These are the paper replication examples provided with RATS. Below is an alphabetical listing. The web site has these (and the general example programs) organized by subject and/or by any of the authors. Note that most of these will be installed with the software into the "Paper Replication Examples" directory. Each paper has a separate directory within that which includes the program(s) and data files.


Some of these have detailed description in this section of the help, some have detailed descriptions as part of one of the RATS e-courses, and some have both.



Arellano and Bond (1991), “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations”, Review of Economic Studies, vol. 58, no. 2, pp. 277-97. Demonstrates dynamic panel data models using GMM. This is included in the Panel and Grouped Data e-course.


Aruoba, Diebold and Scotti (2009), “Real-Time Measurement of Business Conditions”, Journal of Business and Economic Statistics, vol. 27, no. 4, pp. 417-427. This is a large-scale state-space model with mixed frequency observables.


Bai, Lumsdaine and Stock (1998), “Testing For and Dating Common Breaks in Multivariate Time Series”, Review of Economic Studies, vol. 65, no. 3, pp. 394-432. Calculates break statistics on a multivariate models with a common break across all equations.


Bai and Perron (2003), “Computation and analysis of multiple structural change models”, Journal of Applied Econometrics, vol. 18, no. 1, 1-22.


Baillie and Bollerslev (1989), “The Message in Daily Exchange Rates: A Conditional Variance Tale”, Journal of Business and Economic Statistics, vol. 7, no. 3, pp. 297-305. Estimates univariate GARCH models with day-of-the-week effects.


Baillie, Bollerslev and Mikkelson (1996), “Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, vol. 74, pp. 3-30. Original paper on FIGARCH models.


Balcilar, Gupta, Miller(2015), “Regime switching model of US crude oil and stock market prices: 1859 to 2013”, Energy Economics, vol 49, 317-327. Markov Switching VECM with regime-specific impulse response functions.


Balke(2000), “Credit and Economic Activity: Credit Regimes and Nonlinear Propagation of Shocks,” Review of Economics and Statistics, vol 82, 344-349. Threshold VAR, with testing, estimation, and non-linear impulse responses. This is included in the Structural Breaks and Switching Models e-course.


Balke and Fomby (1997), “Threshold Cointegration”, International Economic Review, vol 38, no 3, pp. 627-45. This is included in the Structural Breaks and Switching Models e-course.


Bauwens and Laurent(2005), “A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models,” Journal of Business & Economic Statistics, vol 23, pp 346-354. This is included in the ARCH/GARCH and Volatility Models e-course.


Bernanke, Boivin and Eliasz (2005), “Measuring the Effects of Monetary Policy: A Factor-augmented Vector Autoregressive (FAVAR) Approach”, Quarterly Journal of Economics, vol. 120, no 1, pp. 387-422.


Bernanke and Mihov (1998), “Measuring Monetary Policy”, Quarterly Journal of Economics, vol. 113, no. 3, pp. 869-902 (monthly data calculations). This includes maximum likelihood estimation of structural VAR’s, Mar­kov switching estimate of an SVAR and Monte Carlo integration of a just identified SVAR. A simplified version of their structural VAR is included in the Vector Autoregressions e-course.


Bjørnland and Leitemo (2009): “Identifying the Interdependence between US Monetary Policy and the Stock Market”, Journal of Monetary Economics, vol. 56, no. 2, pp. 275-282. Examines a VAR model with short- and long-run restrictions, including Monte Carlo integration of error bands for impulse responses.


Blanchard and Quah (1989), “The Dynamic Effects of Aggregate Demand and Supply Disturbances”, American Economic Review, vol. 79, no. 4, pp. 655-673. Demonstrates several topics in VAR’s: historical decomposition, recovery of structural shocks, long-run restrictions. This is included in the Vector Autoregressions e-course.


Bollerslev and Mikkelson (1996), “Modeling and Pricing Long Memory in Stock Market Volatility”, Journal of Econometrics, vol. 73, no. 1, pp. 151-184. Estimates Fractionally Integrated GARCH and EGARCH models.


