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Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employ--a methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results.

Econometric models. --- cross-sectional data --- inference --- misspecification testing --- panel data --- regression --- regression models --- respecification --- Stata --- statistical adequacy

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There is now a substantial theoretical literature arguing that inflation impedes financial deepening. Furthermore, it has been hypothesized that the relationship is a nonlinear one, in that there is a threshold level of inflation below which inflation has a positive effect on financial depth, but above which the effect turns negative. Using a large cross-country sample, empirical support is found for the existence of such a threshold. The estimates indicate that the threshold level of inflation is generally between 3 and 6 percent a year, depending on the specific measure of financial depth that is used.

Econometrics --- Finance: General --- Inflation --- Price Level --- Deflation --- Financial Markets and the Macroeconomy --- General Financial Markets: General (includes Measurement and Data) --- Truncated and Censored Models --- Switching Regression Models --- Threshold Regression Models --- Macroeconomics --- Finance --- Econometrics & economic statistics --- Stock markets --- Financial sector development --- Market capitalization --- Threshold analysis --- Prices --- Financial services industry --- Stock exchanges

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Using a novel empirical approach and an extensive dataset developed by the Fiscal Affairs Department of the IMF, we find no evidence of any particular debt threshold above which medium-term growth prospects are dramatically compromised. Furthermore, we find the debt trajectory can be as important as the debt level in understanding future growth prospects, since countries with high but declining debt appear to grow equally as fast as countries with lower debt. Notwithstanding this, we find some evidence that higher debt is associated with a higher degree of output volatility.

Debts, Public --- Default (Finance) --- Economic development --- Finance --- Finance, Public --- Repudiation --- Debts, Government --- Government debts --- National debts --- Public debt --- Public debts --- Sovereign debt --- Debt --- Bonds --- Deficit financing --- Econometric models. --- Econometrics --- Public Finance --- Debt Management --- Sovereign Debt --- Economic Growth and Aggregate Productivity: General --- Truncated and Censored Models --- Switching Regression Models --- Threshold Regression Models --- Public finance & taxation --- Econometrics & economic statistics --- Threshold analysis --- Econometric analysis --- Japan

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Random forest is in many fields of research a common method for data driven predictions. Within economics and prediction of poverty, random forest is rarely used. Comparing out-of-sample predictions in surveys for same year in six countries shows that random forest is often more accurate than current common practice (multiple imputations with variables selected by stepwise and Lasso), suggesting that this method could contribute to better poverty predictions. However, none of the methods consistently provides accurate predictions of poverty over time, highlighting that technical model fitting by any method within a single year is not always, by itself, sufficient for accurate predictions of poverty over time.

Linear Regression Models. --- Machine Learning. --- Macroeconomics and Economic Growth. --- Poverty Monitoring and Analysis. --- Poverty Reduction. --- Poverty. --- Prediction Methods. --- Random Forest. --- Rural Poverty Reduction. --- Science and Technology Development. --- Statistical and Mathematical Sciences. --- Tracking Poverty.

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This paper examines whether a tipping point exists for real GDP growth in Italy above which the ratio of non-performing loans (NPLs) to total loans falls significantly. Estimating a heterogeneous dynamic panel-threshold model with data on 17 Italian regions over the period 1997–2014, we provide evidence for the presence of growth-threshold effects on the NPL ratio in Italy. More specifically, we find that real GDP growth above 1.2 percent, if sustained for a number of years, is associated with a significant decline in the NPLs ratio. Achieving such growth rates requires decisively tackling long-standing structural rigidities and improving the quality of fiscal policy. Given the modest potential growth outlook, however, under which banks are likely to struggle to grow out of their NPL overhang, further policy measures are needed to put the NPL ratio on a firm downward path over the medium term.

Financial crises. --- Financial crises --- Crashes, Financial --- Crises, Financial --- Financial crashes --- Financial panics --- Panics (Finance) --- Stock exchange crashes --- Stock market panics --- Crises --- Econometrics --- Finance: General --- Industries: Financial Services --- 'Panel Data Models --- Spatio-temporal Models' --- Financial Markets and the Macroeconomy --- Bankruptcy --- Liquidation --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Truncated and Censored Models --- Switching Regression Models --- Threshold Regression Models --- General Financial Markets: Government Policy and Regulation --- Finance --- Econometrics & economic statistics --- Nonperforming loans --- Threshold analysis --- Distressed assets --- Financial institutions --- Econometric analysis --- Financial sector policy and analysis --- Loans --- Banks and banking --- Italy --- Panel Data Models --- Spatio-temporal Models