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Abbreviations

Deepness :

A characteristic of a process with a skewed unconditional distribution.

Deviations :

Refers to a definition of the business cycle that links the cycle to transitory deviations of economic activity from a trend level.

Fluctuations :

Refers to a definition of the business cycle that links the cycle to any short-run changes in economic activity.

Level :

Refers to a definition of the business cycle that links the cycle to alternation between phases of expansion and recession in the level of economic activity.

Linear :

Refers to a class of models for which the dependence between two random variables can be completely described by a fixed correlation parameter.

Markov-switching models :

Models that assume the prevailing regime governing the conditional distribution of a variable or variables being modeled depends on an unobserved discrete Markov process.

Nonlinear :

Refers to the class of models for which the dependence between two random variables has a more general functional form than a linear equation and/or can change over time.

Nonlinear time series in macroeconomics :

A field of study in economics pertaining to the use of statistical analysis of data in order to make inferences about nonlinearities in the nature of aggregate phenomena in the economy.

Nuisance parameters :

Parameters that are not of direct interest in a test but influence the distribution of a test statistic.

Pivotal :

Refers to the invariance of the distribution of a test statistic with respect to values of parameters in the data generating process under the null hypothesis.

Power :

Probability of correct rejection of a null hypothesis in repeated experiments.

Self-exciting threshold models :

Models that assume the prevailing regime governing the conditional distribution of a variable or variables being modeled is observable and depends on whether realized values of the time series being modeled exceed or fall below certain “threshold” values.

Sharpness :

A characteristic of a process for which the probability of a peak when increasing is different than the probability of a trough when decreasing.

Size :

Probability of false rejection of a null hypothesis in repeated experiments.

Steepness :

A characteristic of a process with a skewed unconditional distribution for its first differences.

Structural change :

A change in the model describing a time series, with no expected reversal of the change.

Time reversibility :

The ability to substitute −t and t in the equations of motion for a process without changing the process.

Time series :

A collection of data corresponding to the values of a variable at different points of time.

Bibliography

Primary Literature

  • Acemoglu D, Scott A (1997) Asymmetric business cycles: theory and time-series evidence. J Monet Econ 40:501–533

    Article  Google Scholar 

  • Balke NS, Wynne MA (1996) Are deep recessions followed by strong recoveries? Results for the G-7 countries. Appl Econ 28:889–897

    Article  Google Scholar 

  • Ball L, Mankiw NG (1995) Relative price changes as aggregate supply shocks. Q J Econ 110:161–193

    Article  MATH  Google Scholar 

  • Bansal R, Yaron A (2004) Risks for the long run: a potential resolution of asset pricing puzzles. J Financ 59:1481–1509

    Article  Google Scholar 

  • Barlevy G (2005) The cost of business cycles and the benefits of stabilization. Econ Perspect 29:32–49

    Google Scholar 

  • Beaudry P, Koop G (1993) Do recessions permanently change output? J Monet Econ 31:149–163

    Article  Google Scholar 

  • Beveridge S, Nelson CR (1981) A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the business cycle. J Monet Econ 7:151–174

    Article  Google Scholar 

  • Boldin MD (1996) A check on the robustness of Hamilton’s Markov switching model approach to the economic analysis of the business cycle. Stud Nonlinear Dyn Econ 1:35–46

    MATH  Google Scholar 

  • Breunig R, Najarian S, Pagan A (2003) Specification testing of Markov-switching models. Oxf Bull Econ Stat 65:703–725

    Article  Google Scholar 

  • Brock WA, Sayers C (1988) Is the business cycle characterized by deterministic chaos? J Monet Econ 22:71–90

    Article  Google Scholar 

  • Brock WA, Dechert WD, Scheinkman JA (1996) A test of independence based on the correlation dimension. Econ Rev 15:197–235

    Article  MathSciNet  MATH  Google Scholar 

  • Bry G, Boschan C (1971) Cyclical analysis of time series: selected procedures and computer programs. NBER, New York

    Google Scholar 

  • Burns AF, Mitchell WA (1946) Measuring business cycles. NBER, New York

    Google Scholar 

  • Camacho M (2005) Markov-switching stochastic trends and economic fluctuations. J Econ Dyn Control 29:135–158