Burnside (1994), “Hansen-Jagannathan Bounds as Classical Tests of Asset-Pricing Models”, Journal of Business & Economic Statistics, vol. 12, no 1, pp. 57-79.


Camacho (2011), “Markov-switching models and the unit root hypothesis in real U.S. GDP”, Economics Letters, vol. 112, 161-164. Estimation and testing of a Markov switching unit root model.


Campbell and Ammer (1993), “What Moves the Stock and Bond Markets? A Variance Decomposition for Long-Term Asset Returns”, J of Finance, vol. 48, pp. 3-37.


Cappiello, Engle and Sheppard (2006), “Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns”, Journal of Financial Econometrics, vol. 4, no. 4, pp. 537-572. Example of two-step estimates of various DCC GARCH models.


Carstenson (2006), “Stock Market Downswing and the Stability of European Monetary Union Money Demand”, Journal of Business and Economic Statistics, vol. 24, no. 4, pp. 395-402. Demonstrates FM OLS techniques and a Hansen stability test.


Cecchetti and Rich(2001), “Structural Estimates of the U.S. Sacrifice Ratio,” Journal of Business and Economic Statistics, vol. 19, no 4, 416-427. Estimation of several small structural VAR's.


Chan, Karolyi, Longstaff and Sanders (1992), “Comparison of Models of the Short-Term Interest Rate”, Journal of Finance, vol. 47, no. 3, pp. 1209-1227.


Chan and Maheu (2002), “Conditional Jump Dynamics in Stock Market Returns”, Journal of Business and Economic Statistics, vol. 20, no. 3, pp. 377-389. This does constant and time-varying intensity (ARJI) GARCH models.


Chan & McAleer(2003), “Estimating smooth transition autoregressive models with GARCH errors in the presence of extreme observations and outliers,” Applied Financial Economics, vol. 13, no 8, pp 581-592. Estimation of STAR-GARCH models.


Cushman and Zha(1997), “Identifying monetary policy in a small open economy under flexible exchange rates,” Journal of Monetary Economics, vol 39, no 3, pp 433-448.


den Haan (2000) , “The Comovement Between Output and Prices”, Journal of Monetary Economics, vol. 46, no. 1, pp. 3-30. This analyzes the comovement of series using multi-step forecast errors in a VAR.


Dennis (2007), “Optimal Policy in Rational Expectations Models: New Solution Algorithms”, Macroeco­nomic Dynamics, vol 11, 31-55. Optimal control calculations in a DSGE.


Diebold, Rudebusch & Aruoba (2006), “The macroeconomy and the yield curve: a dynamic latent factor approach”, Journal of Econometrics, vol. 131(1-2), 309-338.


Diebold and Yilmaz (2009), “Measuring Financial Asset Return and Volatility Spillovers, with Applica­tion to Global Equity Markets”, Economic Journal, vol. 119, no. 534, pp. 158-171. Analyzes spillovers in a multi-country VAR.


Diebold and Yilmaz(2012), “Better to give than to receive: Predictive directional measurement of volatil­ity spillovers,” International Journal of Forecasting, vol. 28, no 1, 57-66. Extends 2009 paper to allow for “generalized” error decompositions.


Dueker (1997), “Markov Switching in garch Processes and Mean-Reverting Stock-Market Volatility”, Journal of Business & Economic Statistics, vol. 15, no. 1, pp. 26–34. Implements Markov Switching GARCH models. Dueker's method (applied to a different data set) is included in the Structural Breaks and Switching Models e-course.


Dueker (2005), “Dynamic Forecasts of Qualitative Variables: A Qual VAR Model of U.S. Recessions”, Journal of Business & Economic Statistics, vol. 23, no. 1, pp. 96–104. Estimates a VAR including a binary choice endogenous variable.


Ehrmann, Ellison, Valla (2003), “Regime-dependent Impulse Response Functions in a Markov-switching Vector Autoregression Model”, Economics Letters, vol. 78, pp. 295–299. Estimates a Markov-switching VAR with error band calculations for the IRF’s. This is included in the Structural Breaks and Switching Models e-course.


