Yongchen Zhao| PhD, Department of Economics, SUNY Albany

Personal information

Address Department of Economics,
Business Administration Building, Room 109
1400 Washington Avenue, Albany, New York, 12222
E-mail hzhao@albany.edu
Phone +1 (518) 512-7392
Citizenship China (on F-1 Student visa)

   Department Information

The Department of Economics of the University at Albany, State University of New York has a strong faculty engaged in a wide range of applied and theoretical research. In the 2013 rankings of Research Papers in Economics (RePEC), the department is placed in the top 10% of all U.S. economics institutions.

Download my C.V. in PDF

Recent Updates

Aug 3, 2014: Added "Economics Bulletin" and "Empirical Economics" to Referee section.

Jun 4, 2014: The status of the paper "Quantifying Survey Expectations: A Critical Review and Generalization of the Carlson-Parkin Method" is changed from "Revise and Resubmit" to "Forthcoming".

Jun 4, 2014: Added the "University at Albany Distinguished Doctoral Dissertation Award" to the grants and awards section.

Apr 14, 2014: The paper "Testing the value of probability forecasts for calibrated combining" is available online through ScienceDirect. Click HERE to access the paper.

Apr 4, 2014: The paper "Measuring Macroeconomic News in Real Time" (a work in progress with Kajal Lahiri) will be presented at the inaugural conference of the Society for Economic Measurement, University of Chicago, August 18-20, 2014.


  • 2008 - 2014

    University at Albany, State University of New York, USA

    Ph.D. Economics
    Dissertation: Essays on Forecasting with Survey Data and Many Predictors: Forecast Combination, Evaluation, and Applications to the Financial Market
    Adviser: Prof. Kajal Lahiri
  • 2012

    University at Albany, State University of New York, USA.

    M.A. Economics
  • 2004 - 2008

    University of International Relations, Beijing, China.

    B.A. Economics
Click paper tile to see abstract and download link.
The main focus of this paper is to explore the potential for improving econometric specification in modeling hedge fund returns. Specifically, we examine the effects of (1) correcting for selectivity bias due to sample attrition; (2) allowing for nonlinearity; and (3) controlling for fund-specific unobserved heterogeneity. Diagnostic tests confirm the importance of these complications. Using data covering 1996-2008, we show that when selectivity, nonlinearity, and fund heterogeneity are taken into account, we obtain more robust estimates of the effect of key variables on hedge fund returns and managerial efficiency, and the explanatory power of the model is significantly improved.
While the yield spread has long been recognized as a good predictor of recessions, it seems to have been largely overlooked by professional forecasters. We examine this puzzle, established by Rudebusch and Williams (2009), in a data-rich environment including not just the yield spread but many other predictors as well. We confirm the puzzle in this context by examining the contributions of both the SPF forecasts and the yield spread in predicting recessions, and by examining the information content of SPF forecasts directly. Furthermore, we take the first step towards a possible resolution of this puzzle by recognizing the heterogeneity across professional forecasters.
The main purpose of this chapter is to estimate a model for hedge fund returns that will endogenously generate failure probabilities using panel data where sample attrition due to fund failures is a dominant feature. We use the Lipper (TASS) hedge fund database, which includes all live and defunct hedge funds over the period January 1994 through March 2009, to estimate failure probabilities for hedge funds. Our results show that hedge fund failure prediction can be substantially improved by accounting for selectivity bias caused by censoring in the sample. After controlling for failure risk, we find that capital flow, lockup period, redemption notice period, and fund age are significant factors in explaining hedge fund returns. We also show that for an average hedge fund, failure risk increases substantially with age. Surprisingly, a 5-year-old fund on average has only a 65% survival rate.
We propose a generalized ordered response model that nests the popular Carlson-Parkin (CP) method to quantify household inflation expectations while explicitly control for cross-sectional heterogeneity in the threshold parameters and the variance. By matching qualitative and quantitative data from 1979 to 2012 from the University of Michigan's Survey of Consumers, we find evidence against the threshold constancy, symmetry, and homogeneity assumptions of the CP method. We show that the quantified expectations produced by the generalized model outperform those produced by the CP method, most notably during the 2008 recession period. We also show that when an rolling-window identification scheme is employed instead of the unbiasedness assumption over the entire sample, quantified expectations are significantly better in terms of predictive accuracy when compared with the quantitative expectations reported in the survey.
We study the role of consumer confidence surveys in forecasting personal consumption expenditure. We reexamine existing models of consumption and consumer confidence using both quarterly and monthly data in real time. Additionally, we produce forecasts of consumption expenditures with and without consumer confidence measures using a dynamic factor model and a real-time, jagged-edge data set. We establish in a robust way that consumer confidence significantly improves the accuracy of consumption forecasts. Furthermore, traditional macroeconomic theories seem to be unable to fully account for these results.
We combine the probability forecasts of real GDP declines from the U.S. Survey of Professional Forecasters, after trimming the forecasts that do not have "value" in the sense of Merton (1981). For this purpose, we propose a new test to evaluate probability forecasts that does not require converting the probabilities to binary forecasts before testing. The test accommodates serial correlation and skewness in the forecasts, and is implemented using a circular block bootstrap procedure. We find that the number of forecasters making valuable forecasts decreases sharply as horizon increases. The beta-transformed linear pool, based only on the valuable individual forecasts, is shown to outperform the simple average for all horizons on a number of performance measures including calibration and sharpness.
This paper focuses on the newly proposed on-line forecast combination algorithms in Sancetta (2010), Yang (2004), and Wei and Yang (2012). We first establish the asymptotic relationship between these new algorithms and the Bates and Granger (1969) method. Then, we show that when implemented on unbalanced panels, different combination algorithms implicitly impute missing data differently, making results not comparable across methods. Using forecasts of a number of macroeconomic variables from the U.S. Survey of Professional Forecasters, we evaluate the performance of the new algorithms and contrast their inner mechanisms with that of Bates and Granger's method. Missing data in the SPF panels are specifically controlled for by explicit imputation. We find that even though equally weighted average is hard to beat, the new algorithms deliver superior performance especially during periods of volatility clustering and structural breaks.
Instead of using official statistics, this paper attempts to quantify the international propagation of shocks using multi-horizon fixed target real GDP growth forecasts. It is established that under the condition of long-run forecast efficiency, results obtained from the forecasts are identical to those obtained from the actual values. This enables the policy makers to understand the economic situations in real time, overcoming the difficulties caused by data revisions and publication lags. A Factor Structural Vector Autoregressive (FSVAR) model is estimated using forecasts from 1995 to 2013 for 16 countries including major G7 countries and Asian developing economies. Results suggest strong convergence within the group of industrialized countries and developing economies during non-crisis periods. However, the transmission of shocks during crisis periods seem to depend on the nature and origin of the crisis.
We study the information content of the five components of the University of Michigan's Index of Consumer Sentiment and identify the main determinants of these measures, using semiparametric ordered choice models and household data from the Surveys of Consumers from January 1978 to September 2012. Our findings suggest that consumers' own perceptions and expectations, as measured by other survey questions in the Surveys of Consumers, are the most important determinants of the sentiment index. After this set of factors is controlled for, consumers' demographic characteristics, aggregate macroeconomic variables, and professional forecasts account for little in addition. We also find that the sentiment components about the overall economic conditions are less sensitive to consumers' own views and characteristics than the components about consumers' household financial situations. These findings could motivate the use of consumer sentiment measures in a variety of applications, including forecasting consumption expenditures.


