In the previous chapter we covered the basics of reduced-form VARs on stationary data.

Most time series methods are only valid if the underlying time series is stationary. A time series is stationary if its mean, variance, and autocovariance do not rely on the particular time period. In this chapter, we derive the conditions under which a process is stationary, and show some implications of this stationarity.

2nd edition of textbook which uses worked examples and data from classic papers to teach econometrics. 2nd edition

If we take the notion of general equilibrium seriously, then everything in the economy is related to everything else. For this reason, it is often impossible to say which variables are exogenous. Vector autoregressions or “VARs” attempt to model the many interdependencies between economic variables. The VAR generalizes earlier univariate autoregressive (AR) models by allowing a large number of variables depend on lagged values of their own and of other variables. Earlier concepts of stability, lag selection, and impulse response functions are also extended, and Granger causality is introduced. We also replicate an influential paper by Christopher Sims, the inventor of the VAR.

Many financial and economic time series exhibit a regular cyclicality, periodicity, or “seasonality.” When econometricians say data is “seasonal”, they simply mean that there is some sort of periodicity, whether it is weekly, monthly or yearly. Seasonal models can be be deterministic or stochastic, stationary or integrated, additive or multiplicative.

Most time series methods are only valid if the underlying time series is stationary. A time series is stationary if its mean, variance, and autocovariance do not rely on the particular time period. In this chapter, we derive the conditions under which a process is stationary, and show some implications of this stationarity. To answer these questions we will learn about the so-called Box-Jenkins approach of comparing empirical autocorrelation functions and partial autocorrelation functions with their theoretical counterparts.

Many economic and financial time series do not have a constant mean. Rather they show growth or decay. The type of growth–whether deterministic or stochastic–has important implications for policy. In this chpater we examine the different ways to detrend the data. We spend particular attention on the effects of “differencing” (and over-differencing) the data, especially in the context of random walk models with and without drift. We also introduce the ARIMA class of models.

Gender wage gaps are frequently explained as resulting from direct discrimination, employers’ preferences over personality traits, and differing labor force attachment. We rely on a natural quasi-experiment using exogenous changes in state-level, same-sex adoption laws to distinguish between the competing explanations of the gender wage gap. Estimates from a differences-in-differences model show the wage gap between lesbians and heterosexual women shrank or inverted in those states which legalized adoption by same-sex couples. The wage gap did not change for men. This supports the parenthood hypothesis as a viable explanation for a portion of the gender wage gap.

In this chapter we show how to model the long-run relationship between variables in their levels, even if they are integrated. This is possible if two or more variables are “cointegrated.” Two variables are cointegrated is the difference between them is stationary. Or, to put it loosely, they move in parallel. In this chapter we explore the concept of cointegration, error correction mechanisms, and some of the more popular tests of contegration.

Endogeneity problems such as self-selection and program placement bias are key issues in estimating the impact of microcredit programs. Self-selection problem occurs when borrowers select themselves in the program because they might have entrepreneurial skills, and risk taking abilities. Hence they could do better even without the credit. And, program placement bias occurs when a branch of microfinance institution placed in a developed village in order to become financially sustainable. This study focuses on overcoming these two biases while estimating the role of microcredit in alleviating poverty in Bangladesh. This study empirically estimates Foster-Greer-Thorbecke (FGT) poverty measures which are: incidence of poverty, poverty gap and squared poverty gap. To achieve the objectives of the study, primary data were collected from 2,598 households during the period of June 2014 to September 2014. The survey covers 24 districts out of 64 districts in Bangladesh. In addition to current borrowers, four types of control groups, non-borrowers, drop-outs, refused and pipeline borrowers, were interviewed to control for potential self-selection bias. The results of the FGT poverty measures indicate that the incidence of poverty, poverty gap and squared poverty gap are the lowest among borrowers. Then, this study applies fixed-effect logistic regression for measuring the impact of microcredit on the incidence of poverty and fixed-effect Tobit regression for estimating the poverty gap and squared poverty gap. In the empirical estimations, this study controls for age and education of the head of households, female (spouse) education, and age and gender composition of the household members. The results suggest that microcredit borrowers are less likely to be poor after being involved in the program. The results also indicate that microcredit program reduces poverty gap and squared poverty gap by respectively 3.3 and 1.2 percent even after controlling for self-selection and program placement bias. Therefore, this study claims that microcredit is an effective tool for poverty reduction.

