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MacroEconometricModels.jl

A comprehensive Julia package for macroeconometric research and analysis

Overview

MacroEconometricModels.jl provides a unified, high-performance framework for estimating and analyzing macroeconometric models in Julia. The package implements state-of-the-art methods spanning the full empirical macro workflow: from unit root testing and trend-cycle decomposition, through univariate and multivariate model estimation, to structural identification and publication-quality output.

Key Features

Univariate Models

  • Time Series Filters: Hodrick-Prescott (1997), Hamilton (2018) regression, Beveridge-Nelson (1981), Baxter-King (1999) band-pass, and boosted HP (Phillips & Shi 2021) with unified trend()/cycle() accessors
  • ARIMA: AR, MA, ARMA, ARIMA estimation via OLS, CSS, MLE (Kalman filter), and CSS-MLE; automatic order selection (auto_arima); multi-step forecasting with confidence intervals
  • Volatility Models: ARCH (Engle 1982), GARCH (Bollerslev 1986), EGARCH (Nelson 1991), GJR-GARCH (Glosten et al. 1993) via MLE; Stochastic Volatility via Kim-Shephard-Chib (1998) Gibbs sampler (basic, leverage, Student-t variants); news impact curves, ARCH-LM diagnostics, multi-step forecasting
  • Spectral Analysis: Periodogram, Welch method, smoothed periodogram, AR spectral estimation; cross-spectrum with coherence, phase, gain functions; ACF/PACF/CCF with Ljung-Box/Box-Pierce/Durbin-Watson portmanteau tests

Multivariate Models

  • VAR: OLS estimation with lag order selection (AIC, BIC, HQ), stability diagnostics, companion matrix
  • Bayesian VAR: Conjugate Normal-Inverse-Wishart posterior with Minnesota prior; direct and Gibbs samplers; automatic hyperparameter optimization via marginal likelihood (Giannone, Lenza & Primiceri 2015)
  • VECM: Johansen MLE and Engle-Granger two-step estimation for cointegrated systems; automatic rank selection; IRF/FEVD/HD via VAR conversion (to_var); VECM-specific forecasting; Granger causality (short-run, long-run, strong)
  • Panel VAR: GMM estimation via Arellano-Bond (1991) first-difference and Blundell-Bond (1998) system GMM; fixed-effects OLS; Windmeijer (2005) corrected standard errors; Hansen J-test, Andrews-Lu MMSC; OIRF, GIRF, FEVD; group-level bootstrap CIs; lag selection
  • Local Projections: Jorda (2005) with extensions for IV (Stock & Watson 2018), smooth LP (Barnichon & Brownlees 2019), state-dependence (Auerbach & Gorodnichenko 2013), propensity score weighting (Angrist et al. 2018), structural LP (Plagborg-Moller & Wolf 2021), LP forecasting, and LP-FEVD (Gorodnichenko & Lee 2019)
  • Factor Models: Static (PCA), dynamic (two-step/EM), and generalized dynamic (spectral GDFM) with Bai-Ng information criteria; unified forecasting with theoretical and bootstrap CIs
  • FAVAR: Factor-augmented VAR via two-step (PCA + VAR) or Bayesian Gibbs (Carter-Kohn smoother + NIW); favar_panel_irf maps factor IRFs to N observables via loadings
  • Structural DFM: Structural dynamic factor model wrapping GDFM + VAR for identified factor shocks
  • GMM: Flexible estimation with one-step, two-step, and iterated weighting; Hansen J-test

Innovation Accounting

  • IRF: Impulse responses with bootstrap, theoretical, and Bayesian credible intervals
  • FEVD: Forecast error variance decomposition (frequentist and Bayesian)
  • Historical Decomposition: Decompose observed movements into structural shock contributions
  • LP-FEVD: R-squared, LP-A, and LP-B estimators (Gorodnichenko & Lee 2019)

