![]() Due to this correction, the results produced by running the files given below will differ slightly from those in the published paper. I thank Mel Stephens for noticing a small error in the original code that has been corrected. The code illustrates the basic procedure and may easily be modified for other data sets and to provide inference that is robust to autocorrelation or clustering. The data are taken from Acemoglu, Johnson, and Robinson (2001) “The Colonial Origins of Comparative Development: An Empirical Investigation”. Along with the code, each file contains examples illustrating how the code may be implemented the data for the examples may also be downloaded below.Ĭode for Weak Instrument Robust Inferenceīelow are links for the Stata code and data used in the empirical example in “A Simple Approach to Heteroskedasticity and Autocorrelation Robust Inference with Weak Instruments” (with Victor Chernozhukov). The MATLAB code also includes code for performing the weak identification robust inference procedure proposed in “Instrumental Variable Quantile Regression: A Robust Inference Approach” (with Victor Chernozhukov). ado file that may be used to obtain LASSO and Post-LASSO estimates in Stata.īelow are links to MATLAB and Ox code for performing IVQR estimation and inference as developed in “Instrumental Quantile Regression Inference for Structural and Treatment Effect Models” (with Victor Chernozhukov) and “Instrumental Variable Quantile Regression” (with Victor Chernozhukov). For example, Medeiros & Mendes (2016) prove model selection consistency of the adaptive lasso when applied to time-series data with non-Gaussian, heteroskedastic errors.Below are links Stata code and Matlab code for running the empirical examples from “ High-Dimensional Methods and Inference on Structural and Treatment Effects”. Lassopack can also applied to time-series or panel data. ![]() ![]() In doing so, they introduce a bias, but also reduce the variance of the prediction, which can result in improved prediction performance.įorecasting with time-series or panel data Regularization techniques exploit the variance-bias-tradeoff: they reduce the complexity of the model (through shrinkage or by dropping variables). Regularized regression methods tend to outperform OLS in terms of out-of-sample prediction. ), but poor out-of-sample prediction performance. If there are many predictors, OLS is likely to suffer from overfitting: good in-sample fit (large The adaptive lasso is known to exhibit good properties as a model selector as shown by Zou (2006). Lasso, elastic net and square-root lasso set some coefficient estimates to exactly zero, and thus allow for simultaneous estimation and model selection. Furthermore, sequential hypothesis testing induces a pre-test bias. However, this is problematic if the number of regressors is large due to many false positives. A standard approach is to use hypothesis testing to identify the correct model (e.g. Identifying the true model is a fundamental problem in applied econometrics. dummy variables, interaction terms and polynomials) to approximate the true functional form. We can then use a large set of transformations (e.g. There are only few observed variables, but the functional form through which these regressors enter the model is unknown.For example, in cross-country regressions the number of observations is naturally limited by the number of countries, whereas the number of potentially relevant explanatory variables is often large. There are many variables available for each unit of observation.High-dimensionality can arise when (see Belloni et al., 2014): The regularized regression methods implemented in lassopack can deal with situations where the number of regressors is large or may even exceed the number of observations under the assumption of sparsity. , may be large and possibly greater than the number of observations, Lassopack is a suite of programs for regularized regression methods suitable for the high-dimensional setting where the number of predictors, Forecasting with time-series or panel data.
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