Elder and Serletis (2010), “Oil Price Uncertainty”, Journal of Money, Credit, and Banking, vol. 42, no. 6, pp. 1137-1159. Estimates a VAR-GARCH-M model and computes error bands for impulse response functions. This is included in the ARCH/GARCH and Volatility Models e-course.


Enders and Granger (1998), “Unit-Root Tests and Asymmetric Adjustment with an Example Using the Term Structure of Interest Rates”, Journal of Business and Economic Statistics, vol. 16, pp 304-11. This uses the @EndersGranger procedure. Details on this are included in the Structural Breaks and Switching Models e-course.


Enders and Siklos (2001), “Cointegration and Threshold Adjustment”, Journal of Business & Economic Statistics, vol. 19, no. 2, pp. 166–76. Presents an error correction model with threshold adjustments. This uses the @EndersSiklos procedure.


Erceg, Henderson and Levin (2000), “Optimal Monetary Policy with Staggered Wage and Price Contracts”, Journal of Monetary Economics, vol. 46, no. 2, pp. 281-313. DSGE model estimation and use of @DLMIRF procedure.


Fabiani and Mestre (2004), “A System Approach for Measuring the Euro Area NAIRU”, Empirical Eco­nomics, vol.. 29, no. 2, pp. 311-341. Demonstrates state-space model with multiple observables.


Faust (1998), “The Robustness of Identified VAR Conclusions About Money”, Carnegie-Rochester Conference Series on Public Policy, vol. 49, pp. 207-244. Aimed at bounding the amount of the variance in GDP that can be explained by a monetary policy shock, by maximizing the FEVD share of a shock across all shocks that satisfy a set of sign constraints that could reasonably be produced by a (contractionary) monetary policy shock. This uses a different method for solving the maximization problem than is proposed by Faust. It uses the existing constrained optimization capabilities of RATS rather than a specialized eigenvalue solution.


Faust and Leeper (1997), “When Do Long-Run Identifying Restrictions Give Reliable Results”, Journal of Business & Economic Statistics, vol. 15, no. 3, pp. 345–353. Examines small SVAR’s with short- and long-run restrictions.


Filardo (1994), “Business Cycle Phases and Their Transitional Dynamics”, Journal of Business & Economic Statistics, vol. 12, no 3, pp. 299-308. Demonstrates use of the @MSVARSetup procedure with transition probabilities depending upon exogenous variables.


Fry and Pagan (2011), “Sign Restrictions in Structural Vector Autoregressions: A Critical Review,” Journal of Economic Literature, vol. 49, no. 4, 938-60. Shows how to find the median target impulse response. This is included in the Vector Autoregressions e-course with a different model.


Gali (1992), “How Well Does the IS-LM Model Fit Postwar U.S. Data”, Quarterly Journal of Economics, vol. 107, no. 2, pp. 709-738. Demonstrates VAR’s with short- and long-run restrictions, particularly the instruction CVMODEL and the @ShortandLong procedure.


Gali (1999), “Technology, Employment and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations”, American Economic Review, vol. 89, pp. 249-271.


González-Rivera(1998), “Smooth-Transition GARCH Models,” Studies in Nonlinear Dynamics & Econometrics, vol. 3, no 2, 1-20.


Gonzalo and Granger (1995), “Estimation of common long memory components in cointegrated systems”, Journal of Business and Economic Statistics, vol. 13, pp. 27-36. Demonstrates the procedure @JOHMLE and the function %PERP.


Gray (1996), “Modeling the Conditional Distribution of Interest Rates as a Regime-switching Process”, Journal of Financial Economics, vol. 42, pp. 27-62. Replicates work on Markov-switching GARCH models. This is included in the Structural Breaks and Switching Models e-course.


Gregory and Hansen (1996), “Residual-based Tests for Cointegration in Models with Regime Shifts”, Journal of Econometrics, vol. 70, no. 1, pp. 99-126. Demonstrates @GregoryHansen procedure.


Hafner and Herwartz (2006), “Volatility Impulse Responses for Multivariate GARCH Models: An Ex­change Rate Illustration”, Journal of International Money and Finance, vol. 25, no. 5, pp. 719-740. This is included in the ARCH/GARCH and Volatility Models e-course.