DSGE Models

  • Specification: @dsge macro for domain-specific model specification with time-indexed variables, analytical or numerical steady state, and automatic Jacobian computation
  • Linear Solvers: Gensys (Sims 2002), Blanchard-Kahn (1980), Klein (2000) with automatic eigenvalue decomposition
  • Nonlinear Perturbation: Second-order (Schmitt-Grohe & Uribe 2004) and third-order perturbation with Andreasen, Fernandez-Villaverde & Rubio-Ramirez (2018) pruned simulation; Kim et al. (2008) second-order pruning
  • Global Methods: Chebyshev collocation projection (tensor and Smolyak grids), policy function iteration, value function iteration (with Howard improvement steps and Anderson acceleration)
  • Simulation and IRFs: Stochastic simulation, pruned higher-order simulation, analytical and generalized IRFs, FEVD, Lyapunov-based unconditional moments
  • GMM Estimation: IRF matching, Euler equation GMM, Simulated Method of Moments, analytical GMM via Lyapunov equation
  • Bayesian Estimation: Sequential Monte Carlo (SMC with adaptive tempering), SMC-squared (SMC² with particle filter likelihood), random-walk Metropolis-Hastings; delayed acceptance for accelerated sampling; nonlinear particle filter for higher-order solutions
  • Constraints: Perfect foresight (Newton solver), OccBin occasionally binding constraints (Guerrieri & Iacoviello 2015), built-in constrained solvers (Optim.jl box constraints, NLopt.jl nonlinear inequalities, projected Newton) with optional JuMP/Ipopt (NLP) and PATH (MCP) backends

Structural Identification

  • Cholesky, sign restrictions, long-run (Blanchard-Quah), narrative restrictions, Arias et al. (2018), Mountford-Uhlig (2009) penalty function
  • Non-Gaussian ICA: FastICA, JADE, SOBI, dCov, HSIC
  • Non-Gaussian ML: Student-t, mixture-normal, PML, skew-normal
  • Heteroskedasticity-based: Markov-switching, GARCH, smooth-transition, external volatility
  • Identifiability diagnostics: gaussianity tests, independence tests, bootstrap strength tests

Nowcasting

  • Dynamic Factor Model (DFM): EM algorithm with Kalman smoother for mixed-frequency data with arbitrary missing patterns (Banbura & Modugno 2014); Mariano-Murasawa temporal aggregation; block factor structure; AR(1)/IID idiosyncratic components
  • Large Bayesian VAR: GLP-style Normal-Inverse-Wishart prior with hyperparameter optimization via marginal likelihood (Cimadomo et al. 2022); Minnesota shrinkage with sum-of-coefficients and co-persistence priors
  • Bridge Equations: OLS bridge regressions combining pairs of monthly indicators via median (Banbura et al. 2023); transparent and fast baseline
  • News Decomposition: Attribute nowcast revisions to individual data releases via Kalman gain weights
  • Panel Balancing: balance_panel() fills NaN in TimeSeriesData/PanelData using DFM imputation

Cross-Sectional Models

  • Linear Regression: OLS with HC0–HC3 robust and cluster-robust standard errors; Weighted Least Squares (WLS); IV/2SLS with first-stage F-statistic and Sargan test; VIF multicollinearity diagnostics
  • Binary Choice: Logit and Probit MLE via IRLS; marginal effects (AME/MEM/MER) with delta-method SEs; odds_ratio(), classification_table()
  • Ordered and Multinomial: Ordered Logit/Probit MLE with cut-point estimation; Multinomial Logit MLE; Brant (1990) parallel regression test; Hausman-McFadden IIA test

Panel Models

  • Panel Regression: estimate_xtreg for FE/RE/FD/Between/CRE/Arellano-Bond/Blundell-Bond; estimate_xtiv for panel IV (FE-IV/RE-IV/FD-IV/Hausman-Taylor); estimate_xtlogit/estimate_xtprobit for panel discrete choice; Hausman, Breusch-Pagan, Pesaran CD specification tests
  • Difference-in-Differences: Five estimators — TWFE, Callaway-Sant'Anna (2021), Sun-Abraham (2021), Borusyak-Jaravel-Spiess (2024), de Chaisemartin-D'Haultfoeuille (2020); Bacon (2021) decomposition; pretrend tests; negative weight diagnostics; HonestDiD (Rambachan & Roth 2023) sensitivity analysis
  • Event Study LP: Local projection event study with staggered treatment, cluster-robust SEs
  • LP-DiD: Dube, Girardi, Jordà & Taylor (2025) LP-DiD estimator with clean control samples, PMD, and IPW reweighting