    Article  MathSciNet  MATH  Google Scholar 

  • Carrasco M, Hu L, Ploberger W (2007) Optimal test for Markov switching. Working paper

    Google Scholar 

  • Chalkley M, Lee IH (1998) Asymmetric business cycles. Rev Econ Dyn 1:623–645

    Article  Google Scholar 

  • Chan KS (1991) Percentage points of likelihood ratio tests for threshold autoregression. J R Stat Soc Ser B 53:691–696

    MATH  MathSciNet  Google Scholar 

  • Chan KS, Tong H (1986) On estimating thresholds in autoregressive models. J Time Ser Anal 7:179–190

    Article  MathSciNet  MATH  Google Scholar 

  • Chauvet M (1998) An econometric characterization of business cycle dynamics with factor structure and regime switches. Int Econ Rev 39:969–996

    Article  Google Scholar 

  • Chauvet M, Potter S (2001) Recent changes in the US business cycle. Manch Sch 69:481–508

    Article  Google Scholar 

  • Chib S, Nardari F, Shephard N (2002) Markov chain Monte Carlo methods for stochastic volatility models. J Econ 108:281–316

    Article  MathSciNet  MATH  Google Scholar 

  • Clarida RH, Taylor MP (2003) Nonlinear permanent-temporary decompositions in macroeconomics and finance. Econ J 113:C125–C139

    Article  Google Scholar 

  • Clements MP, Krolzig HM (1998) A comparison of the forecast performance of Markov-switching and threshold autoregressive models of US GNP. Econ J 1:C47–C75

    Google Scholar 

  • Clements MP, Krolzig HM (2003) Business cycle asymmetries: characterization and testing based on Markov-switching autoregressions. J Bus Econ Stat 21:196–211

    Article  MathSciNet  Google Scholar 

  • Clements MP, Krolzig HM (2004) Can regime- switching models reproduce the business cycle features of US aggregate consumption, investment and output? Int J Financ Econ 9:1–14

    Article  Google Scholar 

  • Cogley T, Sargent TJ (2001) Evolving post-World War II US inflation dynamics. In: Bernanke BS, Rogoff K (eds) NBER macroeconomics annual 2001. MIT Press, Cambridge, pp 331–373

    Google Scholar 

  • Cogley T, Sargent TJ (2005) Drift and volatilities: monetary policies and outcomes in the post WW II US. Rev Econ Dyn 8:262–302

    Article  Google Scholar 

  • Cohen D (2000) A quantitative defense of stabilization policy. Federal Reserve Board Finance and Economics Discussion Series. Paper 2000-34

    Google Scholar 

  • Cooley TF, Prescott EC (1976) Estimation in the presence of stochastic parameter variation. Econometrica 44:167–184

    Article  MathSciNet  MATH  Google Scholar 

  • Cooper R (1994) Equilibrium selection in imperfectly competitive economies with multiple equilibria. Econ J 104:1106–1122

    Article  Google Scholar 

  • Cover JP (1992) Asymmetric effects of positive and negative money-supply shocks. Q J Econ 107:1261–1282

    Article  Google Scholar 

  • Davies RB (1977) Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 64:247–254

    Article  MathSciNet  MATH  Google Scholar 

  • Davis SJ, Haltiwanger J (2001) Sectoral job creation and destruction responses to oil price changes. J Monet Econ 48:468–512

    Article  Google Scholar 

  • DeJong DN, Liesenfeld R, Richard JF (2005) A nonlinear forecasting model of GDP growth. Rev Econ Stat 87:697–708

    Article  Google Scholar 

  • DeLong JB, Summers LH (1986) Are business cycles symmetrical? In: Gordon RJ (ed) The American business cycle. University of Chicago Press, Chicago, pp 166–179

    Google Scholar 

  • DeLong B, Summers L (1988) How does macroeconomic policy affect output? Brook Pap Econ Act 2:433–480

    Article  Google Scholar 

  • Diebold FX, Chen C (1996) Testing structural stability with endogenous breakpoint: a size comparison of analytic and bootstrap procedures. J Econ 70:221–241

    Article  MATH  Google Scholar 

  • Diebold FX, Rudebusch GD (1996) Measuring business cycles: a modern perspective. Rev Econ Stat 78:67–77