Hamilton and Susmel (1994), “Autoregressive Conditional Heteroskedasticity and Changes in Regime”, Journal of Econometrics, vol. 64, pp. 307-333. Estimation of standard GARCH and ARCH models with Markov Switching.


Hansen, Bruce (1996), “Inference When a Nuisance Parameter is Not Identified Under the Null Hypothesis”, Econometrica, vol. 64, no. 2, pp. 413-430. Replication program for a SETAR model. Demonstrates use of @TAR procedure.


Hansen, Bruce (1994), “Autoregressive Conditional Density Estimation”, International Economic Review, vol 35, no. 3, pp. 705-730. This estimates GARCH models with student t errors with time-varying degrees of freedom, and introduces the skew-t density.


Hansen, Bruce (1997) “Approximate Asymptotic P–Values for Structural Change Tests”, Journal of Busi­ness and Economic Statistics, vol. 15, no. 1, pp. 60–67. Example using the @APBREAKTEST procedure, which uses Hansen’s approximate p–values.


Hansen, Bruce (1999), “Threshold Effects in Non-dynamic Panels: Estimation, Testing and Inference”, Journal of Econometrics, vol. 93, pp. 345–368. Example using the @PANELTHRESH procedure for estimation of threshold effects in a panel data set.


Hansen, Bruce (2000), “Testing for Structural Change in Conditional Models”, Journal of Econometrics, vol. 97, no. 1, pp. 93-115. Demonstrates test for a structural break in a linear regression model with fixed regressor bootstrap.


Hansen and Seo (2002), “Testing for two-regime threshold cointegration in vector error-correction models”, Journal of Econometrics, vol. 110, 293-318.


Harvey, Ruiz and Shepherd (1994), “Multivariate Stochastic Variance Models”, Review of Economic Studies, vol. 61, no. 2, pp. 247-264.


Holtz-Eakin, Newey and Rosen (1988), “Estimating Vector Autoregressions with Panel Data”, Econometrica, vol. 56, no. 6, pp. 1371-95. Panel VAR estimation, demonstrating the use of the @ABLAGS procedure for generating Arellano-Bond instruments. This is a different data set (provided by the authors) of the same technique. This is included in the Panel and Grouped Data e-course.


Inclan and Tiao (1994), “Use of Cumulative Sums of Squares for Retrospective Detection of Changes in Variance”, Journal of the American Statistical Association, vol. 89, pp. 913-923.


Ireland (2004), “A Method for Taking Models to the Data”, Journal of Economic Dynamics and Control, vol. 28, no. 6, pp. 1205-1226. Demonstrates use of DSGE instruction, combined with DLM, for estimating a dynamic model using maximum likelihood.


Jacquier, Polson and Rossi (1994), “Bayesian Analysis of Stochastic Volatility Models”, Journal of Business and Economic Statistics, vol 12, no 4, pp. 371-89. Includes both a Metropolis MCMC and a rejection method similar to that in Kim, Shephard and Chib(1998), “Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models”, Review of Economic Studies, vol 65, pp. 361-93.


Jorda (2005), "Estimation and Inference of Impulse Responses by Local Projections", American Economic Review, vol 95, no 1, pp 161-182. Alternative method of computing impulse response functions by regressions on deep-lag regressions.


Kilian(1998), “Small-Sample Confidence Intervals for Impulse Response Functions”, Review of Economics and Statistics, vol 80, no 2, 218-230. Bootstrap within bootstrap for error bands of impulse responses in a VAR. This is covered in the Vector Autoregressions e-course.


Kilian & Vigfusson(2011), “Are the responses of the U.S. economy asymmetric in energy price increases and decreases?”, Quantitative Economics, vol. 2, no 3, 419-453. Analysis of an asymmetric VAR.


King, Plosser, Stock and Watson (1991), “Stochastic Trends and Economic Fluctuations”, American Economic Review, vol. 81, pp. 819-840. Demonstrates the procedures @SWDOLS, @SWTRENDS, @JOHMLE and @FORCEDFACTOR.