Hypothesis Tests

  • Unit root: ADF, KPSS, Phillips-Perron, Zivot-Andrews, Ng-Perron, Fourier ADF/KPSS (Enders & Lee 2012; Becker, Enders & Lee 2006), DF-GLS/ERS (Elliott, Rothenberg & Stock 1996), LM unit root with 0/1/2 breaks (Schmidt-Phillips 1992; Lee-Strazicich 2003, 2013), two-break ADF (Narayan & Popp 2010)
  • Cointegration: Johansen trace and max-eigenvalue tests, Gregory-Hansen (1996) with regime shift
  • Structural breaks: Andrews (1993) SupWald/SupLM/SupLR with 9 test variants; Bai-Perron (1998) multiple break detection via dynamic programming with BIC/LWZ/sequential selection; factor break tests — Breitung-Eickmeier (2011), Chen-Dolado-Gonzalo (2014), Han-Inoue (2015)
  • Panel unit root: Bai-Ng (2004) PANIC with factor-adjusted pooled/individual tests; Pesaran (2007) CIPS with cross-sectional augmentation; Moon-Perron (2004) factor-adjusted t-statistics
  • Granger causality: pairwise Wald, block (multivariate), all-pairs matrix
  • Model comparison: likelihood ratio (LR) and Lagrange multiplier (LM/score) tests for nested models
  • Normality: Jarque-Bera, Mardia multivariate, Doornik-Hansen, Henze-Zirkler, Royston; unified normality_test_suite()
  • ARCH diagnostics: ARCH-LM test, Ljung-Box on squared residuals
  • Stationarity diagnostics: unit_root_summary(), test_all_variables()
  • Panel VAR specification: Hansen J-test, Andrews-Lu MMSC, lag selection criteria

Visualization

  • D3.js Plotting: Zero-dependency interactive visualization via D3.js v7 with 41 plot dispatches; IRF, FEVD, historical decomposition, filter output, forecasts, model diagnostics, DiD event studies, nowcast fan charts
  • Output Formats: Self-contained HTML files with Solarized Light/Dark themes; embeddable in documentation and presentations

Data Management

  • Typed Containers: TimeSeriesData, PanelData, CrossSectionData with metadata (frequency, variable names, transformation codes)
  • Validation: diagnose() detects NaN/Inf/constant columns; fix() repairs via listwise deletion, interpolation, or mean imputation
  • FRED Transforms: apply_tcode() / inverse_tcode() implement all 7 FRED-MD transformation codes (McCracken & Ng 2016)
  • Panel Support: Stata-style xtset() for panel construction, group_data() for per-entity extraction
  • Summary Statistics: describe_data() with N, Mean, Std, Min, P25, Median, P75, Max, Skewness, Kurtosis
  • Estimation Dispatch: All estimation functions accept TimeSeriesData directly

Output and References

  • Display backends: switchable text, LaTeX, and HTML table output via set_display_backend()
  • Publication-quality tables: report(), table(), print_table()
  • Bibliographic references: refs(model) in AEA text, BibTeX, LaTeX, or HTML format (209 entries)

Installation

using Pkg
Pkg.add("MacroEconometricModels")

Or from the Julia REPL package mode:

] add MacroEconometricModels

Package Structure

The package is organized into the following modules:

