Econometrics: Compilation of Applied Avanced Topics in the Blog
An effort tying to put articles in order and knowledge more accessible.
Is assumed that you know inference statistics (at least: what p-value mean in practice).
[WORK IN PROGRESS]
In this article I present the main structure ordered list of select topics and materials that I have been write about in the applied econometric fields. The order and selection by relevance is base in expert consult, best valuate rigorous academic reference and own experience.
This is almost my own curriculum program in econometrics, as an autodidact learner and future econometric contributor and reference : ) .
Learn to design models (main focus, in my opinion):
- What is the identification strategy?
- What shocks are structural?
- What is the economic mechanism?
To think about how to do that in practice, I will recommend to read the article with some research orientations (Research in Practices: Path and Key Recommendations).
Practice every day:
- Read econometrics papers every day (until it become easy)
- Replicate them from scratch (select)
- Improve the methodology (change something)
- Publish original research (application to consolidate knowledge)
Replicate, replicate, replicate and then implement.
Advance econometrics applications will require intensely programming and computation (you can see my list of published topics in this way).
If the following material is hard tounderstand for you, you will access to the core mathematical statistics notions.
What about to be an econometrician
Basics in Linear Time Series
- Analysis of the Deterministic Trend Model: Rates of Convergence, OLS Estimation and Serial Dependence
- Empirical Measures of Dependency
- Covariance Stationarity, Stationarity and Ergodicity
- Martingales and Martingale Difference Sequences
- Endogeneity
Core Econometrics Notions in my perspective (can start here or wen you go deep have to come here)
- Linearity and non linearity
- MSE (bias and variance threshold)
Foundations core Notions and Models
This are used for macroeconomic forecasting, financial return modeling and long-run equilibrium relationships.
- Maximum Likelihood Estimation
- Moving average and Autoregressive Process: AR, ARMA and ARIMA models
- Unit root tests (ADF, PP, KPSS)
- Cointegration (Engle–Granger, Johansen)
- Error Correction Models (ECM / VECM)
Macro-Financial Time Series Econometrics Methods
This will implemented for monetary policy shocks, macro-financial spillovers, impulse response analysis and forecasting.
GARCH Models and Family
- ARCH
- GARCH
- EWMA and RiskMetrics
- GARCH in Quant Finance and Risk Management
VAR Framework and Factor Models
- VAR
- SVAR (identification and algorithms for inference)
- Bayesian VAR
- PCA and Dynamic Factors
- FAVAR (Factor-Augmented VAR)
- Structural VAR extensions (MS-VAR, TVP-VAR, Proxy-SVAR)
- TVP-VAR (Time-varying parameter VAR)
Dynamic Effects Shocks
- Local projections
- Structural break detection
- State space models
Bayesian Methods for Time Series
- Bayesian Statistics: Point Estimation, Testing Theory
- BVAR Model
- Bayesian Model Selection: Determining the Order of an AR process
- Markov-Chain Monte Carlo Methods to Generate Draws from Posteriors
Non Linear Models and Spectral Analysis
For macroeconomic time series and linear cyclical model
- Fourier transformations
- Kernel smoothing (and LOESS)
- Quartile Regression
- Filters (Kalman Filter)
- Computing likelihood functions for LRE models
- Markov-Switching Models
Extremum Estimation
- Generalized method of moments and maximum likelihood
- Consistency
- Asymptotic Normality
Network Econometrics and Spatial
DSGE and Hybrid Models
- Linear (and Nonlinear) Rational Expectations (LRE) Models
- LRE models as approximations to dynamic stochastic equilibrium (DSGE)
- Moment-based Estimation of linear and nonlinear rational expectations models
- Likelihood-based Estimation of LRE models
- DSGE-VARs
- DSGE-DFM
Maxima Verosimilitud
Tools that will be needed at any point
- Granger causality
- ACI
- MSE (variance and bias threshold)
- Monte Carlo Simulation
- MCMC
[IF YOU HAVE SOME COMMENTS, OBSERVATIONS AND SUGGESTIONS LET ME KNOW]
A the beginning it will be hard and means to read more than start to be runing. But at certain point you will start understanding with a simple view and start going to review the used method in the computation engine level and then start to be thinking about new methods and develop new computational tools (packages).