    Article  Google Scholar 

  • Diebold FX, Lee JH, Weinbach G (1994) Regime switching with time-varying transition probabilities. In: Hargreaves C (ed) Nonstationary time series analysis and cointegration. Oxford University Press, Oxford, pp 283–302

    Google Scholar 

  • Durland JM, McCurdy TH (1994) Duration-dependent transitions in a Markov model of US GNP growth. J Bus Econ Stat 12:279–288

    Google Scholar 

  • Durlauf SN (1991) Multiple equilibria and persistence in aggregate fluctuations. Am Econ Rev Pap Proc 81:70–74

    Google Scholar 

  • Elliott G, Müller U (2006) Efficient tests for general persistent time variation in regression coefficients. Rev Econ Stud 73:907–940

    Article  MATH  MathSciNet  Google Scholar 

  • Elwood SK (1998) Is the persistence of shocks to output asymmetric? J Monet Econ 41:411–426

    Article  Google Scholar 

  • Enders W, Falk BL, Siklos P (2007) A threshold model of real US GDP and the problem of constructing confidence intervals in TAR models. Stud Nonlinear Dyn Econ 11(3):4

    MATH  Google Scholar 

  • Engel J, Haugh D, Pagan A (2005) Some methods for assessing the need for non-linear models in business cycles. Int J Forecast 21:651–662

    Article  Google Scholar 

  • Engle RF (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50:987–1007

    Article  MathSciNet  MATH  Google Scholar 

  • Fernández-Villaverde J, Rubio-Ramírez JF (2007) Estimating macroeconomic models: a likelihood approach. Rev Econ Stud 54:1059–1087

    Article  MathSciNet  MATH  Google Scholar 

  • Filardo AJ (1994) Business-cycle phases and their transitional dynamics. J Bus Econ Stat 12:299–308

    Google Scholar 

  • French MW, Sichel DE (1993) Cyclical patterns in the variance of economic activity. J Bus Econ Stat 11:113–119

    Google Scholar 

  • Friedman M (1964) Monetary studies of the National Bureau, the National Bureau Enters its 45th year. 44th annual report. NBER, New York, pp 7–25. Reprinted in: Friedman M (1969) The optimum quantity of money and other essays. Aldine, Chicago, pp 261–284

    Google Scholar 

  • Friedman M (1993) The “plucking model” of business fluctuations revisited. Econ Inq 31:171–177

    Article  Google Scholar 

  • Galvão AB (2002) Can non-linear time series models generate US business cycle asymmetric shape? Econ Lett 77:187–194

    Article  MATH  Google Scholar 

  • Garcia R (1998) Asymptotic null distribution of the likelihood ratio test in Markov switching models. Int Econ Rev 39:763–788

    Article  MathSciNet  Google Scholar 

  • Garcia R, Schaller H (2002) Are the effects of interest rate changes asymmetric? Econ Inq 40:102–119

    Article  Google Scholar 

  • Gilchrist S, Williams JC (2000) Putty-clay and investment: a business cycle analysis. J Polit Econ 108:928–960

    Article  Google Scholar 

  • Goodwin TH (1993) Business-cycle analysis with a Markov-switching model. J Bus Econ Stat 11:331–339

    ADS  Google Scholar 

  • Granger CWJ, Andersen AP (1978) An introduction to bilinear time series models. Vandenhoeck and Ruprecht, Gottingen

    MATH  Google Scholar 

  • Granger CWJ, Terasvirta T (1993) Modelling nonlinear economic relationships. Oxford University Press, Oxford

    MATH  Google Scholar 

  • Hamilton JD (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57:357–384

    Article  MathSciNet  MATH  Google Scholar 

  • Hamilton JD (2005) What’s real about the business cycle? Fed Reserv Bank St Louis Rev 87:435–452

    Google Scholar 

  • Hansen BE (1992) The likelihood ratio test under nonstandard conditions: testing the Markov switching model of GNP. J Appl Econ 7:S61–S82

    Article  Google Scholar 

  • Hansen BE (1996) Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica 64:413–430