ModuleDescription
data/Data containers, validation, FRED transforms, panel support, summary statistics
core/Shared infrastructure: types, utilities, display backends, covariance estimators
arima/ARIMA suite: types, Kalman filter, estimation (CSS/MLE), forecasting, order selection
filters/Time series filters: HP, Hamilton, Beveridge-Nelson, Baxter-King, boosted HP
arch/ARCH(q) estimation via MLE, volatility forecasting
garch/GARCH, EGARCH, GJR-GARCH estimation via MLE, news impact curves, forecasting
sv/Stochastic Volatility via KSC (1998) Gibbs sampler, posterior predictive forecasts
reg/Cross-sectional regression: OLS, WLS, IV/2SLS, Logit, Probit, Ordered, Multinomial, marginal effects
preg/Panel regression: FE/RE/FD/Between/CRE/AB/BB, Panel IV, Panel Logit/Probit, specification tests
spectral/Spectral analysis: periodogram, Welch, AR, cross-spectrum, ACF/PACF/CCF, portmanteau tests
var/VAR estimation (OLS), structural identification, IRF, FEVD, historical decomposition
vecm/VECM: Johansen MLE, Engle-Granger, cointegrating vectors, forecasting, Granger causality
bvar/Bayesian VAR: conjugate NIW posterior sampling, Minnesota prior, hyperparameter optimization
lp/Local Projections: core, IV, smooth, state-dependent, propensity, structural LP, forecast, LP-FEVD
factor/Static (PCA), dynamic (two-step/EM), generalized (spectral) factor models with forecasting
nongaussian/Non-Gaussian structural identification: ICA, ML, heteroskedastic-ID
teststat/Statistical tests: unit root, cointegration, structural breaks, panel unit root, normality, Granger causality, LR/LM, ARCH diagnostics
pvar/Panel VAR: types, transforms, instruments, estimation (GMM/FE-OLS), analysis, bootstrap, tests
did/Difference-in-Differences: TWFE, Callaway-Sant'Anna, Sun-Abraham, BJS, de Chaisemartin-D'Haultfoeuille; event study LP; LP-DiD
favar/Factor-Augmented VAR: two-step (PCA + VAR) and Bayesian Gibbs estimation, panel IRFs
dsge/DSGE: specification, linearization, solution (Gensys/BK/Klein/perturbation/projection/PFI/VFI), constrained solvers (Optim/NLopt/projected Newton), OccBin, Bayesian estimation (SMC/SMC²/MH)
gmm/Generalized Method of Moments and Simulated Method of Moments
nowcast/Nowcasting: DFM (EM + Kalman), large BVAR, bridge equations, news decomposition
plotting/D3.js interactive visualization: 41 plot dispatches, Solarized Light/Dark themes
summary.jlPublication-quality summary tables and refs() bibliographic references

Mathematical Notation

Throughout this documentation, we use the following notation conventions:

SymbolDescription
$y_t$$n \times 1$ vector of endogenous variables at time $t$
$Y$$T \times n$ data matrix
$p$Number of lags in VAR
$A_i$$n \times n$ coefficient matrix for lag $i$
$\Sigma$$n \times n$ reduced-form error covariance
$B_0$$n \times n$ contemporaneous impact matrix
$\varepsilon_t$$n \times 1$ structural shocks
$u_t$$n \times n$ reduced-form residuals
$h$Forecast/impulse response horizon
$H$Maximum horizon

References

Univariate Time Series

  • Box, George E. P., and Gwilym M. Jenkins. 1976. Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day. ISBN 978-0-816-21104-3.
  • Brockwell, Peter J., and Richard A. Davis. 1991. Time Series: Theory and Methods. 2nd ed. New York: Springer. ISBN 978-1-4419-0319-8.
  • Harvey, Andrew C. 1993. Time Series Models. 2nd ed. Cambridge, MA: MIT Press. ISBN 978-0-262-08224-2.

Time Series Filters

  • Hodrick, Robert J., and Edward C. Prescott. 1997. "Postwar U.S. Business Cycles: An Empirical Investigation." Journal of Money, Credit and Banking 29 (1): 1–16. https://doi.org/10.2307/2953682
  • Hamilton, James D. 2018. "Why You Should Never Use the Hodrick-Prescott Filter." Review of Economics and Statistics 100 (5): 831–843. https://doi.org/10.1162/resta00706
  • Beveridge, Stephen, and Charles R. Nelson. 1981. "A New Approach to Decomposition of Economic Time Series into Permanent and Transitory Components." Journal of Monetary Economics 7 (2): 151–174. https://doi.org/10.1016/0304-3932(81)90040-4
  • Baxter, Marianne, and Robert G. King. 1999. "Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series." Review of Economics and Statistics 81 (4): 575–593. https://doi.org/10.1162/003465399558454
  • Phillips, Peter C. B., and Zhentao Shi. 2021. "Boosting: Why You Can Use the HP Filter." International Economic Review 62 (2): 521–570. https://doi.org/10.1111/iere.12495