Koop, León-González and Strachan (2010), “Efficient Posterior Simulation for Cointegrated Models with Priors on the Cointegration Space”, Econometric Reviews, vol. 29, no. 2, pp. 224-242. This is an example of the Gibbs sampling procedure for cointegrated models.


Koutmos(1996), “Modeling the Dynamic Interdependence of Major European Stock Markets”, Journal of Business Finance and Accounting, vol 23, pp 975-988. This estimates a multivariate EGARCH model with asymmetric volatility spillovers. This is included in the ARCH/GARCH and Volatility Models e-course.


Krolzig’s “International Business Cycles: Regime Shifts in the Stochastic Process of Economic Growth”, Oxford University Discussion Paper. Demonstrates Markov switching and VAR models.


Lanne and Lutkepohl (2008), “Identifying Monetary Policy Shocks via Changes in Volatility”, Journal of  Money, Credit, and Banking, vol. 40, no. 6, pp. 1131-1149. Demonstrates identification of a structural VAR using volatility regimes.


Laubach and Williams(2003), “Measuring the Natural Rate of Interest”, Review of Economics and Statistics, vol. 85, no 4, 1063-1070. Estimation of multivariate state-space models.


Lebo and Box-Steffensmeier (2008), “Dynamic Conditional Correlations in Political Science”, American Journal of Political Science, vol. 53, no. 2, pp. 688-704. Demonstrates DCC GARCH models.


Lee(1994), “Spread and volatility in spot and forward exchange rates,” Journal of International Money and Finance, vol. 13, no 3, 375-383. Estimation of a VECM with GARCH error process.


Lubik and Schorfheide (2007), “Do Central Banks Respond to Exchange Rate Movements? A Structural Investigation”, Journal of Monetary Economics, vol. 54(4), pp. 1069-1087.


Mark and Sul (2003), “Cointegration Vector Estimation by Panel DOLS and Long-run Money Demand”, Oxford Bulletin of Economics and Statistics, vol. 65, no. 5, pp. 655-680. This is included in the Panel and Grouped Data e-course.


Matheson and Stavrev(2013), “The Great Recession and the inflation puzzle,” Economics Letters, vol. 120, no 3, pp 468-472. NAIRU-GAP state-space model with time-varying coefficients and constrained states. This is included in the State Space Models and DSGE e-course.


Michael, Nobay and Peel (1997), “Transactions Costs and Nonlinear Adjustment in Real Exchange Rates: An Empirical Investigation”, Journal of Political Economy, vol. 105, no. 4, pp. 862-879. Estimates ESTAR models.


Morley, Nelson & Zivot (2003), “Why Are the Beveridge-Nelson and Unobserved-Components Decompositions of GDP So Different?”, The Review of Economics and Statistics, vol. 85(2), pp. 235-243. Demonstrates state space modelling.


Mountford and Uhlig (2009), “What are the Effects of Fiscal Policy Shocks?”, Journal of Applied Econometrics, vol. 24(6), pp. 960-992. Demonstrates analysis of impulse responses with sign constraints, and sign + zero constraints.


Ozbek and Ozlale (2005), “Employing the extended Kalman filter in measuring the output gap”, Journal of Economic Dynamics and Control, vol. 29, no. 9, pp. 1611-1622. Demonstrates extended Kalman filter.


Papell and Prodan (2006), “Additional Evidence of Long Run Purchasing Power Parity with Restricted Structural Change”, Journal of Money, Credit, and Banking, vol. 38, no. 5, pp. 1329-1349. Does unit root tests with one and two breaks using the @PerronBreaks procedure.


Pedroni (2001) “Purchasing Power Parity Tests in Cointegrated Panels”, Review of Economics and Statistics, vol. 83, pp. 727-731. Demonstrates the panel data procedures @PANELFM and @PANELDOLS.


Pedroni (2007), “Social Capital, Barriers to Production and Capital Shares: Implications for the Importance of Parameter Heterogeneity from a Nonstationary Panel Approach”, Journal of Applied Econo­metrics, vol. 22, no. 2, pp. 429-451. This demonstrates the @PANELFM and @PANCOINT. Does a test for homogeneity of the cointegrating vectors. This is included in the Panel and Grouped Data e-course.