    Article  MathSciNet  MATH  Google Scholar 

  • Hansen BE (1997) Inference in TAR models. Stud Nonlinear Dyn Econ 2:1–14

    MathSciNet  MATH  Google Scholar 

  • Hansen GD, Prescott EC (2005) Capacity constraints, asymmetries, and the business cycle. Rev Econ Dyn 8:850–865

    Article  Google Scholar 

  • Harding D, Pagan AR (2002) Dissecting the cycle: a methodological investigation. J Monet Econ 49:365–381

    Article  Google Scholar 

  • Harding D, Pagan AR (2003) A comparison of two business cycle dating methods. J Econ Dyn Control 27:1681–1690

    Article  MATH  Google Scholar 

  • Harding D, Pagan AR (2005) A suggested framework for classifying the modes of cycle research. J Appl Econ 20:151–159

    Article  MathSciNet  Google Scholar 

  • Hess GD, Iwata S (1997a) Asymmetric persistence in GDP? A deeper look at depth. J Monet Econ 40:535–554

    Article  Google Scholar 

  • Hess GD, Iwata S (1997b) Measuring and comparing business-cycle features. J Bus Econ Stat 15:432–444

    Google Scholar 

  • Howitt P, McAfee RP (1992) Animal spirits. Am Econ Rev 82:493–507

    Google Scholar 

  • Hristova D (2005) Maximum likelihood estimation of a unit root bilinear model with an application to prices. Stud Nonlinear Dyn Econ 9(1):4

    MathSciNet  MATH  Google Scholar 

  • Keynes JM (1936) The general theory of employment, interest, and money. Macmillan, London

    Google Scholar 

  • Kiefer NM (1978) Discrete parameter variation: efficient estimation of a switching regression model. Econometrica 46:413–430

    Article  MathSciNet  MATH  Google Scholar 

  • Kim CJ (1994) Dynamic linear models with Markov switching. J Econ 60:1–22

    Article  MATH  MathSciNet  Google Scholar 

  • Kim CJ, Murray CJ (2002) Permanent and transitory components of recessions. Empir Econ 27:163–183

    Article  Google Scholar 

  • Kim CJ, Nelson CR (1998) Business cycle turning points, a new coincident index, and tests of duration dependence based on a dynamic factor model with regime switching. Rev Econ Stat 80:188–201

    Article  Google Scholar 

  • Kim CJ, Nelson CR (1999a) State-space models with regime switching: classical and Gibbs- sampling approaches with applications. MIT Press, Cambridge

    Google Scholar 

  • Kim CJ, Nelson CR (1999b) Friedman’s plucking model of business fluctuations: tests and estimates of permanent and transitory components. J Money Credit Bank 31:317–334

    Google Scholar 

  • Kim CJ, Nelson CR (2001) A Bayesian approach to testing for Markov-switching in univariate and dynamic factor models. Int Econ Rev 42:989–1013

    Article  Google Scholar 

  • Kim CJ, Piger JM (2002) Common stochastic trends, common cycles, and asymmetry in economic fluctuations. J Monet Econ 49:1181–1211

    Article  Google Scholar 

  • Kim M-J, Yoo J-S (1995) New index of coincident indicators: a multivariate Markov switching factor model approach. J Monet Econ 36:607–630

    Article  Google Scholar 

  • Kim S, Shephard N, Chib S (1998) Stochastic volatility: likelihood inference and comparison with ARCH models. Rev Econ Stud 65:361–393

    Article  MATH  Google Scholar 

  • Kim CJ, Nelson CR, Piger J (2004) The less-volatile US economy: a Bayesian investigation of timing, breadth, and potential explanations. J Bus Econ Stat 22:80–93

    Article  MathSciNet  Google Scholar 

  • Kim CJ, Morley J, Piger J (2005) Nonlinearity and the permanent effects of recessions. J Appl Econ 20:291–309

    Article  MathSciNet  MATH  Google Scholar 

  • Kim CJ, Piger J, Startz R (2007) The dynamic relationship between permanent and transitory components of US business cycles. J Money Credit Bank 39:187–204

    Article  Google Scholar 

  • Kim CJ, Piger J, Startz R (2008a) Estimation of Markov regime-switching regression models with endogenous switching. J Econ 143:263–273

    Article  MathSciNet  Google Scholar 

  • Kim CJ, Morley J, Piger J (2008b) Bayesian counterfactual analysis of the sources of the great moderation. J Appl Econ 23:173–191