Volatility Models

  • Bollerslev, Tim. 1986. "Generalized Autoregressive Conditional Heteroskedasticity." Journal of Econometrics 31 (3): 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
  • Engle, Robert F. 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation." Econometrica 50 (4): 987–1007. https://doi.org/10.2307/1912773
  • Glosten, Lawrence R., Ravi Jagannathan, and David E. Runkle. 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks." Journal of Finance 48 (5): 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
  • Nelson, Daniel B. 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach." Econometrica 59 (2): 347–370. https://doi.org/10.2307/2938260
  • Kim, Sangjoon, Neil Shephard, and Siddhartha Chib. 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models." Review of Economic Studies 65 (3): 361–393. https://doi.org/10.1111/1467-937X.00050
  • Taylor, Stephen J. 1986. Modelling Financial Time Series. Chichester: Wiley. ISBN 978-0-471-90975-7.

VAR and Structural Identification

  • Blanchard, Olivier Jean, and Danny Quah. 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances." American Economic Review 79 (4): 655–673.
  • Hamilton, James D. 1994. Time Series Analysis. Princeton, NJ: Princeton University Press. ISBN 978-0-691-04289-3.
  • Kilian, Lutz, and Helmut Lutkepohl. 2017. Structural Vector Autoregressive Analysis. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781108164818
  • Lutkepohl, Helmut. 2005. New Introduction to Multiple Time Series Analysis. Berlin: Springer. ISBN 978-3-540-40172-8.
  • Sims, Christopher A. 1980. "Macroeconomics and Reality." Econometrica 48 (1): 1–48. https://doi.org/10.2307/1912017
  • Arias, Jonas E., Juan F. Rubio-Ramirez, and Daniel F. Waggoner. 2018. "Inference Based on Structural Vector Autoregressions Identified with Sign and Zero Restrictions: Theory and Applications." Econometrica 86 (2): 685–720. https://doi.org/10.3982/ECTA14468
  • Mountford, Andrew, and Harald Uhlig. 2009. "What Are the Effects of Fiscal Policy Shocks?" Journal of Applied Econometrics 24 (6): 960–992. https://doi.org/10.1002/jae.1079

Bayesian Methods

  • Doan, Thomas, Robert Litterman, and Christopher Sims. 1984. "Forecasting and Conditional Projection Using Realistic Prior Distributions." Econometric Reviews 3 (1): 1–100. https://doi.org/10.1080/07474938408800053
  • Giannone, Domenico, Michele Lenza, and Giorgio E. Primiceri. 2015. "Prior Selection for Vector Autoregressions." Review of Economics and Statistics 97 (2): 436–451. https://doi.org/10.1162/RESTa00483
  • Litterman, Robert B. 1986. "Forecasting with Bayesian Vector Autoregressions–-Five Years of Experience." Journal of Business & Economic Statistics 4 (1): 25–38. https://doi.org/10.1080/07350015.1986.10509491

VECM and Cointegration

  • Engle, Robert F., and Clive W. J. Granger. 1987. "Co-Integration and Error Correction: Representation, Estimation, and Testing." Econometrica 55 (2): 251–276. https://doi.org/10.2307/1913236
  • Johansen, Soren. 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models." Econometrica 59 (6): 1551–1580. https://doi.org/10.2307/2938278