Courses taught as instructor of record:

Click course tile to see more details.

Upper division undergraduate course. Normal class size: 50 students.

Course objectives
Economic Statistics applies statistical theory to economic and business data in an attempt to quantitatively measure relationships. In this course you will learn how to describe statistical data and read popular material involving statistical results. Students will learn to perform analysis of statistical relationships using various procedures and to interpret their results. The special nature of economic statistics will be emphasized.

There are five main topics to be covered in this course: (1) descriptive statistics; (2) probability and probability distributions; (3) estimation and hypothesis testing; (4) regression and correlation; (5) introductory time series analysis.

Upper division undergraduate course. Normal class size: 30 students.

Course objectives
This course is designed to serve one or more of the following purposes: giving students skills needed in writing thesis; introducing basic techniques in data analysis and evaluating results from such analysis; preparing for advanced forecasting and/or econometrics course; providing working knowledge and adequate experience with statistical software.

The objective of this course is to give students hands on experience in applied economic forecasting by introducing them to the process, the tools, and the practice of economic forecasting. Elementary statistical techniques and basic econometric models are introduced, as well as the use of statistical software. Students are expected to learn the basic process of analyzing economic data. This is an applied course so the emphasis is on practical skills rather than theoretical knowledge.

Upper division undergraduate course. Normal class size: 50 students.

Course objectives
Macroeconomics focuses on the nature, determinants, dynamics, and evolution of measures of aggregate economic activities such as output, consumption, interest rates, employment, and inflation. In this course, we will study how to measure macroeconomic activities, discover relationships between these measures, use our discoveries to analyse macroeconomic phenomenons and policies, and make economic policy suggestions.

The objective of this course is to (1) Identify and describe major factors/variables/indicators measuring macroeconomic activities. (2) State the assumptions and structure of popular elementary macroeconomic theories/models, and derive their main results. (3) Predict the responses of the remaining factors/variables/indicators to a shock in one factor/variable/indicator. (4) Assess the impact of economic policies on the economy in simplified settings. (5) Develop effective macroeconomic policies for a simulated "pet" economy.

Upper division undergraduate course. Normal class size: 30 students.

