Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

This paper incorporates a time-varying intensity of disasters in the Rietz-Barro hypothesis that risk premia result from the possibility of rare, large disasters. During a disaster, an asset’s fundamental value falls by a time-varying amount. This in turn generates time-varying risk premia and thus volatile asset prices and return predictability. Using the recent technique of linearity-generating processes (Gabaix 2007), the model is tractable, and all prices are exactly solved in closed form. In the “variable rare disasters” framework, the following empirical regularities can be understood qualitatively: (i) equity premium puzzle (ii) risk-free rate-puzzle (iii) excess volatility puzzle (iv) predictability of aggregate stock market returns with price-dividend ratios (v) value premium (vi) often greater explanatory power of characteristics than covariances for asset returns (vii) upward sloping nominal yield curve (viiii) a steep yield curve predicts high bond excess returns and a fall in long term rates (ix) corporate bond spread puzzle (x) high price of deep out-of-the-money puts. I also provide a calibration in which those puzzles can be understood quantitatively as well. The fear of disaster can be interpreted literally, or can be viewed as a tractable way to model time-varying risk-aversion or investor sentiment. (JEL: E43, E44, G12)

/pdf/variable-rare-disasters-an-exactly-solved-framework-for-ten-5952lqpjej.pdf

Variable Rare Disasters: An Exactly Solved Framework for Ten Puzzles in Macro-Finance

This paper employs an approximation that makes a nonlinear term structure model extremely tractable for analysis of an economy operating near the zero lower bound for interest rates. We show that such a model oers an excellent description of the data and can be used to summarize the macroeconomic eects of unconventional monetary policy at the zero lower bound. Our estimates imply that the eorts by the Federal Reserve to stimulate the economy since 2009 succeeded in making the unemployment rate in May 2013 0.23% lower than it otherwise would have been.

/pdf/measuring-the-macroeconomic-impact-of-monetary-policy-at-the-1w3qd5quk8.pdf

Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound

This paper employs an approximation that makes a nonlinear term structure model extremely tractable for analysis of an economy operating near the zero lower bound for interest rates. We show that such a model offers an excellent description of the data compared to the benchmark model and can be used to summarize the macroeconomic effects of unconventional monetary policy. Our estimates imply that the efforts by the Federal Reserve to stimulate the economy since July 2009 succeeded in making the unemployment rate in December 2013 1% lower, which is 0.13% more compared to the historical behavior of the Fed.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2469299_code753937.pdf?abstractid=2321323&mirid=4

This paper quanties how variation in real economic activity and ination in the U.S. inuenced the market prices of level, slope, and curvature risks in U.S. Treasury markets. To accomplish this we develop a novel arbitrage-free DTSM in which macroeconomic risks{ in particular, real output and ination risks{ impact bond investment decisions separately from information about the shape of the yield curve. Estimates of our preferred macro-DTSM over the twenty-three year period from 1985 through 2007 reveal that unspanned macro risks explained a substantial proportion of the variation in forward terms premiums. Unspanned macro risks accounted for nearly 90% of the conditional variation in short-dated forward term premiums, with unspanned real economic growth being the key driving factor. Over horizons beyond three years, these eects were entirely attributable to unspanned ination. Using our model, we also reassess some of Chairman Bernanke’s remarks on the interplay between term premiums, the shape of the yield curve, and macroeconomic activity.

/pdf/risk-premiums-in-dynamic-term-structure-models-with-3vjigiidzh.pdf

Risk Premiums in Dynamic Term Structure Models with Unspanned Macro Risks

In any canonical Gaussian dynamic term structure model (GDTSM), the conditional forecasts of the pricing factors are invariant to the imposition of no-arbitrage restrictions. This invariance is maintained even in the presence of a variety of restrictions on the factor structure of bond yields. To establish these results, we develop a novel canonical GDTSM in which the pricing factors are observable portfolios of yields. For our normalization, standard maximum likelihood algorithms converge to the global optimum almost instantaneously. We present empirical estimates and out-of-sample forecasts for several GDTSMs using data on U.S. Treasury bond yields. (JEL E43, G12, C13) Dynamic models of the term structure often posit a linear factor structure for a collection of yields, with these yields related to underlying factors P through a no-arbitrage relationship. Does the imposition of no-arbitrage in a Gaussian dynamic term structure model (GDTSM) improve the out-of-sample forecasts of yields relative to those from the unconstrained factor model, or sharpen model-implied estimates of expected excess returns? In practice, the answers to these questions are obscured by the imposition of over-identifying restrictions on the risk-neutral (Q) or historical (P) distributions of the risk factors, or on their market prices of risk, in addition to the cross-maturity restrictions implied by no-arbitrage. 1

/pdf/a-new-perspective-on-gaussian-dynamic-term-structure-models-1or36iy0zz.pdf

A New Perspective on Gaussian Dynamic Term Structure Models

This paper quantifies how variation in real economic activity and inflation in the U.S. influenced the market prices of level, slope, and curvature risks in U.S. Treasury markets. We develop a novel arbitrage-free dynamic term structure model in which bond investment decisions are influenced by real output and inflation risks that are unspanned by (imperfectly correlated with) information about the shape of the Treasury yield curve. Our model reveals that, over the period 1985-2007, these unspanned macro risks accounted for a large portion of the variation in forward terms premiums, and there was pronounced cyclical variation in the market prices of level and slope risks. We compare fitted term premiums for the post-2007 crisis period to those from a model with spanned macro risks, and use our findings to reassess some of Chairman Bernanke's remarks on the interplay between term premiums, the shape of the yield curve, and the macroeconomy.

Although the threat of rare economic disasters can have large effect on asset prices, difficulty in inference regarding both their likelihood and severity provides the potential for disagreements among investors. Such disagreements lead investors to insure each other against the types of disasters each one fears the most. Due to the highly nonlinear relationship between consumption losses in a disaster and the risk premium, a small amount of risk sharing can significantly attenuate the effect that disaster risk has on the equity premium. We characterize the sensitivity of risk premium to wealth distribution analytically. Our model shows that time variation in the wealth distribution and the amount of disagreement across agents can both lead to significant variation in disaster risk premium. It also highlights the conditions under which disaster risk premium will be large, namely when disagreement across agents is small or when the wealth distribution is highly concentrated in agents fearful of disasters. Finally, the model predicts an inverse U-shaped relationship between the equity premium and the size of the disaster insurance market.

Rare Disasters and Risk Sharing with Heterogeneous Beliefs

http://web.stanford.edu/~kenneths/JLS.pdf

Scott Joslin

Papers

Risk Premiums in Dynamic Term Structure Models with Unspanned Macro Risks

A New Perspective on Gaussian Dynamic Term Structure Models

Risk Premiums in Dynamic Term Structure Models with Unspanned Macro Risks

Rare Disasters and Risk Sharing with Heterogeneous Beliefs

Why Gaussian macro-finance term structure models are (nearly) unconstrained factor-VARs