Local Projections

  • Angrist, Joshua D., Oscar Jorda, and Guido M. Kuersteiner. 2018. "Semiparametric Estimates of Monetary Policy Effects: String Theory Revisited." Journal of Business & Economic Statistics 36 (3): 371–387. https://doi.org/10.1080/07350015.2016.1204919
  • Auerbach, Alan J., and Yuriy Gorodnichenko. 2013. "Fiscal Multipliers in Recession and Expansion." In Fiscal Policy after the Financial Crisis, edited by Alberto Alesina and Francesco Giavazzi, 63–98. Chicago: University of Chicago Press. https://doi.org/10.7208/chicago/9780226018584.003.0003
  • Barnichon, Regis, and Christian Brownlees. 2019. "Impulse Response Estimation by Smooth Local Projections." Review of Economics and Statistics 101 (3): 522–530. https://doi.org/10.1162/resta00778
  • Jorda, Oscar. 2005. "Estimation and Inference of Impulse Responses by Local Projections." American Economic Review 95 (1): 161–182. https://doi.org/10.1257/0002828053828518
  • Stock, James H., and Mark W. Watson. 2018. "Identification and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments." Economic Journal 128 (610): 917–948. https://doi.org/10.1111/ecoj.12593
  • Plagborg-Moller, Mikkel, and Christian K. Wolf. 2021. "Local Projections and VARs Estimate the Same Impulse Responses." Econometrica 89 (2): 955–980. https://doi.org/10.3982/ECTA17813
  • Gorodnichenko, Yuriy, and Byoungchan Lee. 2019. "Forecast Error Variance Decompositions with Local Projections." Journal of Business & Economic Statistics 38 (4): 921–933. https://doi.org/10.1080/07350015.2019.1610661

Factor Models

  • Bai, Jushan, and Serena Ng. 2002. "Determining the Number of Factors in Approximate Factor Models." Econometrica 70 (1): 191–221. https://doi.org/10.1111/1468-0262.00273
  • Forni, Mario, Marc Hallin, Marco Lippi, and Lucrezia Reichlin. 2000. "The Generalized Dynamic-Factor Model: Identification and Estimation." Review of Economics and Statistics 82 (4): 540–554. https://doi.org/10.1162/003465300559037
  • Stock, James H., and Mark W. Watson. 2002. "Forecasting Using Principal Components from a Large Number of Predictors." Journal of the American Statistical Association 97 (460): 1167–1179. https://doi.org/10.1198/016214502388618960

Panel VAR

Robust Inference

  • Andrews, Donald W. K. 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation." Econometrica 59 (3): 817–858. https://doi.org/10.2307/2938229
  • Hansen, Lars Peter. 1982. "Large Sample Properties of Generalized Method of Moments Estimators." Econometrica 50 (4): 1029–1054. https://doi.org/10.2307/1912775
  • Newey, Whitney K., and Kenneth D. West. 1987. "A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix." Econometrica 55 (3): 703–708. https://doi.org/10.2307/1913610
  • Newey, Whitney K., and Kenneth D. West. 1994. "Automatic Lag Selection in Covariance Matrix Estimation." Review of Economic Studies 61 (4): 631–653. https://doi.org/10.2307/2297912

Non-Gaussian Identification

  • Hyvarinen, Aapo. 1999. "Fast and Robust Fixed-Point Algorithms for Independent Component Analysis." IEEE Transactions on Neural Networks 10 (3): 626–634. https://doi.org/10.1109/72.761722
  • Lanne, Markku, and Helmut Lutkepohl. 2010. "Structural Vector Autoregressions with Nonnormal Residuals." Journal of Business & Economic Statistics 28 (1): 159–168. https://doi.org/10.1198/jbes.2009.06003
  • Lanne, Markku, Mika Meitz, and Pentti Saikkonen. 2017. "Identification and Estimation of Non-Gaussian Structural Vector Autoregressions." Journal of Econometrics 196 (2): 288–304. https://doi.org/10.1016/j.jeconom.2016.06.002