    Article  MathSciNet  Google Scholar 

  • King TB (2006) Dynamic equilibrium models with time-varying structural parameters. Working paper

    Google Scholar 

  • King TB, Morley J (2007) Maximum likelihood estimation of nonlinear, non-Gaussian state-space models using a multistage adaptive particle filter. Working paper

    Google Scholar 

  • Koop G, Potter S (2003) Bayesian analysis of endogenous delay threshold models. J Bus Econ Stat 21:93–103

    Article  MathSciNet  Google Scholar 

  • Koop G, Potter S (2006) The vector floor and ceiling model. In: Milas C, Rothman P, Van Dijk D (eds) Nonlinear time series analysis of business cycles. Elsevier, Amsterdam, pp 97–131

    Chapter  Google Scholar 

  • Koop G, Potter S (2007) Estimation and forecasting in models with multiple breaks. Rev Econ Stud 74:763–789

    Article  MathSciNet  MATH  Google Scholar 

  • Koop G, Pesaran MH, Potter S (1996) Impulse response analysis in nonlinear multivariate models. J Econ 74:119–148

    Article  MathSciNet  MATH  Google Scholar 

  • Korenok O, Mizrach B, Radchenko S (2009) A note on demand and supply factors in manufacturing output asymmetries. Macroecon Dyn 13(2):263–277

    Google Scholar 

  • Lam PS (1990) The Hamilton model with a general autoregressive component: estimation and comparison with other models of economic time series. J Monet Econ 26:409–432

    Article  Google Scholar 

  • Leamer EE, Potter SM (2004) A nonlinear model of the business cycle. Working paper

    Google Scholar 

  • Leyton AP, Smith D (2000) A further note of the three phases of the US business cycle. Appl Econ 32:1133–1143

    Article  Google Scholar 

  • Lo MC, Piger J (2005) Is the response of output to monetary policy asymmetric? Evidence from a regime-switching coefficients model. J Money Credit Bank 37:865–887

    Article  Google Scholar 

  • Lucas RE (1972) Econometric testing of the natural rate hypothesis. In: Eckstein O (ed) Econometrics of price determination. US Federal Reserve Board, Washington, DC, pp 50–59

    Google Scholar 

  • Lucas RE (1976) Econometric policy evaluation: a critique. In: Brunner K, Meltzer A (eds) The Phillips curve and labor markets. Carnegie-Rochester conference series on public policy, North-Holland Publishing Company, Amsterdam, vol 1. pp 19–46

    Google Scholar 

  • Lucas RE (1987) Models of business cycles. Basil Blackwell, Oxford

    Google Scholar 

  • Lucas RE (2003) Macroeconomic priorities. Am Econ Rev 93:1–14

    Article  Google Scholar 

  • Ma J (2007) Consumption persistence and the equity premium puzzle: new evidence based on improved inference. Working paper

    Google Scholar 

  • MacKinnon J (2002) Bootstrap inference in econometrics. Can J Econ 35:615–645

    Article  Google Scholar 

  • MacKinnon J (2006) Bootstrap methods in econometrics. Econ Rec 82:S2–S18

    Article  Google Scholar 

  • McConnell MM, Quiros GP (2000) Output fluctuations in the United States: what has changed since the early 1980s? Am Econ Rev 90:1464–1476

    Article  Google Scholar 

  • McQueen G, Thorley SR (1993) Asymmetric business cycle turning points. J Monet Econ 31:341–362

    Article  Google Scholar 

  • Mitchell WA (1927) Business cycles: the problem and its setting. NBER, New York

    Google Scholar 

  • Mizon GE, Richard JF (1986) The encompassing principle and its application to non-nested hypotheses. Econometrica 54:657–678

    Article  MathSciNet  MATH  Google Scholar 

  • Morley J, Piger J (2006) The importance of nonlinearity in reproducing business cycle features. In: Milas C, Rothman P, Van Dijk D (eds) Nonlinear time series analysis of business cycles. Elsevier, Amsterdam, pp 75–95

    Chapter  Google Scholar 

  • Morley J, Piger J (2008a) Trend/cycle decomposition of regime-switching processes. J Econ 146(2):220–226

