:Bureau of Labor Statistics;
http://research.stlouisfed.org/fred2/series/UNRATE;accessed May1st
, 2014.
4. Data Source:FRED, Federal Reserve EconomicData,Federal Reserve Bankof St.Louis:Natural
UnemploymentRate [NROU] ;U.S. Congressional BudgetOffice;
http://research.stlouisfed.org/fred2/series/NROU;
5. Data Source:FRED, Federal Reserve EconomicData,Federal Reserve Bankof St.Louis:Job
Openings:Total Nonfarm[JTSJOR];U.S.Departmentof Labor:Bureauof Labor Statistics;
http://research.stlouisfed.org/fred2/series/JTSJOR;accessedMay1st, 2014.