Hypothesis Tests

  • Dickey, David A., and Wayne A. Fuller. 1979. "Distribution of the Estimators for Autoregressive Time Series with a Unit Root." Journal of the American Statistical Association 74 (366): 427–431. https://doi.org/10.1080/01621459.1979.10482531
  • Kwiatkowski, Denis, Peter C. B. Phillips, Peter Schmidt, and Yongcheol Shin. 1992. "Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root." Journal of Econometrics 54 (1–3): 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y
  • Andrews, Donald W. K. 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point." Econometrica 61 (4): 821–856. https://doi.org/10.2307/2951764
  • Bai, Jushan, and Pierre Perron. 1998. "Estimating and Testing Linear Models with Multiple Structural Changes." Econometrica 66 (1): 47–78. https://doi.org/10.2307/2998540
  • Bai, Jushan, and Serena Ng. 2004. "A PANIC Attack on Unit Roots and Cointegration." Econometrica 72 (4): 1127–1177. https://doi.org/10.1111/j.1468-0262.2004.00528.x
  • Pesaran, M. Hashem. 2007. "A Simple Panel Unit Root Test in the Presence of Cross-Section Dependence." Journal of Applied Econometrics 22 (2): 265–312. https://doi.org/10.1002/jae.951
  • Moon, Hyungsik Roger, and Benoit Perron. 2004. "Testing for a Unit Root in Panels with Dynamic Factors." Journal of Econometrics 122 (1): 81–126. https://doi.org/10.1016/j.jeconom.2003.10.020
  • Breitung, Jorg, and Sandra Eickmeier. 2011. "Testing for Structural Breaks in Dynamic Factor Models." Journal of Econometrics 163 (1): 71–84. https://doi.org/10.1016/j.jeconom.2010.11.008
  • Granger, Clive W. J. 1969. "Investigating Causal Relations by Econometric Models and Cross-spectral Methods." Econometrica 37 (3): 424–438. https://doi.org/10.2307/1912791
  • Wilks, Samuel S. 1938. "The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses." Annals of Mathematical Statistics 9 (1): 60–62. https://doi.org/10.1214/aoms/1177732360

DSGE Models

  • Sims, Christopher A. 2002. "Solving Linear Rational Expectations Models." Computational Economics 20 (1–2): 1–20. https://doi.org/10.1023/A:1020517101123
  • Blanchard, Olivier Jean, and Charles M. Kahn. 1980. "The Solution of Linear Difference Models under Rational Expectations." Econometrica 48 (5): 1305–1311. https://doi.org/10.2307/1912186
  • Klein, Paul. 2000. "Using the Generalized Schur Form to Solve a Multivariate Linear Rational Expectations Model." Journal of Economic Dynamics and Control 24 (10): 1405–1423. https://doi.org/10.1016/S0165-1889(99)00045-7
  • Schmitt-Grohe, Stephanie, and Martin Uribe. 2004. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function." Journal of Economic Dynamics and Control 28 (4): 755–775. https://doi.org/10.1016/S0165-1889(03)00043-5
  • Andreasen, Martin M., Jesus Fernandez-Villaverde, and Juan F. Rubio-Ramirez. 2018. "The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications." Review of Economic Studies 85 (1): 1–49. https://doi.org/10.1093/restud/rdx037
  • Guerrieri, Luca, and Matteo Iacoviello. 2015. "OccBin: A Toolkit for Solving Dynamic Models with Occasionally Binding Constraints Easily." Journal of Monetary Economics 70: 22–38. https://doi.org/10.1016/j.jmoneco.2014.08.005
  • Herbst, Edward, and Frank Schorfheide. 2015. Bayesian Estimation of DSGE Models. Princeton, NJ: Princeton University Press. ISBN 978-0-691-16108-2.

Difference-in-Differences

FAVAR

  • Bernanke, Ben S., Jean Boivin, and Piotr Eliasz. 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach." Quarterly Journal of Economics 120 (1): 387–422. https://doi.org/10.1162/0033553053327452

Nowcasting

  • Banbura, Marta, and Michele Modugno. 2014. "Maximum Likelihood Estimation of Factor Models on Datasets with Arbitrary Pattern of Missing Data." Journal of Applied Econometrics 29 (1): 133–160. https://doi.org/10.1002/jae.2306
  • Cimadomo, Jacopo, Domenico Giannone, Michele Lenza, Francesca Monti, and Andrej Sokol. 2022. "Nowcasting with Large Bayesian Vector Autoregressions." ECB Working Paper No. 2696.
  • Banbura, Marta, Irina Belousova, Katalin Bodnar, and Mate Barnabas Toth. 2023. "Nowcasting Employment in the Euro Area." ECB Working Paper No. 2815.

License

This package is released under the GNU General Public License v3.0.

Contributing

Contributions are welcome! Please see the GitHub repository for contribution guidelines.

Contents