Empirical Econometrics Research Articles

DISTRIBUTED LAG MODEL PENGARUH JUMLAH UANG BEREDAR TERHADAP NILAI TUKAR RUPIAH MENGGUNAKAN METODE KOYCK DAN ALMON

A regression model that contains the dependent variable which is influenced by the current independent variable, and is also influenced by the independent variable at the previous time is called a distributed lag model. Distributed lag model is a dynamic model in econometrics that is useful in empirical econometrics because it makes a static economic theory dynamic by taking into account the role of time explicitly. There are two distributed lag models, namely the infinite lag model and the finite lag model using the Koyck method and the Almon method in determining the estimated Distributed lag model. This study aims to determine the Distributed lag model for the effect of the money supply on the rupiah exchange rate and determine the best model based on the Koyck method and the Almon method. From the results of selecting the best model based on the SIC value and judging by the more precise R2 of the Koyck method, the resulting model ist = 7958 + 0.0002Xt + 0.000177Xt-1+ 0.000157Xt-2+ 0.000139Xt-3 + 0.0000123Xt-4

Modeling Investor Behavior Using Machine Learning: Mean‐Reversion and Momentum Trading Strategies

We model investor behavior by training machine learning techniques with financial data comprising more than 13,000 investors of a large bank in Brazil over 2016 to 2018. We take high‐frequency data on every sell or buy operation of these investors on a daily basis, allowing us to fully track these investment decisions over time. We then analyze whether these investment changes correlate with the IBOVESPA index. We find that investors decide their investment strategies using recent past price changes. There is some degree of heterogeneity in investment decisions. Overall, we find evidence of mean‐reverting investment strategies. We also find evidence that female investors and higher academic degree have a less pronounced mean‐reverting strategy behavior comparatively to male investors and those with lower academic degree. Finally, this paper provides a general methodological approach to mitigate potential biases arising from ad-hoc design decisions of discarding or introducing variables in empirical econometrics. For that, we use feature selection techniques from machine learning to identify relevant variables in an objective and concise way.

Football clubs’ profitability after the Financial Fair Play regulation: evidence from Italy

PurposeIn 2009, the Union of European Football Associations approved the Financial Fair Play (FFP) regulations. These regulations refer to the requirements of transparency and financial conditions for football clubs participating in European competitions. The purpose of this paper is to combine the managerial and the economic points-of-view in order to understand if Italian football clubs are ready to comply with the new FFP rules.Design/methodology/approachIn the analysis, the authors focus on the main Italian football league, Serie A. Using business model analysis, the authors determine what the main profit determinants of football associations are, through panel data for 15 clubs from 2011-2013. The authors then try to match the statistical and managerial approach.FindingsThe results show that the key contributing factors to profits are on the costs side. In particular, the most relevant expenses are player wages. The core revenue for clubs comes from the net profit from player trading, while other income, such as TV rights, commercial and match-day proceeds, have no statistical significance for profit formation.Research limitations/implicationsThe main limitation of the analysis is that the survey regarded only on Italian Championship. So it is deeply influenced by the competitive structure of the Italian league that is different from that of other European leagues. Therefore, the authors think that the future developments of this work could be the examination of another European Championship, for example, English Premier League, and the compared analysis of Italian league and other European one.Practical implicationsThere is a close-link between costs and profitability, and especially between expenditure on players and the ability to close the financial year with a profit. Clubs must address the issue of player wages in order to follow a path of economic recovery towards profitability.Originality/valueIn the work the authors put together managerial point of view and economical one, in order to understand how FFP can impact on profitability of football clubs. The authors use an empirical econometrics model to test the hypothesis about business model of Italian Clubs.

On Econometric Inference and Multiple Use of the Same Data

In economics and finance, test statistics and confidence regions are typically probabilistically dependent on previously-used data. However, the Bayesian and classical justifications for usual tests and confidence regions are known to be invalid in this case. Given the success of empirical econometrics, this leads to the question: Why has econometric inference been possible? The paper provides a simple, yet general, answer to the question. We outline a novel econometric theory that i) is immune to conditioning on previously-used data, and that ii) nests model calibration, and most of econometric practices, whether they are labelled Bayesian or classical.

Quantile Regression on Quantile Ranges

Motivated by the fact that a linear specification in a quantile regression setting is unable to describe the non-linear relations among economic variables, well documented in the empirical econometrics literature, we formulate a threshold quantile regression model for one, known and unknown threshold value. Our modeling framework is more general than that of Hansen (2000), Galvao et. al. (2010) in that we consider covariates that do not exhibit threshold effects themselves, but are allowed to be affected by the covariate subject to regime-changes. We are also dealing with different inference problems. We derive the asymptotic properties of the model parameters as well as the threshold value(s) and develop inferencial procedures to identify heterogeneous effects of different covariate quantile ranges on different quantiles of the response. We conduct inference by developing a sup-Wald test that converges to a two-parameter Gaussian process. In addition, we derive the limiting distribution of the threshold value(s) for the single and multiple threshold effects settings under two asymptotic frameworks: one assuming fixed and another assuming shrinking shifts. Furthermore, we construct confidence intervals for the unknown threshold value via a likelihood-ratio-type test statistic and conduct a simulation study to investigate the coverage probability of the confidence intervals for different quantiles. Our asymptotic results complement those found in the existing literature on threshold regression models.