Course objectives
This course focuses on the structure and working of financial markets, financial institutions, (and in particular) central banks, the role of money in an economy, as well as the theories about monetary policies. In this course, we will study how to categorize elements of the financial institution, understand interest rates and their behavior, and analyze the working of central banks.

The objective of this course is to (1) Identify the major elements of the financial system and describe how they work, individually, and collectively as a whole. (2) State the basic facts about interest rates and their behavior. (3) Derive elementary monetary theories covered in this course. (4) Assess monetary policies using relevant theories. (5) Develop effective monetary policies for a simulated "pet" economy.

General education course. Normal class size: 150 students.

Course objectives
This course falls under the Global and Cross-Cultural category of General Education Program. The learning objectives of courses under this category are as follows: To demonstrate: An understanding of the impact (e.g., economic, political, historical, cultural) of nations, regions and cultures upon other nations, regions and cultures; An understanding of the reciprocal interactions between individuals and global systems; An ability to see cultural groups from their own points of view; An ability to use the analytical tools of economics to engage in comparative analyses of cultures, nations and regions

The goal of this course on developing economies is to understand: The nature of underdevelopment and poverty in developing economies; Basic economic theories about growth; Global interactions of countries through international trade and development; Global issues of population pressures, environment, technology transfer and urban migration

This course is similar to the AECO480 Economic Forecasting course, but presented at a relatively less technical and more applied level for students without prior econometrics training. Please see the AECO480 Economic Forecasting course for more details and sample course materials.

Courses TA'ed or tutored:

AECO110 Principles of Economics I: Microeconomics

AECO111 Principles of Economics II: Macroeconomics

AECO300 Intermediate Microeconomics

AECO301 Intermediate Macroeconomics

UFSP100 Freshmen Seminar: Forecasting in Business and Economics

Selected comments from students:

I enjoyed the class, I would have liked to have done the state budget case study though. I understand that other students needed more instruction though.

Student from Forecasting course

I have taken this course 3 times now and I have never really understood it. However, working with professor Zhao I got to saw it from a new FRESH perspective. I think having someone younger teaching this class really helps to have a more open flow of conversation about the topics at hand.

Student from Macroeconomics course

Tried to engage students and wasn't monotone.

Student from Money and Banking course

Explanation of the concepts of the material covered was sufficient for me to understand with little additional review on my own. Copies of the well prepared presentations were available online so that anything covered in class could be reexamined after class.

Student from Macroeconomics course

The instructor explained assignments well, he made students learn the materials well. He answered my emails and answered questions. He respected students.

Student from Developing Economies course

Conference Presentations

The Inaugural Conference of the Society for Economic Measurement, University of Chicago, August 18-20, 2014. (Scheduled)

Third Annual Joint Mini Conference of the Upstate Chapters of the American Statistical Association, Geneseo, NY, April 12th, 2014.

Society for Nonlinear Dynamics and Econometrics 22nd Annual Symposium, Baruch College CUNY, New York City, New York, USA, April 17, 2014.

Eastern Economic Association 39th Annual Conference, New York City, New York, USA, May 10, 2013.

New York Camp Econometrics VIII, The Center for Policy Research, Syracuse University, Bolton Landing, New York, USA, April 5, 2013 (poster).

New York Camp Econometrics VII, The Center for Policy Research, Syracuse University, Cooperstown, New York, USA, April 15, 2012.

64th Annual Conference, New York State Economics Association, Rochester Institute of Technology, Rochester, New York, USA, September 24, 2011.

New York Camp Econometrics VI, The Center for Policy Research, Syracuse University, Lake Placid, New York, USA, April 9, 2011.

30th CIRET (Centre for International Research on Economic Tendency Surveys) Conference, The Conference Board, New York City, New York, USA, October 16, 2010.

Grants and Awards

University at Albany Distinguished Doctoral Dissertation Award, University at Albany, SUNY, May, 2014.

SAS-IIF Grant to Support Research on Principles of Forecasting ($5000), International Institute of Forecasters, December, 2013.

Pong S. Lee Award for Excellence in Teaching, Department of Economics, University at Albany, SUNY, August, 2012.

Extended Year Funding for Dissertation Research, College of Arts and Sciences, University at Albany, SUNY, May, 2012.

Pong S. Lee Endowment Award for Outstanding Research, Department of Economics, University at Albany, SUNY, December, 2011.

Outstanding Performance on Preliminary Comprehensive Examinations, Department of Economics, University at Albany, SUNY, September, 2009.

Graduate Assistantship, Department of Economics, University at Albany, SUNY, September, 2008 to May 2012.


Economics Bulletin; Empirical Economics; International Journal of Forecasting

Research Tools and Languages

Proficiency in Stata; experience in Matlab, SAS (certified base programmer), Eviews, and Limdep/Nlogit.

Spoken and written proficiency in English (acquired) and Chinese Mandarin (native).

Professional Development

Courses taken: ACAS601 Seminar in College Teaching (Fall 2011); ACAS602 Preparing for Professoriate (Spring 2011).

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