This course will comprise two parts: reviewing general methods for statistical inference and introducing econometric models for empirical applications. In the first part, we will review basic concepts of asymptotic analysis and introduce the (generalized)~method of moments. The concepts and methods introduced in this part are flexibly applied to a wide range of econometric models. In the second part, we will discuss different classes of time-series econometric models, including univariate andmultivariate conditional mean, variance and distribution models, state-space models and dynamic factor models. These models substantially extend linear regressions in various directions, and have wide empirical applications in financial and macroeconomic time series analysis. We also plan to discuss quantile regressions and panel data models that are also important for econometric analysis.

I. Statistical Inference
Asymptotic Concepts
Time Series Data
Method of Moments
Generalized Method of Moments
HAC Estimation for Asymptotic Covariance Matrix
Bootstrap Method
Model Evaluation

II. Econometric Models
Conditional Mean, Variance and Distribution Models
Quantile Regressions
Panel Data Models
State-Space Models
Dynamic Factor Models

III. Student Workshop
Proposal(midterm) & Paper(final)
Presentation(last two weeks)