Our thesis is that the reason many of us today are inclined toward socialism (explicit cooperation) and against laissez-faire capitalism (implicit cooperation) is because the first type of behavior was much more genetically beneficial during previous generations of our species. There is, however, a seemingly strong argument against this hypothesis: evidence from human prehistory indicates that trade (implicit cooperation) previously was widespread. How, then, can we be hard-wired in favor of socialism and against capitalism if our ancestors were engaged in market behavior in past millennia? Although trade which is self-centered and beneficial (presumably mutually beneficial to all parties in the exchange) did indeed appear hundreds of thousands of years ago, benevolence was established in our hard-wiring very substantially earlier, literally hundreds of millions of years ago, and is therefore far more deeply integrated into the human psyche.

This study extends the volatility prediction literature with (1) new intraday realized volatility measures and (2) various implied volatility indexes for commodities, currencies, and equities. Predicting volatility is important for academics, investors, and regulators. Applications range from forecasting stock and option returns to constructing early warning systems. Using twenty-three Chicago Board Options Exchange VIX indexes, as opposed to the common S&P 100 and S&P 500 equity indexes, we find a bidirectional lead-lag relationship between implied volatility and realized volatility. The lead-lag relationships are more robust and stronger using suggested intraday volatility measures than using the interday volatility measures that are common in the literature.

This paper studies how reputation enforces socially cooperative behavior in road racing in the New Orleans metro area. We find that reputation mechanisms have a much stronger effect for frequent road racers than for members of the New Orleans Track Club. We find that club membership cuts cheating in half while a runner who has finished at least one-third of the 2013 running season does not cheat. Thus, self-governance eliminates corruption when there is a reputational mechanism in place. Since data on informal running clubs are unavailable, our analysis underestimates the effect of club membership on socially cooperative behavior in road racing.

We present an active-learning computer exercise where students pick stocks for a portfolio. Using their selection of stocks, two different portfolios are created: 1) a portfolio that never rebalances and 2) a portfolio that continuously rebalances. They then calculate the rates of return and betas for their individual stocks and for their portfolios. The students are then asked to draw conclusions about the benefits of diversification which are shown to apply regardless of the specific type of rebalancing in a diversified portfolio.

Social Economics: Current and Emerging Avenues, JOAN COSTA-FONT AND MARIO MACIS (EDITORS). Cambridge, MA: The MIT Press, 2017. Pp. vii, 332. $35.00

PurposeThe purpose of this paper is to determine the nature of the wage gap between genders and sexual orientation.Design/methodology/approachThe paper uses OLS on pooled repeated cross-sections.FindingsThe differences in wages between gay/straight men and women mirror what would be expected from labor force attachment more so than direct heterosexism.Research limitations/implicationsThe authors use a functional definition of sexual preference that reflects whether the respondent had sex with someone of the same gender in the same year. It does not ask whether the person identifies publicly as gay/lesbian/bisexual.Originality/valueThe authors verify and extend earlier findings on the sexual orientation and gendered wage gap.

In this article, we provide four financial technical analysis tools: moving averages, Bollinger bands, moving-average convergence divergence, and the relative strength index. The tftools command is used with four subcommands, each referring to a technical analysis tool: bollingerbands, macd, movingaverage, and rsi. We provide examples for each tool. tftools allows researchers to backtest their own investment strategies and will be of interest to investors, researchers, and students of finance.

The 2008 financial crisis refocused investors' attention to several safe-haven assets, most notably gold and US Treasuries. We compare the role of these two assets as potential hedge instruments for thirteen major indexes' returns and their volatilities. Our study extends the literature by using gold returns purged from the effects of being denominated in US dollars. We also utilize seventeen different volatility indexes to include US and international equities as well as currencies instead of the common S & P-500 index. While gold and Treasuries are comparable in their correlation with contemporaneous market returns, Treasuries seem to be safe haven asset of choice. Gold is more correlated than Treasuries in terms of lead-lag relationships with market returns as well as market volatility indexes.