    Google Scholar 

  • Morley J, Piger J (2008b) The asymmetric business cycle. Working paper

    Google Scholar 

  • Morley JC, Nelson CR, Zivot E (2003) Why are the Beveridge-Nelson and unobserved- components decompositions of GDP so different? Rev Econ Stat 85:235–243

    Article  Google Scholar 

  • Neftçi SH (1984) Are economic time series asymmetric over the business cycle? J Polit Econ 92:307–328

    Article  Google Scholar 

  • Niemira MP, Klein PA (1994) Forecasting financial and economic cycles. Wiley, New York

    Google Scholar 

  • Öcal N, Osborn DR (2000) Business cycle non-linearities in UK consumption and production. J Appl Econ 15:27–44

    Article  Google Scholar 

  • Owyang MT, Ramey G (2004) Regime switching and monetary policy measurement. J Monet Econ 51:1577–1198

    Article  Google Scholar 

  • Peel D, Davidson J (1998) A non-linear error correction mechanism based on the bilinear model. Econ Lett 58:165–170

    Article  MATH  Google Scholar 

  • Pesaran MH, Potter SM (1997) A floor and ceiling model of US output. J Econ Dyn Control 21:661–695

    Article  MathSciNet  MATH  Google Scholar 

  • Potter SM (1995) A nonlinear approach to US GNP. J Appl Econ 10:109–125

    Article  Google Scholar 

  • Potter SM (2000) A nonlinear model of the business cycle. Stud Nonlinear Dyn Econ 4:85–93

    Article  MATH  Google Scholar 

  • Primiceri GE (2005) Time varying structural vector autoregressions and monetary policy. Rev Econ Stud 72:821–852

    Article  MathSciNet  MATH  Google Scholar 

  • Ramsey JB, Rothman P (1996) Time irreversibility and business cycle asymmetry. J Money Credit Bank 28:1–21

    Article  Google Scholar 

  • Ravn MO, Sola M (1995) Stylized facts and regime changes: are prices procyclical? J Monet Econ 36:497–526

    Article  Google Scholar 

  • Rotemberg JJ, Woodford M (1996) Real-business-cycle models and the forecastable movements in output, hours, and consumption. Am Econ Rev 86:71–89

    Google Scholar 

  • Rothman P (1991) Further evidence on the asymmetric behavior of unemployment rates over the business cycle. J Macroecon 13:291–298

    Article  Google Scholar 

  • Rothman P (1998) Forecasting asymmetric unemployment rates. Rev Econ Stat 80:164–168

    Article  Google Scholar 

  • Rothman P (2008) Reconsideration of Markov chain evidence on unemployment rate asymmetry. Stud Nonlinear Dyn Econ 12(3):6

    MathSciNet  Google Scholar 

  • Rothman P, van Dijk D, Franses PH (2001) A multivariate STAR analysis of the relationship between money and output. Macroeconom Dyn 5:506–532

    MATH  Google Scholar 

  • Schumpeter J (1942) Capitalism, socialism, and democracy. Harper, New York

    Google Scholar 

  • Sensier M, van Dijk D (2004) Testing for volatility changes in US macroeconomic time series. Rev Econ Stat 86:833–839

    Article  Google Scholar 

  • Sichel DE (1993) Business cycle asymmetry: a deeper look. Econ Inq 31:224–236

    Article  Google Scholar 

  • Sichel DE (1994) Inventories and the three phases of the business cycle. J Bus Econ Stat 12:269–277

    Google Scholar 

  • Sims CA (2001) Comment on Sargent and Cogley’s: evolving post-World War II US inflation dynamics. In: Bernanke BS, Rogoff K (eds) NBER macroeconomics annual 2001. MIT Press, Cambridge, pp 373–379

    Google Scholar 

  • Sims CA, Zha T (2006) Were there regime switches in US monetary policy? Am Econ Rev 96:54–81

    Article  Google Scholar 

  • Sinclair TM (2008) Asymmetry in the business cycle: Friedman’s plucking model with correlated innovations. Working paper

    Google Scholar 

  • Stock JH, Watson MW (2002) Has the business cycle changed and why? In: Gertler M, Rogoff K (eds) NBER macroeconomics annual 2002. MIT Press, Cambridge, pp 159–218