Reference in priority order:
Hamilton, James D. Time Series Analysis. Princeton University Press, 1994.
MIT - Time Series Analysis ***
Lütkepohl — Multiple Time Series Analysis
Kilian & Lütkepohl — Structural VAR Models
Jordà (2005) Estimation and Inference of Impulse Responses by Local Projections
Aplications in Quant Finance and Risk Management:
Princenton - Mean--Variance Analysis &Variance Analysis & CAPM
J.P.Morgan and Reuters. RiskMetrics - Technical Document
Aditional Rreadings:
James H Stock and Mark W Watson. Vector autoregressions. Journal of Eco-nomic Perspectives, 15(4):101–115, 2001.
Roberts, M.R. and T.M. Whited (2013), "Endogeneity in Empirical Corporate Finance", in G.M.
Constantinides, M. Harris and R. Stulz, eds, Handbook of the Economics of Finance, Vol. 2A, Elsevier, chapter 7, pp. 493-572
Brockwell, Peter, and Richard Davis. Time Series: Theory and Methods. Springer-Verlag, 1991.
Canova, Fabio. Methods for Applied Macroeconomic Research. Princeton University Press, 2007.
DeJong, David, and Chetan Dave. Structural Macroeconometrics. Princeton University Press, 2011.
Hall, Peter, and C. C. Heyde. Martingale Limit Theory and Its Application. Probability and Mathematical Statistics. Academic Press, 1980.
Griliches, Zvi, and Michael Intriligator, eds. Handbook of Econometrics. Vol. 3. North Holland, 1986.
Lütkepohl, Helmut. Introduction to Multiple Time Series Analysis. Springer-Verlag, 1993.
General Econometrics:
Amemiya, T. (1985): “Advanced Econometrics,” Harvard University Press.
Davidson, R. and J. MacKinnon (1993): “Estimation and Inference in Econometrics,” Oxford University Press.
White, H. (1984): “Asymptotic Theory for Econometricians,” Academic Press.
Hayashi, Fumio (2000): “Econometrics,” Princeton University Press.
Time Series Analysis:
Brockwell, P.J. and R.A. Davis (1991): “Time Series: Theory and Models,” Springer-Verlag.
Campbell, J.Y., A.W. Lo, and A.C. MacKinlay (1997): “The Econometrics of Financial Markets,” Princeton University Press.
Granger, C.W.J. and P. Newbold (1987): “Forecasting Economic Time Series,” Academic Press.
Harvey, A.C. (1990): “The Econometric Analysis of Time Series,” MIT Press.
Modern Macroeconometrics:
Canova, Fabio (2007): Methods for Applied Macroeconomic Research, Princeton University Press.
DeJong, David and Chetan Dave (2007): Structural Macroeconometrics, Princeton University Press.
Herbst, Edward and Frank Schorfheide (2015): Bayesian Estimation of DSGE Models, Princeton University Press.
Bayesian Statistics and Econometrics:
Gelman, Andrew, John B. Carlin, Hal S. Stern, and Donald B. Rubin (1995): “Bayesian Data Analysis,” Chapman & Hall, New York.
Geweke, John (2005): “Contemporary Bayesian Econometrics and Statistics,” Wiley, New York.
Koop, Gary (2003): “Bayesian Econometrics,” John Wiley & Sons.
Lancaster, Tony (2004): “An Introduction to Modern Bayesian Econometrics,” Blackwell Publishing.
Poirier, Dale (1995): “Intermediate Statistics and Econometrics - A Comparative Approach,” MIT-Press.
Robert, Christian P. (1994): “The Bayesian Choice,” Springer-Verlag, New York