Perron and Wada (2009), “Let’s take a break: Trends and cycles in US real GDP”, Journal of Monetary Economics, vol. 56, pp. 749-765. State space models. This is included in the State Space Models and DSGE e-course.


Pesaran, Shin and Smith (1999), “Pooled Mean Group Estimation of Dynamic Heterogeneous Panels”, Journal of the American Statistical Association, vol. 94, no. 446, pp. 621-634. Demonstrates the instruction SWEEP. This is included in the Panel and Grouped Data e-course.


Quah and Vahey (1995), “Measuring Core Inflation?”, Economic Journal, vol. 105, pp. 1130-44. Demonstrates use of the HISTORY instruction and the %BQFACTOR function.


Sadorsky(2012), “Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies”, Energy Economics, vol 34, pp 248-255. Various GARCH estimates in the energy markets with calculation of hedge ratios.


Sims and Zha (1999), “Error Bands for Impulse Responses”, Econometrica, vol. 67, no. 5, pp. 1113-1156. This includes calculation of error bands using Monte Carlo integration, bootstrapping and also includes IRF’s for a structural VAR.


Sinclair (2009), “The Relationships between Permanent and Transitory Movements in U.S. Output and the Unemployment Rate”, Journal of Money, Credit and Banking, vol. 41(2-3), pp. 529-542. State space models.


Skalin and Terasvirta (1999), “Another Look at Swedish Business Cycles, 1861-1988”, Journal of Applied Econometrics, vol. 14, no. 4, pp. 359-78. Estimation of STAR models for industrial production, imports, and exports as well as a non-linear Granger causality test.


Terasvirta (1994), “Specification, Estimation and Evaluation of Smooth Transition Autoregressive Models”, Journal of the American Statistical Association, vol. 89, pp. 208-218. Demonstrates the procedures @STARTEST, @YULELAGS, and @LAGPOLYROOTS. One of the examples is included in the Structural Breaks and Switching Models e-course.


Tsay (1998), “Testing and Modeling Multivariate Threshold Models”, Journal of the American Statistical Association, vol. 93, no. 443, pp. 1188-1202. Demonstrates use of RLS (recursive least squares) and SWEEP instructions. This (the interest rate example) is included in the Structural Breaks and Switching Models e-course.


Tse, Y.K. (2000), “A Test for Constant Correlations in a Multivariate GARCH Model”, Journal of Econometrics, 98, pp. 107-127. Implements Tse’s LM test for constant correlation in a multivariate GARCH model.


Uhlig (2005), “What are the effects of monetary policy on output? Results from an agnostic identification procedure”, Journal of Monetary Economics, vol. 52, pp. 381-419. Three programs replicating VAR identification of impulse responses with sign restrictions. This is included in the Vector Autoregressions e-course.


Watson (1993), “Measures of Fit for Calibrated Models”, Journal of Political Economy, vol. 101, 1011-1041. In addition to demonstrating Watson’s measures of fit, it also demonstrates methods for solv­ing DSGE models. Watson uses the popular King, Plosser, and Rebelo model. This demonstrates the procedures @SSMSPECTRUM, @VARSPECTRUM, and the instruction DSGE.


Watson(1994), “Business Cycle Durations and Postwar Stabilization of the U.S. Economy”, American Economic Review, vol 84, no 1, 24-46. Bry-Boschan business cycle dating.


West and Cho(1995), “The predictive ability of several models of exchange rate volatility,” Journal of Econometrics, vol. 69, no 2, 367-391. Univariate GARCH models of exchange rates. This is included in the ARCH/GARCH and Volatility Models e-course.


Willinger, Taqqu and Teverovsky (1999), “Stock Market Prices and Long-Range Dependence”, Finance and Stochastics, vol. 3, pp. 1-13. Uses the procedures @HURST and @RSSTATISTIC, and demonstrates state space models.


Wright (2000), “Alternative Variance-Ratio Tests Using Ranks and Signs”, Journal of Business and Economic Statistics, vol. 18, 1-9. Demonstrates the procedure @VRATIO.