    Google Scholar 

  • Subba Rao T, Gabr MM (1984) An introduction to bispectral analysis and bilinear time series models, vol 24, Lecture notes in statistics. Springer, New York

    MATH  Google Scholar 

  • Terasvirta T (1994) Specification, estimation, and evaluation of smooth transition autoregressive models. J Am Stat Assoc 89:208–218

    MATH  Google Scholar 

  • Teräsvirta T (1995) Modeling nonlinearity in US gross national product 1889–1987. Empir Econ 20:577–598

    Article  Google Scholar 

  • Teräsvirta T (1998) Modelling economic relationships with smooth transition regressions. In: Ullah A, Giles DEA (eds) Handbook of applied economic statistics. Marcel Dekker, New York, pp 507–552

    Google Scholar 

  • Teräsvirta T, Anderson HM (1992) Characterizing nonlinearities in business cycles using smooth transition autoregressive models. J Appl Econ 7:S119–S136

    Article  Google Scholar 

  • Tiao GC, Tsay RS (1994) Some advances in non-linear and adaptive modeling in time- series analysis. J Forecast 13:109–131

    Article  Google Scholar 

  • Tong H (1978) On a threshold model. In: Chen CH (ed) Pattern recognition and signal processing. Sijthoff and Noordhoff, Amsterdam, pp 575–586

    Chapter  Google Scholar 

  • Tsay RS (1989) Testing and modeling threshold autoregressive processes. J Am Stat Assoc 84:231–240

    Article  MathSciNet  MATH  Google Scholar 

  • Tsay RS (1998) Testing and modeling multivariate threshold processes. J Am Stat Assoc 93:1188–1202

    Article  MathSciNet  MATH  Google Scholar 

  • van Dijk D, Franses PH (1999) Modeling multiple regimes in the business cycle. Macroecon Dyn 3:311–340

    Article  MATH  Google Scholar 

  • van Dijk D, Franses PH (2003) Selecting a nonlinear time series model using weighted tests of equal forecast accuracy. Oxf Bull Econ Stat 65:727–744

    Article  Google Scholar 

  • Wynne MA, Balke NS (1992) Are deep recessions followed by strong recoveries? Econ Lett 39:183–189

    Article  Google Scholar 

  • Yellen JL, Akerlof GA (2006) Stabilization policy: a reconsideration. Econ Inq 44:1–22

    Article  Google Scholar 

Books and Reviews

  • Davidson R, MacKinnon JG (2004) Econometric theory and methods. Oxford University Press, Oxford

    Google Scholar 

  • Diebold FX (1998) The past, present, and future of macroeconomic forecasting. J Econ Perspect 12:175–192

    Article  Google Scholar 

  • Engle R (2001) GARCH 101: the use of ARCH/GARCH models in applied econometrics. J Econ Perspect 15:157–168

    Article  Google Scholar 

  • Franses PH (1998) Time series models for business and economic forecasting. Cambridge University Press, Cambridge

    Google Scholar 

  • Hamilton JD (1994a) State-space models. In: Engle RF, McFadden DL (eds) Handbook of econometrics, vol 4. Elsevier, Amsterdam, pp 041–3080

    Google Scholar 

  • Hamilton JD (1994b) Time series analysis. Princeton University Press, Princeton

    MATH  Google Scholar 

  • Kim CJ, Nelson CR (1999) Has the US economy become more stable? A Bayesian approach based on a Markov-switching model of the business cycle. Rev Econ Stat 81:608–616

    Google Scholar 

  • Koop G (2003) Bayesian econometrics. Wiley, Chichester

    Google Scholar 

  • Teräsvirta T, Tjøstheim D, Granger CWJ (1994) Aspects of modeling nonlinear time series. In: Engle RF, McFadden DL (eds) Handbook of econometrics, vol 4. Elsevier, Amsterdam, pp 2919–2957

    Google Scholar 

  • Tsay RS (2005) Analysis of financial time series. Wiley, Hoboken

    Book  MATH  Google Scholar 

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    James Morley

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Morley, J. (2015). Macroeconomics, Nonlinear Time Series in. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27737-5_316-3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Online ISBN: 978-3-642-27737-5

  • eBook Packages: Springer Reference Physics and AstronomyReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics

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