Solving the Omitted Variables Problem of Regression Analysis Using the Relative Vertical Position of Observations

Leightner, Jonathan E.; Inoue, Tomoo

doi:10.1155/2012/728980

Cited by 21 publications

(18 citation statements)

References 26 publications

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“…2 This last result implies that, since VSGLS is BLUE, BD-RTPLS must be better than BLUE when there is no random error which is reasonable if BD-RTPLS is better at capturing non-linear aspects of the data. Published applications of BD-RTPLS include Leightner, 2015, 2013, 2011a, 2011b, 2010a, 2010b, 2008, 2007, 2005a, 2005b, 2002and Leightner and Inoue, 2012a, 2012b, 2008a, 2008b. The next section of the paper uses BD-RTPLS to estimate the percentage change in inflation due to a one percent decrease in unemployment (the Phillips Curve) for 34 OECD countries between 2002 and 2017.…”

Section: Data and Estimation Issuesmentioning

confidence: 99%

Estimates of the Inflation versus Unemployment Tradeoff that are not Model Dependent

Leightner¹

2020

Journal of Central Banking Theory and Practice

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For governments who want to improve their economies via fiscal, monetary, trade or exchange rate policies, the tradeoff between the inflation rate and the unemployment rate is extremely important. This tradeoff has become known as the Phillips curve. Among economists there is no consensus on how to model and estimate the Phillips curve. Ideally, all the factors that could affect the Aggregate Supply and Aggregate Demand curves should be included in the model including exchange rates, transportation costs, infrastructure, weather, income distribution, etc. No researcher has created a model that could not be criticized for omitting some important variables. This paper use Bi-Directional Reiterative Truncated Least Squares, a statistical technique that solves the omitted variables problem, to estimate the tradeoff between inflation and unemployment for 34 countries between 2002 and 2017. I find that this tradeoff varies noticeably from country to country in a given year, but that many of these tradeoffs move in the same direction over time. This common direction of movement implies that the international context for the vast majority of the countries studied is affecting the inflation versus unemployment tradeoff.

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Section: Data and Estimation Issuesmentioning

confidence: 99%

Estimates of the Inflation versus Unemployment Tradeoff that are not Model Dependent

Leightner¹

2020

Journal of Central Banking Theory and Practice

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“…Building on this intuition, Leightner [5] developed a new analytical technique named "Reiterative Truncated Projected Least Squares" (RTPLS) that solves the omitted variable problem of regression analysis without using instrumental variables and their unreasonable assumptions. Leightner [23] and Leightner and Inoue [24] created the second and third generation of the technique respectively: RTPLS2 and RTPLS3. Leightner and Inoue [24] also produce an argument that RTPLS3 is unbiased.…”

Section: An Intuitive Explanation Of the Analytical Technique Usedmentioning

confidence: 99%

“…Leightner [23] and Leightner and Inoue [24] created the second and third generation of the technique respectively: RTPLS2 and RTPLS3. Leightner and Inoue [24] also produce an argument that RTPLS3 is unbiased. Inoue, Lafaye de Micheaux, and Leightner [7] introduce the fourth generation, RTPLS4.…”

Section: An Intuitive Explanation Of the Analytical Technique Usedmentioning

confidence: 99%

“…When the importance of omitted variables is 100 times the size of measurement and round off error, then OLS produces more than 28 times the error of RTPLS4. Published studies that used RTPLS, RTPLS2, or RTPLS3 in applications include Leightner and Inoue [6,[24][25][26][27][28] and Leightner [5,23,[29][30][31][32][33][34][35][36].…”

Section: An Intuitive Explanation Of the Analytical Technique Usedmentioning

confidence: 99%

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The Changing Effectiveness of Monetary Policy

Leightner

2013

Economies

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In the wake of the 2008 financial crisis, many countries are hoping that massive increases in their money supplies will revive their economies. Evaluating the effectiveness of this strategy using traditional statistical methods would require the construction of an extremely complex economic model of the world that showed how each country’s situation affected all other countries. No matter how complex that model was, it would always be subject to the criticism that it had omitted important variables. Omitting important variables from traditional statistical methods ruins all estimates and statistics. This paper uses a relatively new statistical method that solves the omitted variables problem. This technique produces a separate slope estimate for each observation which makes it possible to see how the estimated relationship has changed over time due to omitted variables. I find that the effectiveness of monetary policy has fallen between the first quarter of 2003 and the fourth quarter of 2012 by 14%, 36%, 38%, 32%, 29% and 69% for Japan, the UK, the USA, the Euro area, Brazil, and the Russian Federation respectively. I hypothesize that monetary policy is suffering from diminishing returns because it cannot address the fundamental problem with the world’s economy today; that problem is a global glut of savings that is either sitting idle or funding speculative bubbles

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“…Omitted variable bias (OVB) is one of the most common and vexing problems in ordinary least squares regression. It occurs when a variable that is correlated with both the dependent and one or more included independent variables is omitted from a regression equation [6] Even under the conditions that the estimates are not biased, the power of the relevant statistical tests is somewhat adversely affected by the omission of the relevant variables [7]. According to Clarke [8] when a model is mis specified due to omitted variable, there is always the fear of omitted variable bias, a key underlying assumption is that the danger posed by omitted variable can be ameliorated by the inclusion of control variables.…”

Section: Introductionmentioning

confidence: 99%

Choice Model between Omission of Relevant Variable and Inclusion of Irrelevant Variable in a Multicollinear Regression Model

2020

International Journal of Scientific and Innovative Mathematical

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When it comes to the actual construction of a regression model, there is little that an analyst spends more of his time wrestling with than the correct selection of variables to be included in the model. Misspecification can arise either because of omission of a variable specified by the truth, the case of the left out variable, or because of inclusion of a variable not specified by the truth, the case of the irrelevant variable. Most studies on misspecification bias has emphasized on multicollinearity as a reason for omission or inclusion of variables. This study considers both the implications of including too few ("omitted" variables) and too many ("irrelevant" variables) explanatory variables in a collinear regression model. Our result reveals that inclusion of irrelevant variables is not as severe as the consequences of omitting relevant variables in both collinear and zero correlation models.

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Solving the Omitted Variables Problem of Regression Analysis Using the Relative Vertical Position of Observations

Cited by 21 publications

References 26 publications

Estimates of the Inflation versus Unemployment Tradeoff that are not Model Dependent

Estimates of the Inflation versus Unemployment Tradeoff that are not Model Dependent

The Changing Effectiveness of Monetary Policy

Choice Model between Omission of Relevant Variable and Inclusion of Irrelevant Variable in a Multicollinear Regression Model

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