The Multiple Quantile Regression on Quantile Ranges Model

Motivated by the fact that a linear speci fication in a quantile regression setting is unable to describe the non-linear relations among economic variables, as documented in the empirical econometrics literature, we are the first to formulate and analyze a multiple threshold quantile regression model. Generalizing Hansen (2000) framework, we propose an asymptotic framework to analyze the properties of the model parameters together with the unknown threshold values and develop inferential procedures (Wald tests) to identify heterogeneous e ffects of di fferent covariate quantile ranges on diff erent quantiles of the response. We derive the limiting distribution of the threshold values under two asymptotic frameworks: one assuming fixed and another assuming shrinking magnitude of shifts and we discuss the case where more than one quantile of the response is a effected by some regime change. Finally, we propose a double supremum Wald test for zero versus k regime-changes and a likelihood-ratio-type test for l versus l 1 regime-changes in the covariate and derive their limiting distributions. Simulations assess favorably the relevance of our testing procedures. Our asymptotic results complement those of Gonzalo and Pitarakis (2006) and Li and Ling (2011) to the quantile regression setting.

Profile of Charles F. Manski

Charles Manski has a history of questioning dogma. Manski, who was elected to the National Academy of Sciences in 2009 and is currently a professor of economics at Northwestern University (Evanston, IL), has developed new methods in theoretical and empirical econometrics over the course of his career. In his Inaugural Article (1), he builds on his recent work on planning under ambiguity, a common problem for many policymakers. Policymakers are in the business of making tough decisions with a paucity of data. No one can possibly know how all taxation schedules, criminal justice laws, or vaccination programs will influence labor supplies, crime rates, and spread of disease, respectively. Therefore, what is a policymaker to do under this common condition of partial knowledge? Traditional economic approaches to this problem use subjective probabilities that are often based on convenient nonrefutable assumptions. Throw those assumptions out, says Manski, “Economists usually want to find the optimal solution to something…That's not the way I operate…I'm satisfied to throw out things that are demonstrably bad.” Rather than create a solution based on so-called “assumptions of convenience,” Manski has championed an approach in which weaker but more credible assumptions can reveal informative conclusions (2). Throwing away traditional assumptions means that a single optimal solution may have to give way to a range of good solutions. However, Manski, unlike some other scientists and many politicians, is, he says, “not uncomfortable with ambiguity.” Charles Manski was born in 1948, the child of what he calls a “classic American immigrant” family, and grew up in Boston, MA. His father fled from Poland during World War II and put himself through night school to earn a Bachelor of Arts degree. His mother's education ended at high school. However, Manski and his two brothers managed to earn advanced degrees. “It's not like …

When are inferences too fragile to be believed?

The use of sensitivity analysis is routine in some fields of empirical econometrics, although econometric theorists have generally taken a critical attitude towards it. This paper presents a framework in which arguments for and against such analysis can be evaluated. It appears that sensitivity is not necessarily a bad, nor sturdiness necessarily a good.

Weak Instruments: Diagnosis and Cures in Empirical Econometrics

What is the weak-instruments (WI) problem and what causes it? Universal agreement does not exist on these questions. We define weak instruments by two features: (i) two-stage least squares (2SLS) analysis is badly biased toward the ordinary least-squares (OLS) estimate, and alternative “unbiased” estimators such as limited-information maximum likelihood (LIML) may not solve the problem; and (ii) the standard (first-order) asymptotic distribution does not give an accurate framework for inference. Thus, a researcher may estimate “bad results” and not be aware of the outcome. The cause of WI is often stated to be a low R or F statistic of the reduced-form equation, in the most commonly occurring situation of one right-handside endogenous variable. We find the situation is more complex with an additional factor, the correlation between the stochastic disturbances of the structural equation and the reduced form, that needs to be taken into account. We discuss in this paper a specification test (Hahn and Hausman, 2002a) for WI, a caution against using “no moments” estimators such as LIML in the WI situation, and suggestions for different estimators, an approach to inference of Frank Kleibergen (2002) for WI. We end with a caution of how “small biases” can become “large biases” in the WI situation. We begin with the limited-information structural model under the assumptions of Hausman (1983):

MELE: Maximum Entropy Leuven Estimators

Multicollinearity hampers empirical econometrics. The remedies proposed to date suffer from pitfalls of their own. The ridge estimator is not generally accepted as a vital alternative to the ordinary least-squares (OLS) estimator because it depends upon unknown parameters. The generalized maximum entropy (GME) estimator of Golan, Judge and Miller depends upon subjective exogenous information that affects the estimated parameters in an unpredictable way. This paper presents novel maximum entropy estimators inspired by the theory of light that do not depend upon any additional information. Monte Carlo experiments show that they are not affected by any level of multicollinearity and dominate OLS uniformly. The Leuven estimators are consistent and asymptotically normal.

The demand for commercial bank loans and the lending rate

This paper presents a two-equation structural model of short-term loans and the lending rate, estimated with quarterly data for the Netherlands. The emphasis is on the fact that the lending rate is observed as a zone rather than as a unique and directly measurable market price. An important empirical result is that the interest rate elasticity of loan demand is significant. This conclusive finding confirms the intuitive feeling with regard to the importance of the rate of interest for credit prevailing in the banking community, but is seldom found in empirical econometrics.

Confirmation and Replication in Empirical Econometrics: A Step Toward Improved Scholarship

Confirmation and Replication in Empirical Econometrics: A Step Toward Improved Scholarship

On the Uses of Misspecification Checks and Tests of Non-Nested Hypotheses in Empirical Econometrics

On the Uses of Misspecification Checks and Tests of Non-Nested Hypotheses in Empirical Econometrics L. G. Godfrey L. G. Godfrey University of York Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 94, Issue Supplement, 1 December 1984, Pages 69–81, https://doi.org/10.2307/2232656 Published: 01 December 1984